├── .gitee └── PULL_REQUEST_TEMPLATE.md ├── .gitignore ├── CONTRIBUTING.md ├── LICENSE ├── NOTICE ├── OWNERS ├── README.md ├── README_CN.md ├── RELEASE.md ├── build.sh ├── docs ├── MindSpore-logo.png ├── api_python │ └── hub.rst └── api_python_en │ └── hub.rst ├── mindspore_hub ├── __init__.py ├── _utils │ ├── __init__.py │ ├── auto_rename.py │ ├── check.py │ ├── download.py │ ├── full_download.sh │ ├── sparse_download.sh │ ├── valid_dataset.py │ └── whitelist.py ├── deploy │ ├── __init__.py │ ├── benchmark.py │ └── service.py ├── info.py ├── list.py ├── load.py ├── manage.py ├── optimize │ ├── __init__.py │ └── transfer.py └── version.py ├── mshub_res ├── README.md ├── README_cn.md ├── assets │ ├── mindspore │ │ ├── 1.10 │ │ │ ├── 3dcnn_brast2017.md │ │ │ ├── 3ddensenet_iseg2017.md │ │ │ ├── adnet_vot2013vot2014.md │ │ │ ├── advancedeast_icpr2018.md │ │ │ ├── aecrnet_reside.md │ │ │ ├── albert_mnli.md │ │ │ ├── albert_squadv1.1.md │ │ │ ├── albert_sst2.md │ │ │ ├── alexnet_cifar10.md │ │ │ ├── alexnet_imagenet2012.md │ │ │ ├── alphapose_coco2017.md │ │ │ ├── apdrawinggan_generator_apdrawingdb.md │ │ │ ├── arbitrarystyletransfer_mscoco.md │ │ │ ├── arcface_ms1mv2.md │ │ │ ├── attentioncluster_fc1natt1_mnist.md │ │ │ ├── attentioncluster_fc1natt2_mnist.md │ │ │ ├── attentioncluster_fc1natt4_mnist.md │ │ │ ├── attentioncluster_fc1natt8_mnist.md │ │ │ ├── attentioncluster_fc2natt1_mnist.md │ │ │ ├── attentioncluster_fc2natt4_mnist.md │ │ │ ├── attentioncluster_fc2natt64_mnist.md │ │ │ ├── attentioncluster_fc2natt8_mnist.md │ │ │ ├── attgan_Generator_celeba.md │ │ │ ├── augvit_cifar10.md │ │ │ ├── autoaugment_cifar10.md │ │ │ ├── autodeeplab_0.5M_cityscapes.md │ │ │ ├── autodeeplab_1.5M_cityscapes.md │ │ │ ├── autodeeplab_1M_cityscapes.md │ │ │ ├── autodeeplab_cityscapes.md │ │ │ ├── autodis_criteo.md │ │ │ ├── autoslim_imagenet2012.md │ │ │ ├── avacifar_cifar10.md │ │ │ ├── avahpa_hpa.md │ │ │ ├── bertbase_cnnews128.md │ │ │ ├── bertfinetune_15classifier_tnews.md │ │ │ ├── bertfinetune_bilstmcrf_chinesener.md │ │ │ ├── bertfinetune_crf_cluener.md │ │ │ ├── bertfinetune_ner_cluener.md │ │ │ ├── bertfinetune_squad_squad.md │ │ │ ├── bertlarge_boost_enwiki512.md │ │ │ ├── bertlarge_cnnews128.md │ │ │ ├── bertthor_mlperf.md │ │ │ ├── bgcf_amazonbeauty.md │ │ │ ├── brdnet_waterloo.md │ │ │ ├── c3d_hmdb51.md │ │ │ ├── c3d_ucf101.md │ │ │ ├── cascadercnn_coco2017.md │ │ │ ├── cct_imagenet2012.md │ │ │ ├── centerface_widerface.md │ │ │ ├── centernet_coco2017.md │ │ │ ├── centernetdet_coco2017.md │ │ │ ├── centernetresnet101_coco2017.md │ │ │ ├── centernetresnet50v1_coco2017.md │ │ │ ├── cgan_G_mnist.md │ │ │ ├── cnnctc_mjstiiit.md │ │ │ ├── cnndirectionmodel_fsns.md │ │ │ ├── convnext_imagenet2012.md │ │ │ ├── crnn_crnnds.md │ │ │ ├── crnnseq2seqocr_fsns.md │ │ │ ├── csd_div2k.md │ │ │ ├── cspdarknet53_imagenet2012.md │ │ │ ├── ctcmodel_timit.md │ │ │ ├── ctpn_finetune_icdar2013.md │ │ │ ├── ctpn_pretrain_icdar2013.md │ │ │ ├── cyclegan_GA_apple2orange.md │ │ │ ├── cyclegan_GA_horse2zebra.md │ │ │ ├── cyclegan_GB_apple2orange.md │ │ │ ├── cyclegan_GB_horse2zebra.md │ │ │ ├── dam_douban.md │ │ │ ├── dam_ubuntu.md │ │ │ ├── dbpn_div2k.md │ │ │ ├── dbpngan_G_div2k.md │ │ │ ├── dbpngan_Generator_div2k.md │ │ │ ├── dcgan_imagenet2012.md │ │ │ ├── ddag_allresearch_sysumm01.md │ │ │ ├── ddag_indoorsearch_sysumm01.md │ │ │ ├── ddag_infraredtovisible_regdb.md │ │ │ ├── ddag_visibletoinfrared_regdb.md │ │ │ ├── ddrnet_imagenet2012.md │ │ │ ├── deepfm_criteo.md │ │ │ ├── deepid_youtubeface.md │ │ │ ├── deeplabv3plus_s16_voc2012.md │ │ │ ├── deeplabv3plus_s8r1_voc2012.md │ │ │ ├── deeplabv3plus_s8r2_voc2012.md │ │ │ ├── deeplabv3s16_voc2012.md │ │ │ ├── deeplabv3s8r2_voc2012.md │ │ │ ├── deepsort_market1501.md │ │ │ ├── deeptext_icdar2013.md │ │ │ ├── delf_gldv2.md │ │ │ ├── delf_googlelandmarksdatasetv2.md │ │ │ ├── dem_att_cub.md │ │ │ ├── dem_cub.md │ │ │ ├── densenet121_imagenet2012.md │ │ │ ├── depthnet_coarsenet_nyu.md │ │ │ ├── depthnet_finenet_nyu.md │ │ │ ├── dgcn_citeseer.md │ │ │ ├── dgcn_cora.md │ │ │ ├── dgcn_pubmed.md │ │ │ ├── dgu_udc.md │ │ │ ├── dlinknet_DeepGlobeRoadExtractionDataset.md │ │ │ ├── dlrm_criteo.md │ │ │ ├── dncnn_bsd500.md │ │ │ ├── dpn_imagenet2012.md │ │ │ ├── drnet_classifier_imagenet2012.md │ │ │ ├── drnet_predictor_imagenet2012.md │ │ │ ├── dscnn_speechcommandsdatasetversion1.md │ │ │ ├── duconv_duconv.md │ │ │ ├── east_icdar2015.md │ │ │ ├── ecapatdnn_voxceleb.md │ │ │ ├── ecolite_ucf101.md │ │ │ ├── edcn_criteo.md │ │ │ ├── efficientdetd0_coco2017.md │ │ │ ├── efficientnetb0_imagenet2012.md │ │ │ ├── efficientnetb1_imagenet2012.md │ │ │ ├── efficientnetb2_imagenet2012.md │ │ │ ├── efficientnetb3_imagenet2012.md │ │ │ ├── efficientnetv2_imagenet2012.md │ │ │ ├── egnet_resnet_dutstr.md │ │ │ ├── egnet_vgg_dutstr.md │ │ │ ├── emotect_baidu.md │ │ │ ├── enet_cityscapes.md │ │ │ ├── erfnet_cityscapes.md │ │ │ ├── ernie_bdqa.md │ │ │ ├── ernie_brcd.md │ │ │ ├── ernie_chnsenticorp.md │ │ │ ├── ernie_cmcr.md │ │ │ ├── ernie_msraner.md │ │ │ ├── ernie_xnli.md │ │ │ ├── esrgan_generator_div2k.md │ │ │ ├── faceattribute_fairface_rwmfd.md │ │ │ ├── facedetection_widerface.md │ │ │ ├── facequalityassessment_300wlp.md │ │ │ ├── facerecognition_ms1mv2.md │ │ │ ├── facerecognitionfortracking_lfw.md │ │ │ ├── fasterrcnn_resnetv1101_coco2017.md │ │ │ ├── fasterrcnn_resnetv1152_coco2017.md │ │ │ ├── fasterrcnn_resnetv150_coco2017.md │ │ │ ├── fasterrcnndcn_coco2017.md │ │ │ ├── fastscnn_cityscapes.md │ │ │ ├── fastspeech_ljspeech11.md │ │ │ ├── fasttext_agnews.md │ │ │ ├── fasttext_dbpedia.md │ │ │ ├── fasttext_yelp.md │ │ │ ├── fatdeepffm_criteo.md │ │ │ ├── fcanet_augmentedpascal.md │ │ │ ├── fcn4_musictagging.md │ │ │ ├── fcn8s_voc2012.md │ │ │ ├── fdabnn_cifar10.md │ │ │ ├── fishnet99_imagenet2012.md │ │ │ ├── gan_G_mnist.md │ │ │ ├── gat_citeseer.md │ │ │ ├── gat_cora.md │ │ │ ├── gcn_citesser.md │ │ │ ├── gcn_cora.md │ │ │ ├── genetres50_minux_imagenet2012.md │ │ │ ├── genetres50_plus_imagenet2012.md │ │ │ ├── genetres50_theta_imagenet2012.md │ │ │ ├── ghostnet_imagenet2012.md │ │ │ ├── ghostnetquant_imagenet2012.md │ │ │ ├── gnmtv2_wmtende.md │ │ │ ├── googlenet_cifar10.md │ │ │ ├── googlenet_imagenet2012.md │ │ │ ├── gpt3_openweb.md │ │ │ ├── gru_muti30k.md │ │ │ ├── hardnet_imagenet2012.md │ │ │ ├── hed_bsds500.md │ │ │ ├── hournas_cifar10.md │ │ │ ├── hrnet_cityscapes.md │ │ │ ├── hrnetw48cls_imagenet2012.md │ │ │ ├── hrnetw48seg_cityscapes.md │ │ │ ├── hypertext_iflytek.md │ │ │ ├── hypertext_tnews.md │ │ │ ├── ibnnet_imagenet2012.md │ │ │ ├── icnet_cityscapes.md │ │ │ ├── inceptionresnetv2_imagenet2012.md │ │ │ ├── inceptionv3_imagenet2012.md │ │ │ ├── inceptionv4_imagenet2012.md │ │ │ ├── ipt_denoise30_cbsd68.md │ │ │ ├── ipt_denoise50_cbsd68.md │ │ │ ├── ipt_derain_rain100l.md │ │ │ ├── ipt_x2_set5.md │ │ │ ├── ipt_x3_set5.md │ │ │ ├── ipt_x4_set5.md │ │ │ ├── irn_div2k.md │ │ │ ├── isynet_N0_imagenet2012.md │ │ │ ├── isynet_N1S1_imagenet2012.md │ │ │ ├── isynet_N1S2_imagenet2012.md │ │ │ ├── isynet_N1S3_imagenet2012.md │ │ │ ├── isynet_N1_imagenet2012.md │ │ │ ├── isynet_N2_imagenet2012.md │ │ │ ├── isynet_N3_imagenet2012.md │ │ │ ├── ktnet_record.md │ │ │ ├── ktnet_squad.md │ │ │ ├── learningtoseeinthedark_sony.md │ │ │ ├── lenet_mnist.md │ │ │ ├── lightcnn_msceleb1m.md │ │ │ ├── lpcnet_librispeech.md │ │ │ ├── lstm_aclimdbv1.md │ │ │ ├── lstmcrf_conll2000.md │ │ │ ├── luke_squad1.1.md │ │ │ ├── manidp_cifar10.md │ │ │ ├── maskrcnn_coco2017.md │ │ │ ├── maskrcnnmobilenetv1_coco2017.md │ │ │ ├── mass_newscrawl_gigaword_cornell.md │ │ │ ├── mcnn_shanghaitecha.md │ │ │ ├── melgan_Generator_ljspeech.md │ │ │ ├── metabaseline_miniimagenet.md │ │ │ ├── midas_redweb.md │ │ │ ├── mmoe_censusincome.md │ │ │ ├── mnasnet_imagenet2012.md │ │ │ ├── mobilenetv1_cifar10.md │ │ │ ├── mobilenetv1_imagenet2012.md │ │ │ ├── mobilenetv2_imagenet2012.md │ │ │ ├── mobilenetv3_imagenet2012.md │ │ │ ├── mobilenetv3large_imagenet2012.md │ │ │ ├── mobilenetv3smallx10_imagenet2012.md │ │ │ ├── moleculardynamics_water.md │ │ │ ├── multitasknet_veri.md │ │ │ ├── mvd_allresearch_sysumm01.md │ │ │ ├── mvd_indoorsearch_sysumm01.md │ │ │ ├── mvd_infraredtovisible_regdb.md │ │ │ ├── mvd_visibletoinfrared_regdb.md │ │ │ ├── naml_mindlarge.md │ │ │ ├── nasfpn_coco2017.md │ │ │ ├── nasnet_imagenet2012.md │ │ │ ├── ncf_movielens.md │ │ │ ├── neighbor2neighbor_kodak.md │ │ │ ├── nfnet_imagenet2012.md │ │ │ ├── nima_avadataset.md │ │ │ ├── ntsnet_cub2002011.md │ │ │ ├── ocrnet_cityscapes.md │ │ │ ├── octsqueeze_kitti.md │ │ │ ├── openpose_coco2017.md │ │ │ ├── opt_cococaption.md │ │ │ ├── osnet_dukemtmcreid.md │ │ │ ├── osnet_market1501.md │ │ │ ├── osnet_msmt17.md │ │ │ ├── patchcore_mvtecad.md │ │ │ ├── pcbrpp_market1501.md │ │ │ ├── pdarts_cifar10.md │ │ │ ├── pgan_G_celeba.md │ │ │ ├── pix2pix_Generator_facades.md │ │ │ ├── pix2pix_Generator_maps.md │ │ │ ├── pix2pix_facades.md │ │ │ ├── pix2pix_maps.md │ │ │ ├── pnasnet_imagenet2012.md │ │ │ ├── pointnet2_modelnet40.md │ │ │ ├── pointnet_shapenet.md │ │ │ ├── poseestnet_veir.md │ │ │ ├── posenet_kingscollege.md │ │ │ ├── posenet_stmaryschurch.md │ │ │ ├── predrnnplusplus_movingmnist.md │ │ │ ├── protonet_omniglot.md │ │ │ ├── proxylessnas_imagenet2012.md │ │ │ ├── psenet_icdar2015.md │ │ │ ├── pspnet_ade20k.md │ │ │ ├── pspnet_voc2012.md │ │ │ ├── pvnet_ape_linemod.md │ │ │ ├── pvnet_cam_linemod.md │ │ │ ├── pvnet_can_linemod.md │ │ │ ├── pvnet_cat_linemod.md │ │ │ ├── pwcnet_flyingchairs.md │ │ │ ├── r2plus1d_ucf101.md │ │ │ ├── ras_dutstrain.md │ │ │ ├── rcan_div2k.md │ │ │ ├── rdn_div2k.md │ │ │ ├── rednet30_bsd300.md │ │ │ ├── refinedet_coco2017.md │ │ │ ├── refinenet_voc2012.md │ │ │ ├── relationnet_omniglot.md │ │ │ ├── renas_cifar10.md │ │ │ ├── repvgg_imagenet2012.md │ │ │ ├── res2net101_imagenet2012.md │ │ │ ├── res2net152_imagenet2012.md │ │ │ ├── res2net50_cifar10.md │ │ │ ├── res2net50_imagenet2012.md │ │ │ ├── res2netdeeplabv3_s16r1_voc2012.md │ │ │ ├── res2netdeeplabv3_s8r2_voc2012.md │ │ │ ├── res2netdeeplabv3_s8r2multiscale_voc2012.md │ │ │ ├── res2netdeeplabv3_s8r2multiscalefilp_voc2012.md │ │ │ ├── res2netfasterrcnn_coco2017.md │ │ │ ├── res2netyolov3_coco2017.md │ │ │ ├── resnest50_imagenet2012.md │ │ │ ├── resnet101_imagenet2012.md │ │ │ ├── resnet152_imagenet2012.md │ │ │ ├── resnet18_cifar10.md │ │ │ ├── resnet18_imagenet2012.md │ │ │ ├── resnet34_imagenet2012.md │ │ │ ├── resnet50_cifar10.md │ │ │ ├── resnet50_imagenet2012.md │ │ │ ├── resnet50_quadruplet_sop.md │ │ │ ├── resnet50_triplet_sop.md │ │ │ ├── resnet50advpruning_imagenet2012.md │ │ │ ├── resnet50bam_imagenet2012.md │ │ │ ├── resnet50quadruplet_sop.md │ │ │ ├── resnet50triplet_sop.md │ │ │ ├── resnetv2101_cifar10.md │ │ │ ├── resnetv2152_cifar10.md │ │ │ ├── resnetv250_cifar10.md │ │ │ ├── resnetv250frn_imagenet2012.md │ │ │ ├── resnext101_imagenet2012.md │ │ │ ├── resnext15264x4d_imagenet2012.md │ │ │ ├── resnext50_imagenet2012.md │ │ │ ├── retinafaceresnet50_widerface.md │ │ │ ├── retinanet_coco2017.md │ │ │ ├── retinanetresnet101_coco2017.md │ │ │ ├── rotate_wn18rr.md │ │ │ ├── sdne_wiki.md │ │ │ ├── semantichumanmatting_mattinghumandatasets.md │ │ │ ├── senet_imagenet2012.md │ │ │ ├── senetresnet50_imagenet2012.md │ │ │ ├── senta_sst2.md │ │ │ ├── seq2seq_wmt14.md │ │ │ ├── seres2net50_imagenet2012.md │ │ │ ├── seresnet50_imagenet2012.md │ │ │ ├── seresnext50_imagenet2012.md │ │ │ ├── shufflenetv1_imagenet2012.md │ │ │ ├── shufflenetv2_imagenet2012.md │ │ │ ├── siamfc_ilsvrc2015vid.md │ │ │ ├── siamrpn_vid.md │ │ │ ├── simclr_encoder_cifar10.md │ │ │ ├── simclr_linearclassifier_cifar10.md │ │ │ ├── simplebaselines_coco2017.md │ │ │ ├── simplepose_coco2017.md │ │ │ ├── singan_G_asinglenatureimage.md │ │ │ ├── singlepathnas_imagenet2012.md │ │ │ ├── skipgram_text8.md │ │ │ ├── sknet_cifar10.md │ │ │ ├── slowfast_ava.md │ │ │ ├── snnmlp_t_imagenet2012.md │ │ │ ├── sphereface_casiawebface.md │ │ │ ├── sppnet_mult_imagenet2012.md │ │ │ ├── sppnet_single_imagenet2012.md │ │ │ ├── sppnet_zfnet_imagenet2012.md │ │ │ ├── squeezenet11_imagenet2012.md │ │ │ ├── squeezenet_cifar10.md │ │ │ ├── squeezenet_imagenet2012.md │ │ │ ├── squeezenetresidual_cifar10.md │ │ │ ├── squeezenetresidual_imagenet2012.md │ │ │ ├── srcnn_ilsvrc2013.md │ │ │ ├── srgan_div2k.md │ │ │ ├── ssd300_coco2017.md │ │ │ ├── ssdghostnet_coco2017.md │ │ │ ├── ssdinceptionv2_coco2014mini.md │ │ │ ├── ssdmobilenetv1fpn_coco2017.md │ │ │ ├── ssdmobilenetv2_coco2017.md │ │ │ ├── ssdmobilenetv2fpnlite_coco2017.md │ │ │ ├── ssdresnet34_coco2017.md │ │ │ ├── ssdresnet50_coco2017.md │ │ │ ├── ssdresnet50fpn_coco2017.md │ │ │ ├── ssdvgg16_coco2017.md │ │ │ ├── ssimae_mvtecadbottle.md │ │ │ ├── ssimae_mvtecadcable.md │ │ │ ├── ssimae_mvtecadcapsule.md │ │ │ ├── ssimae_mvtecadcarpet.md │ │ │ ├── ssimae_mvtecadgrid.md │ │ │ ├── ssimae_mvtecadmetalnut.md │ │ │ ├── stackedhourglass_mpii.md │ │ │ ├── stargan_Generator_celeba.md │ │ │ ├── stgan_celeba.md │ │ │ ├── stgcn_cheb15_pemsd7m.md │ │ │ ├── stgcn_cheb30_pemsd7m.md │ │ │ ├── stgcn_cheb45_pemsd7m.md │ │ │ ├── stgcn_gcn15_pemsd7m.md │ │ │ ├── stgcn_gcn30_pemsd7m.md │ │ │ ├── stgcn_gcn45_pemsd7m.md │ │ │ ├── stnet_kinetics400.md │ │ │ ├── stpm_mvtecadbottle.md │ │ │ ├── stpm_mvtecadcable.md │ │ │ ├── stpm_mvtecadcapsule.md │ │ │ ├── stpm_mvtecadcarpet.md │ │ │ ├── stpm_mvtecadgrid.md │ │ │ ├── stpm_mvtecadhazelnut.md │ │ │ ├── stpm_mvtecadleather.md │ │ │ ├── stpm_mvtecadmetalnut.md │ │ │ ├── stpm_mvtecadpill.md │ │ │ ├── stpm_mvtecadscrew.md │ │ │ ├── stpm_mvtecadtile.md │ │ │ ├── stpm_mvtecadtoothbrush.md │ │ │ ├── stpm_mvtecadtransistor.md │ │ │ ├── stpm_mvtecadwood.md │ │ │ ├── stpm_mvtecadzipper.md │ │ │ ├── swintransformer_imagenet2012.md │ │ │ ├── tacotron2_ljspeech11.md │ │ │ ├── tall_tacos.md │ │ │ ├── tbnet_steam.md │ │ │ ├── tcn_addingproblem.md │ │ │ ├── tcn_permutedmnist.md │ │ │ ├── textcnn_moviereview.md │ │ │ ├── textcnn_sst2.md │ │ │ ├── textcnn_subj.md │ │ │ ├── textcrnn_gru_polarity_moviereview.md │ │ │ ├── textcrnn_lstm_polarity_moviereview.md │ │ │ ├── textfusenet_totaltext.md │ │ │ ├── tgcn_losloop.md │ │ │ ├── tgcn_sztaxi.md │ │ │ ├── tinybert_enwiki128_mnli.md │ │ │ ├── tinybert_enwiki128_qnli.md │ │ │ ├── tinybert_enwiki128_sst2.md │ │ │ ├── tinydarknet_imagenet2012.md │ │ │ ├── tnt_imagenet2012.md │ │ │ ├── tokenfusion_nyudv2.md │ │ │ ├── tprr_readeralbert_hotpotqa.md │ │ │ ├── tprr_readerdownstream_hotpotqa.md │ │ │ ├── tprr_rerankalbert_hotpotqa.md │ │ │ ├── tprr_rerankdownstream_hotpotqa.md │ │ │ ├── transformer_large_wmt.md │ │ │ ├── transformer_wmt.md │ │ │ ├── tsm_somethingsomethingv2.md │ │ │ ├── tsn_flow_ucf101.md │ │ │ ├── tsn_rgb_ucf101.md │ │ │ ├── u2net_dutstr.md │ │ │ ├── ugatit_selfie2anime.md │ │ │ ├── unet2d_isbichallenge.md │ │ │ ├── unet3d_luna16.md │ │ │ ├── unet3plus_lits2017.md │ │ │ ├── unet_medical_isbi.md │ │ │ ├── unetnested_cellnuclei.md │ │ │ ├── vehiclenet_vehiclenetdataset.md │ │ │ ├── vgg16_bn_imagenet2012.md │ │ │ ├── vgg16_cifar10.md │ │ │ ├── vgg16_imagenet2012.md │ │ │ ├── vgg19_cifar10.md │ │ │ ├── vgg19_imagenet2012.md │ │ │ ├── vit_imagenet2012.md │ │ │ ├── vitbase_cifar10.md │ │ │ ├── vnet_promise2012.md │ │ │ ├── warpctc_captcha.md │ │ │ ├── wavemlp_imagenet2012.md │ │ │ ├── wdsr_div2k.md │ │ │ ├── wgan_batchnorm_G_lsunbedrooms.md │ │ │ ├── wgan_nobatchnorm_G_lsunbedrooms.md │ │ │ ├── wideanddeep_criteo.md │ │ │ ├── wideanddeep_dynamicshape_criteo.md │ │ │ ├── wideanddeep_hostdevice_criteo.md │ │ │ ├── wideanddeep_ps_criteo.md │ │ │ ├── wideanddeepmultitable_outbrain.md │ │ │ ├── wideresnet_cifar10.md │ │ │ ├── xception_imagenet2012.md │ │ │ ├── yolov3darknet53_coco2014.md │ │ │ ├── yolov3darknet53shape416_coco2014.md │ │ │ ├── yolov3resnet18_coco2017.md │ │ │ ├── yolov3tiny_coco2017.md │ │ │ ├── yolov4_coco2017.md │ │ │ ├── yolov4shape416_coco2017.md │ │ │ ├── yolov5s_coco2017.md │ │ │ ├── yolox_coco2017.md │ │ │ └── yolox_darknet53_coco2017.md │ │ ├── 1.3 │ │ │ ├── 3dcnn_brast2017.md │ │ │ ├── 3ddensenet_iseg2017.md │ │ │ ├── adnet_vot2013vot2014.md │ │ │ ├── advancedeast_icpr2018.md │ │ │ ├── albert_mnli.md │ │ │ ├── albert_squadv1.1.md │ │ │ ├── albert_sst2.md │ │ │ ├── alexnet_cifar10.md │ │ │ ├── alexnet_imagenet2012.md │ │ │ ├── alphapose_coco2017.md │ │ │ ├── apdrawinggan_generator_apdrawingdb.md │ │ │ ├── arcface_ms1mv2.md │ │ │ ├── attentioncluster_fc1natt1_mnist.md │ │ │ ├── attentioncluster_fc1natt2_mnist.md │ │ │ ├── attentioncluster_fc1natt4_mnist.md │ │ │ ├── attentioncluster_fc1natt8_mnist.md │ │ │ ├── attentioncluster_fc2natt1_mnist.md │ │ │ ├── attentioncluster_fc2natt4_mnist.md │ │ │ ├── attentioncluster_fc2natt64_mnist.md │ │ │ ├── attentioncluster_fc2natt8_mnist.md │ │ │ ├── attgan_G_celeba.md │ │ │ ├── autoaugment_cifar10.md │ │ │ ├── autodeeplab_0.5M_cityscapes.md │ │ │ ├── autodeeplab_1.5M_cityscapes.md │ │ │ ├── autodeeplab_1M_cityscapes.md │ │ │ ├── autodis_criteo.md │ │ │ ├── bertbase_cnnews128.md │ │ │ ├── bertbilstmcrf_ner.md │ │ │ ├── bertfinetuning_classifier_cola.md │ │ │ ├── bertfinetuning_nercrf_cluener.md │ │ │ ├── bertfinetuning_nersoftmax_cluener.md │ │ │ ├── bertfinetuning_squad_squadv1.1.md │ │ │ ├── bertlarge_cnnews128.md │ │ │ ├── bgcf_amazonbeauty.md │ │ │ ├── cct_imagenet2012.md │ │ │ ├── centerface_widerface.md │ │ │ ├── centernet_coco2017.md │ │ │ ├── centernetdet_coco2017.md │ │ │ ├── centernetresnet101_coco2017.md │ │ │ ├── centernetresnet50v1_coco2017.md │ │ │ ├── cgan_G_mnist.md │ │ │ ├── cnnctc_mjstiiit.md │ │ │ ├── cnndirectionmodel_fsns.md │ │ │ ├── crnn_synth.md │ │ │ ├── crnnseq2seqocr_fsns.md │ │ │ ├── cspdarknet53_imagenet2012.md │ │ │ ├── ctcmodel_timit.md │ │ │ ├── ctpn_icdar2013.md │ │ │ ├── cyclegan_GA_apple2orange.md │ │ │ ├── cyclegan_GA_horse2zebra.md │ │ │ ├── cyclegan_GB_apple2orange.md │ │ │ ├── cyclegan_GB_horse2zebra.md │ │ │ ├── dam_douban.md │ │ │ ├── dam_ubuntu.md │ │ │ ├── dbpn_div2k.md │ │ │ ├── dbpngan_G_div2k.md │ │ │ ├── ddrnet_imagenet2012.md │ │ │ ├── deepfm_criteo.md │ │ │ ├── deepid_youtubeface.md │ │ │ ├── deeplabv3s16_voc2012.md │ │ │ ├── deeplabv3s8r2_voc2012.md │ │ │ ├── deeptext_icdar2013_scutforu_cocotextv2.md │ │ │ ├── delf_gldv2.md │ │ │ ├── densenet121_imagenet2012.md │ │ │ ├── dgcn_citeseer.md │ │ │ ├── dgcn_cora.md │ │ │ ├── dgcn_pubmed.md │ │ │ ├── dgu_udc.md │ │ │ ├── dlrm_criteo.md │ │ │ ├── dpn_imagenet2012.md │ │ │ ├── duconv_duconv.md │ │ │ ├── ecolite_ucf101.md │ │ │ ├── efficientnetb1_imagenet2012.md │ │ │ ├── efficientnetv2_imagenet2012.md │ │ │ ├── emotect_baidu.md │ │ │ ├── esrgan_generator_div2k.md │ │ │ ├── faceattribute_fairface_rwmfd.md │ │ │ ├── facedetection_widerface.md │ │ │ ├── facequalityassessment_300wlp.md │ │ │ ├── facerecognition_ms1mv2.md │ │ │ ├── facerecognitionfortracking_lfw.md │ │ │ ├── fasterrcnndcn_coco2017.md │ │ │ ├── fasterrcnnresnetv1101_coco2017.md │ │ │ ├── fasterrcnnresnetv1152_coco2017.md │ │ │ ├── fasterrcnnresnetv150_coco2017.md │ │ │ ├── fasttext_agnews.md │ │ │ ├── fasttext_dbpedia.md │ │ │ ├── fasttext_yelp.md │ │ │ ├── fatdeepffm_criteo.md │ │ │ ├── fcn4_musictagging.md │ │ │ ├── fcn8s_voc2012.md │ │ │ ├── gan_G_mnist.md │ │ │ ├── gat_citeseer.md │ │ │ ├── gat_cora.md │ │ │ ├── gcn_citesser.md │ │ │ ├── gcn_cora.md │ │ │ ├── genetres50_minux_imagenet2012.md │ │ │ ├── genetres50_plus_imagenet2012.md │ │ │ ├── genetres50_theta_imagenet2012.md │ │ │ ├── ghostnet_imagenet2012.md │ │ │ ├── ghostnetquant_imagenet2012.md │ │ │ ├── gloreres200_imagenet2012.md │ │ │ ├── gloreres50_imagenet2012.md │ │ │ ├── gnmtv2_wmtende.md │ │ │ ├── googlenet_cifar10.md │ │ │ ├── googlenet_imagenet2012.md │ │ │ ├── gpt3_openweb.md │ │ │ ├── gru_muti30k.md │ │ │ ├── hardnet_imagenet2012.md │ │ │ ├── hournas_cifar10.md │ │ │ ├── hrnetw48cls_imagenet2012.md │ │ │ ├── ibnnet_imagenet2012.md │ │ │ ├── icnet_cityscapes.md │ │ │ ├── inceptionresnetv2_imagenet2012.md │ │ │ ├── inceptionv2_imagenet2012.md │ │ │ ├── inceptionv3_imagenet2012.md │ │ │ ├── inceptionv4_imagenet2012.md │ │ │ ├── ipt_denoise30_cbsd68.md │ │ │ ├── ipt_denoise50_cbsd68.md │ │ │ ├── ipt_derain_rain100l.md │ │ │ ├── ipt_x2_set5.md │ │ │ ├── ipt_x3_set5.md │ │ │ ├── ipt_x4_set5.md │ │ │ ├── irn_div2k.md │ │ │ ├── learningtoseeinthedark_sony.md │ │ │ ├── lenet_mnist.md │ │ │ ├── lenetquant_mnist.md │ │ │ ├── lightcnn_msceleb1m.md │ │ │ ├── lstm_aclimdbv1.md │ │ │ ├── luke_squad1.1.md │ │ │ ├── manidp_cifar10.md │ │ │ ├── maskrcnn_coco2017.md │ │ │ ├── maskrcnnmobilenetv1_coco2017.md │ │ │ ├── mass_newscrawl_gigaword_cornell.md │ │ │ ├── mmoe_censusincome.md │ │ │ ├── mnasnet_imagenet2012.md │ │ │ ├── mobilenetv1_cifar10.md │ │ │ ├── mobilenetv1_imagenet2012.md │ │ │ ├── mobilenetv2_imagenet2012.md │ │ │ ├── mobilenetv2quant_imagenet2012.md │ │ │ ├── mobilenetv3_imagenet2012.md │ │ │ ├── mobilenetv3large_imagenet2012.md │ │ │ ├── moleculardynamics_water.md │ │ │ ├── multitasknet_veri.md │ │ │ ├── naml_mindlarge.md │ │ │ ├── nasfpn_coco2017.md │ │ │ ├── nasnet_imagenet2012.md │ │ │ ├── ncf_movielens.md │ │ │ ├── neighbor2neighbor_kodak.md │ │ │ ├── nfnet_imagenet2012.md │ │ │ ├── ntsnet_cub2002011.md │ │ │ ├── openpose_coco2017.md │ │ │ ├── osnet_dukemtmc.md │ │ │ ├── osnet_market1501.md │ │ │ ├── osnet_msmt17.md │ │ │ ├── pgan_G_celeba.md │ │ │ ├── pix2pix_G_facades.md │ │ │ ├── pix2pix_G_maps.md │ │ │ ├── pnasnet_imagenet2012.md │ │ │ ├── pointnet2_modelnet40.md │ │ │ ├── pointnet_shapenet.md │ │ │ ├── poseestnet_veir.md │ │ │ ├── posenet_kingscollege.md │ │ │ ├── posenet_stmaryschurch.md │ │ │ ├── protonet_omniglot.md │ │ │ ├── proxylessnas_imagenet2012.md │ │ │ ├── psenet_icdar2015.md │ │ │ ├── pspnet_ade20k.md │ │ │ ├── pspnet_voc2012.md │ │ │ ├── ras_dutstrain.md │ │ │ ├── refinedet_coco2017.md │ │ │ ├── renas_cifar10.md │ │ │ ├── resnest50_imagenet2012.md │ │ │ ├── resnet101_imagenet2012.md │ │ │ ├── resnet152_imagenet2012.md │ │ │ ├── resnet18_cifar10.md │ │ │ ├── resnet18_imagenet2012.md │ │ │ ├── resnet34_imagenet2012.md │ │ │ ├── resnet50_cifar10.md │ │ │ ├── resnet50_imagenet2012.md │ │ │ ├── resnet50advpruning_imagenet2012.md │ │ │ ├── resnet50quant_imagenet2012.md │ │ │ ├── resnet50thor_imagenet2012.md │ │ │ ├── resnetv2101_cifar10.md │ │ │ ├── resnetv2152_cifar10.md │ │ │ ├── resnetv250_cifar10.md │ │ │ ├── resnetv250frn_imagenet2012.md │ │ │ ├── resnext101_imagenet2012.md │ │ │ ├── resnext15264x4d_imagenet2012.md │ │ │ ├── resnext50_imagenet2012.md │ │ │ ├── retinanet_coco2017.md │ │ │ ├── retinanetresnet101_coco2017.md │ │ │ ├── rotate_wn18rr.md │ │ │ ├── senet_imagenet2012.md │ │ │ ├── seresnet50_imagenet2012.md │ │ │ ├── shufflenetv1_imagenet2012.md │ │ │ ├── shufflenetv2_imagenet2012.md │ │ │ ├── siamfc_ilsvrc2015vid.md │ │ │ ├── simclr_encoder_cifar10.md │ │ │ ├── simclr_linearclassifier_cifar10.md │ │ │ ├── simplebaselines_coco2017.md │ │ │ ├── simplepose_coco2017.md │ │ │ ├── skipgram_text8.md │ │ │ ├── squeezenet11_imagenet2012.md │ │ │ ├── squeezenet_cifar10.md │ │ │ ├── squeezenetresidual_cifar10.md │ │ │ ├── squeezenetresidual_imagenet2012.md │ │ │ ├── srcnn_ilsvrc2013.md │ │ │ ├── ssd300_coco2017.md │ │ │ ├── ssdghostnet_coco2017.md │ │ │ ├── ssdmobilenetv1fpn_coco2017.md │ │ │ ├── ssdmobilenetv2_coco2017.md │ │ │ ├── ssdmobilenetv2fpnlite_coco2017.md │ │ │ ├── ssdresnet34_coco2017.md │ │ │ ├── ssdresnet50_coco2017.md │ │ │ ├── ssdresnet50fpn_coco2017.md │ │ │ ├── ssdvgg16_coco2017.md │ │ │ ├── ssimae_mvtecadbottle.md │ │ │ ├── ssimae_mvtecadcable.md │ │ │ ├── ssimae_mvtecadcapsule.md │ │ │ ├── ssimae_mvtecadcarpet.md │ │ │ ├── ssimae_mvtecadgrid.md │ │ │ ├── ssimae_mvtecadmetalnut.md │ │ │ ├── stackedhourglass_mpii.md │ │ │ ├── stargan_G_celeba.md │ │ │ ├── stgcn_cheb15_pemsd7m.md │ │ │ ├── stgcn_cheb30_pemsd7m.md │ │ │ ├── stgcn_cheb45_pemsd7m.md │ │ │ ├── stgcn_gcn15_pemsd7m.md │ │ │ ├── stgcn_gcn30_pemsd7m.md │ │ │ ├── stgcn_gcn45_pemsd7m.md │ │ │ ├── stnet_kinetics400.md │ │ │ ├── swintransformer_imagenet2012.md │ │ │ ├── tacotron2_ljspeech1.1.md │ │ │ ├── tcn_addingproblem.md │ │ │ ├── tcn_permutedmnist.md │ │ │ ├── textcnn_moviereview.md │ │ │ ├── textcnn_sst2.md │ │ │ ├── textcnn_subj.md │ │ │ ├── textcrnn_gru_polarity_moviereview.md │ │ │ ├── textcrnn_lstm_polarity_moviereview.md │ │ │ ├── textfusenet_totaltext.md │ │ │ ├── tgcn_losloop.md │ │ │ ├── tgcn_sztaxi.md │ │ │ ├── tinybert_enwiki128_mnli.md │ │ │ ├── tinybert_enwiki128_qnli.md │ │ │ ├── tinybert_enwiki128_sst2.md │ │ │ ├── tinydarknet_imagenet2012.md │ │ │ ├── tnt_imagenet2012.md │ │ │ ├── tprr_readeralbert_hotpotqa.md │ │ │ ├── tprr_readerdownstream_hotpotqa.md │ │ │ ├── tprr_rerankalbert_hotpotqa.md │ │ │ ├── tprr_rerankdownstream_hotpotqa.md │ │ │ ├── transformer_wmt.md │ │ │ ├── tsm_somethingsomethingv2.md │ │ │ ├── u2net_dutstr.md │ │ │ ├── ugatit_selfie2anime.md │ │ │ ├── unet2d_isbichallenge.md │ │ │ ├── unet3d_luna16.md │ │ │ ├── unet3plus_lits2017.md │ │ │ ├── unet_medical_isbi.md │ │ │ ├── unetnested_cellnuclei.md │ │ │ ├── vgg16_bn_imagenet2012.md │ │ │ ├── vgg16_cifar10.md │ │ │ ├── vgg16_imagenet2012.md │ │ │ ├── vgg19_cifar10.md │ │ │ ├── vgg19_imagenet2012.md │ │ │ ├── vitbase_cifar10.md │ │ │ ├── warpctc_captcha.md │ │ │ ├── wgan_batchnorm_G_lsunbedrooms.md │ │ │ ├── wgan_nobatchnorm_G_lsunbedrooms.md │ │ │ ├── wideanddeep_criteo.md │ │ │ ├── wideanddeepmultitable_outbrain.md │ │ │ ├── wideresnet_cifar10.md │ │ │ ├── xception_imagenet2012.md │ │ │ ├── yolov3darknet53_coco2014.md │ │ │ ├── yolov3darknet53quant_coco2014.md │ │ │ ├── yolov3darknet53shape416_coco2014.md │ │ │ ├── yolov3resnet18_coco2017.md │ │ │ ├── yolov4_coco2017.md │ │ │ └── yolov5shape640_coco2017.md │ │ ├── 1.5 │ │ │ ├── advancedeast_icpr2018.md │ │ │ ├── albert_mnli.md │ │ │ ├── albert_squadv1.1.md │ │ │ ├── albert_sst2.md │ │ │ ├── alexnet_cifar10.md │ │ │ ├── alexnet_imagenet2012.md │ │ │ ├── arbitrarystyletransfer_mscoco.md │ │ │ ├── arcface_ms1mv2.md │ │ │ ├── attgan_G_celeba.md │ │ │ ├── autoaugment_cifar10.md │ │ │ ├── autodeeplab_0.5M_cityscapes.md │ │ │ ├── autodeeplab_1.5M_cityscapes.md │ │ │ ├── autodeeplab_1M_cityscapes.md │ │ │ ├── autodis_criteo.md │ │ │ ├── avacifar_cifar10.md │ │ │ ├── avahpa_hpa.md │ │ │ ├── bertbase_cnnews128.md │ │ │ ├── bertbilstmcrf_ner.md │ │ │ ├── bertfinetuning_classifier_cola.md │ │ │ ├── bertfinetuning_nercrf_cluener.md │ │ │ ├── bertfinetuning_nersoftmax_cluener.md │ │ │ ├── bertfinetuning_squad_squadv1.1.md │ │ │ ├── bertlarge_cnnews128.md │ │ │ ├── bgcf_amazonbeauty.md │ │ │ ├── brdnet_waterloo.md │ │ │ ├── c3d_hmdb51.md │ │ │ ├── c3d_ucf101.md │ │ │ ├── centerface_widerface.md │ │ │ ├── centernet_coco2017.md │ │ │ ├── centernetdet_coco2017.md │ │ │ ├── centernetresnet101_coco2017.md │ │ │ ├── centernetresnet50v1_coco2017.md │ │ │ ├── cgan_G_mnist.md │ │ │ ├── cnnctc_mjstiiit.md │ │ │ ├── cnndirectionmodel_fsns.md │ │ │ ├── crnn_synth.md │ │ │ ├── crnnseq2seqocr_fsns.md │ │ │ ├── cspdarknet53_imagenet2012.md │ │ │ ├── ctpn_icdar2013.md │ │ │ ├── cyclegan_GA_apple2orange.md │ │ │ ├── cyclegan_GA_horse2zebra.md │ │ │ ├── cyclegan_GB_apple2orange.md │ │ │ ├── cyclegan_GB_horse2zebra.md │ │ │ ├── dcgan_imagenet2012.md │ │ │ ├── ddpg_actornet_none.md │ │ │ ├── ddpg_actortarget_none.md │ │ │ ├── ddpg_criticnet_none.md │ │ │ ├── ddpg_critictarget_none.md │ │ │ ├── deepfm_criteo.md │ │ │ ├── deeplabv3s16_voc2012.md │ │ │ ├── deeplabv3s8r2_voc2012.md │ │ │ ├── deepsort_market1501.md │ │ │ ├── deeptext_icdar2013_scutforu_cocotextv2.md │ │ │ ├── dem_att_cub.md │ │ │ ├── densenet121_imagenet2012.md │ │ │ ├── dgcn_citeseer.md │ │ │ ├── dgcn_cora.md │ │ │ ├── dgcn_pubmed.md │ │ │ ├── dgu_udc.md │ │ │ ├── dncnn_bsd500.md │ │ │ ├── dpn_imagenet2012.md │ │ │ ├── east_icdar2015.md │ │ │ ├── ecolite_ucf101.md │ │ │ ├── efficientnetb0_imagenet2012.md │ │ │ ├── efficientnetb2_imagenet2012.md │ │ │ ├── efficientnetb3_imagenet2012.md │ │ │ ├── emotect_baidu.md │ │ │ ├── ernie_bdqa.md │ │ │ ├── ernie_brcd.md │ │ │ ├── ernie_chnsenticorp.md │ │ │ ├── ernie_cmcr.md │ │ │ ├── ernie_msraner.md │ │ │ ├── ernie_xnli.md │ │ │ ├── faceattribute_fairface_rwmfd.md │ │ │ ├── facedetection_widerface.md │ │ │ ├── facequalityassessment_300wlp.md │ │ │ ├── facerecognition_ms1mv2.md │ │ │ ├── facerecognitionfortracking_lfw.md │ │ │ ├── fasterrcnnresnetv1101_coco2017.md │ │ │ ├── fasterrcnnresnetv1152_coco2017.md │ │ │ ├── fasterrcnnresnetv150_coco2017.md │ │ │ ├── fastscnn_cityspaces.md │ │ │ ├── fasttext_agnews.md │ │ │ ├── fasttext_dbpedia.md │ │ │ ├── fasttext_yelp.md │ │ │ ├── fatdeepffm_criteo.md │ │ │ ├── fcn4_musictagging.md │ │ │ ├── fcn8s_voc2012.md │ │ │ ├── fishnet99_imagenet2012.md │ │ │ ├── gan_G_mnist.md │ │ │ ├── gat_citeseer.md │ │ │ ├── gat_cora.md │ │ │ ├── gcn_citesser.md │ │ │ ├── gcn_cora.md │ │ │ ├── genetres50_minux_imagenet2012.md │ │ │ ├── genetres50_plus_imagenet2012.md │ │ │ ├── genetres50_theta_imagenet2012.md │ │ │ ├── ghostnet_imagenet2012.md │ │ │ ├── ghostnetquant_imagenet2012.md │ │ │ ├── gloreres200_imagenet2012.md │ │ │ ├── gloreres50_imagenet2012.md │ │ │ ├── gnmtv2_wmtende.md │ │ │ ├── googlenet_cifar10.md │ │ │ ├── googlenet_imagenet2012.md │ │ │ ├── gpt3_openweb.md │ │ │ ├── gru_muti30k.md │ │ │ ├── hardnet_imagenet2012.md │ │ │ ├── hournas_cifar10.md │ │ │ ├── ibnnet_imagenet2012.md │ │ │ ├── icnet_cityscapes.md │ │ │ ├── inceptionresnetv2_imagenet2012.md │ │ │ ├── inceptionv3_imagenet2012.md │ │ │ ├── inceptionv4_imagenet2012.md │ │ │ ├── ipt_denoise30_cbsd68.md │ │ │ ├── ipt_denoise50_cbsd68.md │ │ │ ├── ipt_derain_rain100l.md │ │ │ ├── ipt_x2_set5.md │ │ │ ├── ipt_x3_set5.md │ │ │ ├── ipt_x4_set5.md │ │ │ ├── ktnet_record.md │ │ │ ├── ktnet_squad.md │ │ │ ├── learningtoseeinthedark_sony.md │ │ │ ├── lenet_mnist.md │ │ │ ├── lenetquant_mnist.md │ │ │ ├── lightcnn_msceleb1m.md │ │ │ ├── lstm_aclimdbv1.md │ │ │ ├── manidp_cifar10.md │ │ │ ├── maskrcnn_coco2017.md │ │ │ ├── maskrcnnmobilenetv1_coco2017.md │ │ │ ├── mass_newscrawl_gigaword_cornell.md │ │ │ ├── mcnn_shanghaitecha.md │ │ │ ├── midas_redweb.md │ │ │ ├── mnasnet_imagenet2012.md │ │ │ ├── mobilenetv1_cifar10.md │ │ │ ├── mobilenetv1_imagenet2012.md │ │ │ ├── mobilenetv2_imagenet2012.md │ │ │ ├── mobilenetv2quant_imagenet2012.md │ │ │ ├── mobilenetv3_imagenet2012.md │ │ │ ├── mobilenetv3large_imagenet2012.md │ │ │ ├── mobilenetv3smallx10_imagenet2012.md │ │ │ ├── moleculardynamics_water.md │ │ │ ├── naml_mindlarge.md │ │ │ ├── nasfpn_coco2017.md │ │ │ ├── nasnet_imagenet2012.md │ │ │ ├── ncf_movielens.md │ │ │ ├── ntsnet_cub2002011.md │ │ │ ├── openpose_coco2017.md │ │ │ ├── pgan_G_celeba.md │ │ │ ├── pix2pix_G_facades.md │ │ │ ├── pix2pix_G_maps.md │ │ │ ├── pointnet2_modelnet40.md │ │ │ ├── posenet_kingscollege.md │ │ │ ├── posenet_stmaryschurch.md │ │ │ ├── protonet_omniglot.md │ │ │ ├── psenet_icdar2015.md │ │ │ ├── pspnet_ade20k.md │ │ │ ├── pspnet_voc2012.md │ │ │ ├── r2plus1d_ucf101.md │ │ │ ├── rednet30_bsd300.md │ │ │ ├── relationnet_omniglot.md │ │ │ ├── renas_cifar10.md │ │ │ ├── resnet101_imagenet2012.md │ │ │ ├── resnet152_imagenet2012.md │ │ │ ├── resnet18_cifar10.md │ │ │ ├── resnet18_imagenet2012.md │ │ │ ├── resnet34_imagenet2012.md │ │ │ ├── resnet50_cifar10.md │ │ │ ├── resnet50_imagenet2012.md │ │ │ ├── resnet50_quadruplet_sop.md │ │ │ ├── resnet50_triplet_sop.md │ │ │ ├── resnet50advpruning_imagenet2012.md │ │ │ ├── resnet50bam_imagenet2012.md │ │ │ ├── resnet50quant_imagenet2012.md │ │ │ ├── resnet50thor_imagenet2012.md │ │ │ ├── resnetv2101_cifar10.md │ │ │ ├── resnetv2152_cifar10.md │ │ │ ├── resnetv250_cifar10.md │ │ │ ├── resnext101_imagenet2012.md │ │ │ ├── resnext15264x4d_imagenet2012.md │ │ │ ├── resnext50_imagenet2012.md │ │ │ ├── retinafaceresnet50_widerface.md │ │ │ ├── retinanet_coco2017.md │ │ │ ├── retinanetresnet101_coco2017.md │ │ │ ├── rotate_wn18rr.md │ │ │ ├── sdne_wiki.md │ │ │ ├── senet_imagenet2012.md │ │ │ ├── seq2seq_wmt14.md │ │ │ ├── seresnet50_imagenet2012.md │ │ │ ├── seresnext50_imagenet2012.md │ │ │ ├── shufflenetv1_imagenet2012.md │ │ │ ├── shufflenetv2_imagenet2012.md │ │ │ ├── siamfc_ilsvrc2015vid.md │ │ │ ├── siamrpn_vid.md │ │ │ ├── simclr_encoder_cifar10.md │ │ │ ├── simclr_linearclassifier_cifar10.md │ │ │ ├── simplebaselines_coco2017.md │ │ │ ├── simplepose_coco2017.md │ │ │ ├── singlepathnas_imagenet2012.md │ │ │ ├── skipgram_text8.md │ │ │ ├── sknet_cifar10.md │ │ │ ├── sppnet_mult_imagenet2012.md │ │ │ ├── sppnet_single_imagenet2012.md │ │ │ ├── sppnet_zfnet_imagenet2012.md │ │ │ ├── squeezenet11_imagenet2012.md │ │ │ ├── squeezenet_cifar10.md │ │ │ ├── squeezenetresidual_cifar10.md │ │ │ ├── squeezenetresidual_imagenet2012.md │ │ │ ├── srcnn_ilsvrc2013.md │ │ │ ├── srgan_div2k.md │ │ │ ├── ssd300_coco2017.md │ │ │ ├── ssdghostnet_coco2017.md │ │ │ ├── ssdmobilenetv1fpn_coco2017.md │ │ │ ├── ssdmobilenetv2_coco2017.md │ │ │ ├── ssdmobilenetv2fpnlite_coco2017.md │ │ │ ├── ssdresnet50_coco2017.md │ │ │ ├── ssdresnet50fpn_coco2017.md │ │ │ ├── ssdvgg16_coco2017.md │ │ │ ├── ssimae_mvtecadbottle.md │ │ │ ├── ssimae_mvtecadcable.md │ │ │ ├── ssimae_mvtecadcapsule.md │ │ │ ├── ssimae_mvtecadcarpet.md │ │ │ ├── ssimae_mvtecadgrid.md │ │ │ ├── ssimae_mvtecadmetalnut.md │ │ │ ├── stackedhourglass_mpii.md │ │ │ ├── stargan_G_celeba.md │ │ │ ├── stgan_celeba.md │ │ │ ├── stgcn_cheb15_pemsd7m.md │ │ │ ├── stgcn_cheb30_pemsd7m.md │ │ │ ├── stgcn_cheb45_pemsd7m.md │ │ │ ├── stgcn_gcn15_pemsd7m.md │ │ │ ├── stgcn_gcn30_pemsd7m.md │ │ │ ├── stgcn_gcn45_pemsd7m.md │ │ │ ├── tacotron2_ljspeech1.1.md │ │ │ ├── tbnet_steam.md │ │ │ ├── textcnn_moviereview.md │ │ │ ├── textcnn_sst2.md │ │ │ ├── textcnn_subj.md │ │ │ ├── textcrnn_gru_polarity_moviereview.md │ │ │ ├── textcrnn_lstm_polarity_moviereview.md │ │ │ ├── tgcn_losloop.md │ │ │ ├── tgcn_sztaxi.md │ │ │ ├── tinybert_enwiki128_mnli.md │ │ │ ├── tinybert_enwiki128_qnli.md │ │ │ ├── tinybert_enwiki128_sst2.md │ │ │ ├── tinydarknet_imagenet2012.md │ │ │ ├── tnt_imagenet2012.md │ │ │ ├── tprr_readeralbert_hotpotqa.md │ │ │ ├── tprr_readerdownstream_hotpotqa.md │ │ │ ├── tprr_rerankalbert_hotpotqa.md │ │ │ ├── tprr_rerankdownstream_hotpotqa.md │ │ │ ├── transformer_wmt.md │ │ │ ├── u2net_dutstr.md │ │ │ ├── unet2d_isbichallenge.md │ │ │ ├── unet3d_luna16.md │ │ │ ├── unet_medical_isbi.md │ │ │ ├── unetnested_cellnuclei.md │ │ │ ├── vgg16_bn_imagenet2012.md │ │ │ ├── vgg16_cifar10.md │ │ │ ├── vgg16_imagenet2012.md │ │ │ ├── vgg19_cifar10.md │ │ │ ├── vgg19_imagenet2012.md │ │ │ ├── vit_imagenet2012.md │ │ │ ├── vitbase_cifar10.md │ │ │ ├── warpctc_captcha.md │ │ │ ├── wdsr_div2k.md │ │ │ ├── wgan_batchnorm_G_lsunbedrooms.md │ │ │ ├── wgan_nobatchnorm_G_lsunbedrooms.md │ │ │ ├── wideanddeep_criteo.md │ │ │ ├── wideanddeepmultitable_outbrain.md │ │ │ ├── wideresnet_cifar10.md │ │ │ ├── xception_imagenet2012.md │ │ │ ├── yolov3darknet53_coco2014.md │ │ │ ├── yolov3darknet53quant_coco2014.md │ │ │ ├── yolov3darknet53shape416_coco2014.md │ │ │ ├── yolov3resnet18_coco2017.md │ │ │ ├── yolov3tiny_coco2017.md │ │ │ ├── yolov4_coco2017.md │ │ │ ├── yolov5shape640_coco2017.md │ │ │ └── yolox_coco2017.md │ │ ├── 1.6 │ │ │ ├── 3dcnn_brast2017.md │ │ │ ├── 3ddensenet_iseg2017.md │ │ │ ├── adnet_vot2013vot2014.md │ │ │ ├── advancedeast_icpr2018.md │ │ │ ├── aecrnet_reside.md │ │ │ ├── albert_mnli.md │ │ │ ├── albert_squadv1.1.md │ │ │ ├── albert_sst2.md │ │ │ ├── alexnet_cifar10.md │ │ │ ├── alexnet_imagenet2012.md │ │ │ ├── alphapose_coco2017.md │ │ │ ├── apdrawinggan_generator_apdrawingdb.md │ │ │ ├── arbitrarystyletransfer_mscoco.md │ │ │ ├── arcface_ms1mv2.md │ │ │ ├── attentioncluster_fc1natt1_mnist.md │ │ │ ├── attentioncluster_fc1natt2_mnist.md │ │ │ ├── attentioncluster_fc1natt4_mnist.md │ │ │ ├── attentioncluster_fc1natt8_mnist.md │ │ │ ├── attentioncluster_fc2natt1_mnist.md │ │ │ ├── attentioncluster_fc2natt4_mnist.md │ │ │ ├── attentioncluster_fc2natt64_mnist.md │ │ │ ├── attentioncluster_fc2natt8_mnist.md │ │ │ ├── attgan_G_celeba.md │ │ │ ├── augvit_cifar10.md │ │ │ ├── autoaugment_cifar10.md │ │ │ ├── autodeeplab_0.5M_cityscapes.md │ │ │ ├── autodeeplab_1.5M_cityscapes.md │ │ │ ├── autodeeplab_1M_cityscapes.md │ │ │ ├── autodis_criteo.md │ │ │ ├── autoslim_imagenet2012.md │ │ │ ├── avacifar_cifar10.md │ │ │ ├── avahpa_hpa.md │ │ │ ├── bertbase_cnnews128.md │ │ │ ├── bertbilstmcrf_ner.md │ │ │ ├── bertfinetuning_classifier_cola.md │ │ │ ├── bertfinetuning_nercrf_cluener.md │ │ │ ├── bertfinetuning_nersoftmax_cluener.md │ │ │ ├── bertfinetuning_squad_squadv1.1.md │ │ │ ├── bertlarge_cnnews128.md │ │ │ ├── bgcf_amazonbeauty.md │ │ │ ├── brdnet_waterloo.md │ │ │ ├── c3d_hmdb51.md │ │ │ ├── c3d_ucf101.md │ │ │ ├── cascadercnn_coco2017.md │ │ │ ├── cct_imagenet2012.md │ │ │ ├── centerface_widerface.md │ │ │ ├── centernet_coco2017.md │ │ │ ├── centernetdet_coco2017.md │ │ │ ├── centernetresnet101_coco2017.md │ │ │ ├── centernetresnet50v1_coco2017.md │ │ │ ├── cgan_G_mnist.md │ │ │ ├── cnnctc_mjstiiit.md │ │ │ ├── cnndirectionmodel_fsns.md │ │ │ ├── crnn_synth.md │ │ │ ├── crnnseq2seqocr_fsns.md │ │ │ ├── csd_div2k.md │ │ │ ├── cspdarknet53_imagenet2012.md │ │ │ ├── ctcmodel_timit.md │ │ │ ├── ctpn_icdar2013.md │ │ │ ├── cyclegan_GA_apple2orange.md │ │ │ ├── cyclegan_GA_horse2zebra.md │ │ │ ├── cyclegan_GB_apple2orange.md │ │ │ ├── cyclegan_GB_horse2zebra.md │ │ │ ├── dam_douban.md │ │ │ ├── dam_ubuntu.md │ │ │ ├── dbpn_div2k.md │ │ │ ├── dbpngan_G_div2k.md │ │ │ ├── dcgan_imagenet2012.md │ │ │ ├── ddag_allresearch_sysumm01.md │ │ │ ├── ddag_indoorsearch_sysumm01.md │ │ │ ├── ddag_infraredtovisible_regdb.md │ │ │ ├── ddag_visibletoinfrared_regdb.md │ │ │ ├── ddrnet_imagenet2012.md │ │ │ ├── deepfm_criteo.md │ │ │ ├── deepid_youtubeface.md │ │ │ ├── deeplabv3plus_s16_voc2012.md │ │ │ ├── deeplabv3plus_s8r2_voc2012.md │ │ │ ├── deeplabv3s16_voc2012.md │ │ │ ├── deeplabv3s8r2_voc2012.md │ │ │ ├── deepsort_market1501.md │ │ │ ├── deeptext_icdar2013_scutforu_cocotextv2.md │ │ │ ├── delf_gldv2.md │ │ │ ├── delf_googlelandmarksdatasetv2.md │ │ │ ├── dem_att_cub.md │ │ │ ├── densenet121_imagenet2012.md │ │ │ ├── depthnet_coarsenet_nyu.md │ │ │ ├── depthnet_finenet_nyu.md │ │ │ ├── dgcn_citeseer.md │ │ │ ├── dgcn_cora.md │ │ │ ├── dgcn_pubmed.md │ │ │ ├── dgu_udc.md │ │ │ ├── dlinknet_DeepGlobeRoadExtractionDataset.md │ │ │ ├── dlrm_criteo.md │ │ │ ├── dncnn_bsd500.md │ │ │ ├── dpn_imagenet2012.md │ │ │ ├── dscnn_speechcommandsdatasetversion1.md │ │ │ ├── duconv_duconv.md │ │ │ ├── east_icdar2015.md │ │ │ ├── ecolite_ucf101.md │ │ │ ├── edcn_criteo.md │ │ │ ├── efficientnetb0_imagenet2012.md │ │ │ ├── efficientnetb1_imagenet2012.md │ │ │ ├── efficientnetb2_imagenet2012.md │ │ │ ├── efficientnetb3_imagenet2012.md │ │ │ ├── egnet_resnet_dutstr.md │ │ │ ├── egnet_vgg_dutstr.md │ │ │ ├── emotect_baidu.md │ │ │ ├── enet_cityscapes.md │ │ │ ├── erfnet_cityscapes.md │ │ │ ├── ernie_bdqa.md │ │ │ ├── ernie_brcd.md │ │ │ ├── ernie_chnsenticorp.md │ │ │ ├── ernie_cmcr.md │ │ │ ├── ernie_msraner.md │ │ │ ├── ernie_xnli.md │ │ │ ├── esrgan_generator_div2k.md │ │ │ ├── faceattribute_fairface_rwmfd.md │ │ │ ├── facedetection_widerface.md │ │ │ ├── facequalityassessment_300wlp.md │ │ │ ├── facerecognition_ms1mv2.md │ │ │ ├── facerecognitionfortracking_lfw.md │ │ │ ├── fasterrcnndcn_coco2017.md │ │ │ ├── fasterrcnnresnetv1101_coco2017.md │ │ │ ├── fasterrcnnresnetv1152_coco2017.md │ │ │ ├── fasterrcnnresnetv150_coco2017.md │ │ │ ├── fastscnn_cityscapes.md │ │ │ ├── fastscnn_cityspaces.md │ │ │ ├── fasttext_agnews.md │ │ │ ├── fasttext_dbpedia.md │ │ │ ├── fasttext_yelp.md │ │ │ ├── fatdeepffm_criteo.md │ │ │ ├── fcanet_augmentedpascal.md │ │ │ ├── fcn4_musictagging.md │ │ │ ├── fcn8s_voc2012.md │ │ │ ├── fdabnn_cifar10.md │ │ │ ├── fishnet99_imagenet2012.md │ │ │ ├── gan_G_mnist.md │ │ │ ├── gat_citeseer.md │ │ │ ├── gat_cora.md │ │ │ ├── gcn_citesser.md │ │ │ ├── gcn_cora.md │ │ │ ├── genetres50_minux_imagenet2012.md │ │ │ ├── genetres50_plus_imagenet2012.md │ │ │ ├── genetres50_theta_imagenet2012.md │ │ │ ├── ghostnet_imagenet2012.md │ │ │ ├── ghostnetquant_imagenet2012.md │ │ │ ├── gloreres200_imagenet2012.md │ │ │ ├── gloreres50_imagenet2012.md │ │ │ ├── gnmtv2_wmtende.md │ │ │ ├── googlenet_cifar10.md │ │ │ ├── googlenet_imagenet2012.md │ │ │ ├── gpt3_openweb.md │ │ │ ├── gru_muti30k.md │ │ │ ├── hardnet_imagenet2012.md │ │ │ ├── hournas_cifar10.md │ │ │ ├── hrnet_cityscapes.md │ │ │ ├── hrnetw48cls_imagenet2012.md │ │ │ ├── hrnetw48seg_cityscapes.md │ │ │ ├── hypertext_iflytek.md │ │ │ ├── hypertext_tnews.md │ │ │ ├── ibnnet_imagenet2012.md │ │ │ ├── icnet_cityscapes.md │ │ │ ├── inceptionresnetv2_imagenet2012.md │ │ │ ├── inceptionv3_imagenet2012.md │ │ │ ├── inceptionv4_imagenet2012.md │ │ │ ├── ipt_denoise30_cbsd68.md │ │ │ ├── ipt_denoise50_cbsd68.md │ │ │ ├── ipt_derain_rain100l.md │ │ │ ├── ipt_x2_set5.md │ │ │ ├── ipt_x3_set5.md │ │ │ ├── ipt_x4_set5.md │ │ │ ├── irn_div2k.md │ │ │ ├── ktnet_record.md │ │ │ ├── ktnet_squad.md │ │ │ ├── learningtoseeinthedark_sony.md │ │ │ ├── lenet_mnist.md │ │ │ ├── lenetquant_mnist.md │ │ │ ├── lightcnn_msceleb1m.md │ │ │ ├── lpcnet_librispeech.md │ │ │ ├── lstm_aclimdbv1.md │ │ │ ├── lstmcrf_conll2000.md │ │ │ ├── luke_squad1.1.md │ │ │ ├── manidp_cifar10.md │ │ │ ├── maskrcnn_coco2017.md │ │ │ ├── maskrcnnmobilenetv1_coco2017.md │ │ │ ├── mass_newscrawl_gigaword_cornell.md │ │ │ ├── mcnn_shanghaitecha.md │ │ │ ├── melgan_ljspeech.md │ │ │ ├── metabaseline_miniimagenet.md │ │ │ ├── midas_redweb.md │ │ │ ├── mmoe_censusincome.md │ │ │ ├── mnasnet_imagenet2012.md │ │ │ ├── mobilenetv1_cifar10.md │ │ │ ├── mobilenetv1_imagenet2012.md │ │ │ ├── mobilenetv2_imagenet2012.md │ │ │ ├── mobilenetv2quant_imagenet2012.md │ │ │ ├── mobilenetv3_imagenet2012.md │ │ │ ├── mobilenetv3large_imagenet2012.md │ │ │ ├── mobilenetv3small_imagenet2012.md │ │ │ ├── mobilenetv3smallx10_imagenet2012.md │ │ │ ├── moleculardynamics_water.md │ │ │ ├── multitasknet_veri.md │ │ │ ├── mvd_allresearch_sysumm01.md │ │ │ ├── mvd_indoorsearch_sysumm01.md │ │ │ ├── mvd_infraredtovisible_regdb.md │ │ │ ├── mvd_visibletoinfrared_regdb.md │ │ │ ├── naml_mindlarge.md │ │ │ ├── nasfpn_coco2017.md │ │ │ ├── nasnet_imagenet2012.md │ │ │ ├── ncf_movielens.md │ │ │ ├── neighbor2neighbor_kodak.md │ │ │ ├── nfnet_imagenet2012.md │ │ │ ├── nima_avadataset.md │ │ │ ├── ntsnet_cub2002011.md │ │ │ ├── ocrnet_cityscapes.md │ │ │ ├── octsqueeze_kitti.md │ │ │ ├── openpose_coco2017.md │ │ │ ├── opt_cococaption.md │ │ │ ├── osnet_dukemtmc.md │ │ │ ├── osnet_market1501.md │ │ │ ├── osnet_msmt17.md │ │ │ ├── pdarts_cifar10.md │ │ │ ├── pgan_G_celeba.md │ │ │ ├── pix2pix_facades.md │ │ │ ├── pix2pix_maps.md │ │ │ ├── pnasnet_imagenet2012.md │ │ │ ├── pointnet2_modelnet40.md │ │ │ ├── pointnet_shapenet.md │ │ │ ├── poseestnet_veir.md │ │ │ ├── posenet_kingscollege.md │ │ │ ├── posenet_stmaryschurch.md │ │ │ ├── predrnnplusplus_movingmnist.md │ │ │ ├── protonet_omniglot.md │ │ │ ├── proxylessnas_imagenet2012.md │ │ │ ├── psenet_icdar2015.md │ │ │ ├── pspnet_ade20k.md │ │ │ ├── pspnet_voc2012.md │ │ │ ├── pvnet_ape_linemod.md │ │ │ ├── pvnet_cam_linemod.md │ │ │ ├── pvnet_can_linemod.md │ │ │ ├── pvnet_cat_linemod.md │ │ │ ├── pwcnet_flyingchairs.md │ │ │ ├── r2plus1d_ucf101.md │ │ │ ├── ras_dutstrain.md │ │ │ ├── rdn_div2k.md │ │ │ ├── refinedet_coco2017.md │ │ │ ├── refinenet_voc2012.md │ │ │ ├── relationnet_omniglot.md │ │ │ ├── renas_cifar10.md │ │ │ ├── res2net101_imagenet2012.md │ │ │ ├── res2net152_imagenet2012.md │ │ │ ├── res2net50_cifar10.md │ │ │ ├── res2net50_imagenet2012.md │ │ │ ├── res2netdeeplabv3_s16r1_voc2012.md │ │ │ ├── res2netdeeplabv3_s8r2_voc2012.md │ │ │ ├── res2netdeeplabv3_s8r2multiscale_voc2012.md │ │ │ ├── res2netdeeplabv3_s8r2multiscalefilp_voc2012.md │ │ │ ├── res2netfasterrcnn_coco2017.md │ │ │ ├── res2netyolov3_coco2017.md │ │ │ ├── resnest50_imagenet2012.md │ │ │ ├── resnet101_imagenet2012.md │ │ │ ├── resnet152_imagenet2012.md │ │ │ ├── resnet18_cifar10.md │ │ │ ├── resnet18_imagenet2012.md │ │ │ ├── resnet34_imagenet2012.md │ │ │ ├── resnet50_cifar10.md │ │ │ ├── resnet50_imagenet2012.md │ │ │ ├── resnet50_quadruplet_sop.md │ │ │ ├── resnet50_triplet_sop.md │ │ │ ├── resnet50advpruning_imagenet2012.md │ │ │ ├── resnet50bam_imagenet2012.md │ │ │ ├── resnet50quadruplet_sop.md │ │ │ ├── resnet50quant_imagenet2012.md │ │ │ ├── resnet50thor_imagenet2012.md │ │ │ ├── resnet50triplet_sop.md │ │ │ ├── resnetv2101_cifar10.md │ │ │ ├── resnetv2152_cifar10.md │ │ │ ├── resnetv250_cifar10.md │ │ │ ├── resnetv250frn_imagenet2012.md │ │ │ ├── resnext101_imagenet2012.md │ │ │ ├── resnext15264x4d_imagenet2012.md │ │ │ ├── resnext50_imagenet2012.md │ │ │ ├── retinafaceresnet50_widerface.md │ │ │ ├── retinanet_coco2017.md │ │ │ ├── retinanetresnet101_coco2017.md │ │ │ ├── rotate_wn18rr.md │ │ │ ├── semantichumanmatting_MattingHumanDatasets.md │ │ │ ├── senet_imagenet2012.md │ │ │ ├── senetresnet50_imagenet2012.md │ │ │ ├── seq2seq_wmt14.md │ │ │ ├── seres2net50_imagenet2012.md │ │ │ ├── seresnet50_imagenet2012.md │ │ │ ├── seresnext50_imagenet2012.md │ │ │ ├── shufflenetv1_imagenet2012.md │ │ │ ├── shufflenetv2_imagenet2012.md │ │ │ ├── siamfc_ilsvrc2015vid.md │ │ │ ├── siamrpn_vid.md │ │ │ ├── simclr_encoder_cifar10.md │ │ │ ├── simclr_linearclassifier_cifar10.md │ │ │ ├── simplebaselines_coco2017.md │ │ │ ├── simplepose_coco2017.md │ │ │ ├── singan_G_asinglenatureimage.md │ │ │ ├── singlepathnas_imagenet2012.md │ │ │ ├── skipgram_text8.md │ │ │ ├── sknet_cifar10.md │ │ │ ├── sphereface_casiawebface.md │ │ │ ├── sppnet_mult_imagenet2012.md │ │ │ ├── sppnet_single_imagenet2012.md │ │ │ ├── sppnet_zfnet_imagenet2012.md │ │ │ ├── squeezenet11_imagenet2012.md │ │ │ ├── squeezenet_cifar10.md │ │ │ ├── squeezenetresidual_cifar10.md │ │ │ ├── squeezenetresidual_imagenet2012.md │ │ │ ├── srcnn_ilsvrc2013.md │ │ │ ├── srgan_div2k.md │ │ │ ├── ssd300_coco2017.md │ │ │ ├── ssdghostnet_coco2017.md │ │ │ ├── ssdinceptionv2_coco2014mini.md │ │ │ ├── ssdmobilenetv1fpn_coco2017.md │ │ │ ├── ssdmobilenetv2_coco2017.md │ │ │ ├── ssdmobilenetv2fpnlite_coco2017.md │ │ │ ├── ssdresnet34_coco2017.md │ │ │ ├── ssdresnet50_coco2017.md │ │ │ ├── ssdresnet50fpn_coco2017.md │ │ │ ├── ssdvgg16_coco2017.md │ │ │ ├── ssimae_mvtecadbottle.md │ │ │ ├── ssimae_mvtecadcable.md │ │ │ ├── ssimae_mvtecadcapsule.md │ │ │ ├── ssimae_mvtecadcarpet.md │ │ │ ├── ssimae_mvtecadgrid.md │ │ │ ├── ssimae_mvtecadmetalnut.md │ │ │ ├── stackedhourglass_mpii.md │ │ │ ├── stargan_G_celeba.md │ │ │ ├── stgan_celeba.md │ │ │ ├── stgcn_cheb15_pemsd7m.md │ │ │ ├── stgcn_cheb30_pemsd7m.md │ │ │ ├── stgcn_cheb45_pemsd7m.md │ │ │ ├── stgcn_gcn15_pemsd7m.md │ │ │ ├── stgcn_gcn30_pemsd7m.md │ │ │ ├── stgcn_gcn45_pemsd7m.md │ │ │ ├── stnet_kinetics400.md │ │ │ ├── swintransformer_imagenet2012.md │ │ │ ├── tacotron2_ljspeech1.1.md │ │ │ ├── tall_tacos.md │ │ │ ├── tbnet_steam.md │ │ │ ├── tcn_addingproblem.md │ │ │ ├── tcn_permutedmnist.md │ │ │ ├── textcnn_moviereview.md │ │ │ ├── textcnn_sst2.md │ │ │ ├── textcnn_subj.md │ │ │ ├── textcrnn_gru_polarity_moviereview.md │ │ │ ├── textcrnn_lstm_polarity_moviereview.md │ │ │ ├── textfusenet_totaltext.md │ │ │ ├── tgcn_losloop.md │ │ │ ├── tgcn_sztaxi.md │ │ │ ├── tinybert_enwiki128_mnli.md │ │ │ ├── tinybert_enwiki128_qnli.md │ │ │ ├── tinybert_enwiki128_sst2.md │ │ │ ├── tinydarknet_imagenet2012.md │ │ │ ├── tnt_imagenet2012.md │ │ │ ├── tprr_readeralbert_hotpotqa.md │ │ │ ├── tprr_readerdownstream_hotpotqa.md │ │ │ ├── tprr_rerankalbert_hotpotqa.md │ │ │ ├── tprr_rerankdownstream_hotpotqa.md │ │ │ ├── transformer_large_wmt.md │ │ │ ├── tsm_somethingsomethingv2.md │ │ │ ├── u2net_dutstr.md │ │ │ ├── unet2d_isbichallenge.md │ │ │ ├── unet3d_luna16.md │ │ │ ├── unet3plus_lits2017.md │ │ │ ├── unet_medical_isbi.md │ │ │ ├── unetnested_cellnuclei.md │ │ │ ├── vehiclenet_vehiclenetdataset.md │ │ │ ├── vgg16_bn_imagenet2012.md │ │ │ ├── vgg16_cifar10.md │ │ │ ├── vgg16_imagenet2012.md │ │ │ ├── vgg19_cifar10.md │ │ │ ├── vgg19_imagenet2012.md │ │ │ ├── vit_imagenet2012.md │ │ │ ├── vitbase_cifar10.md │ │ │ ├── vnet_promise2012.md │ │ │ ├── warpctc_captcha.md │ │ │ ├── wdsr_div2k.md │ │ │ ├── wgan_batchnorm_G_lsunbedrooms.md │ │ │ ├── wgan_nobatchnorm_G_lsunbedrooms.md │ │ │ ├── wideanddeep_criteo.md │ │ │ ├── wideanddeepmultitable_outbrain.md │ │ │ ├── wideresnet_cifar10.md │ │ │ ├── xception_imagenet2012.md │ │ │ ├── yolov3darknet53_coco2014.md │ │ │ ├── yolov3darknet53quant_coco2014.md │ │ │ ├── yolov3darknet53shape416_coco2014.md │ │ │ ├── yolov3resnet18_coco2017.md │ │ │ ├── yolov3tiny_coco2017.md │ │ │ ├── yolov4_coco2017.md │ │ │ └── yolov5shape640_coco2017.md │ │ ├── 1.7 │ │ │ ├── 3dcnn_brast2017.md │ │ │ ├── 3ddensenet_iseg2017.md │ │ │ ├── adnet_vot2013vot2014.md │ │ │ ├── advancedeast_icpr2018.md │ │ │ ├── aecrnet_reside.md │ │ │ ├── albert_mnli.md │ │ │ ├── albert_squadv1.1.md │ │ │ ├── albert_sst2.md │ │ │ ├── alexnet_cifar10.md │ │ │ ├── alexnet_imagenet2012.md │ │ │ ├── alphapose_coco2017.md │ │ │ ├── apdrawinggan_generator_apdrawingdb.md │ │ │ ├── arbitrarystyletransfer_mscoco.md │ │ │ ├── arcface_ms1mv2.md │ │ │ ├── attentioncluster_fc1natt1_mnist.md │ │ │ ├── attentioncluster_fc1natt2_mnist.md │ │ │ ├── attentioncluster_fc1natt4_mnist.md │ │ │ ├── attentioncluster_fc1natt8_mnist.md │ │ │ ├── attentioncluster_fc2natt1_mnist.md │ │ │ ├── attentioncluster_fc2natt4_mnist.md │ │ │ ├── attentioncluster_fc2natt64_mnist.md │ │ │ ├── attentioncluster_fc2natt8_mnist.md │ │ │ ├── attgan_G_celeba.md │ │ │ ├── augvit_cifar10.md │ │ │ ├── autoaugment_cifar10.md │ │ │ ├── autodeeplab_0.5M_cityscapes.md │ │ │ ├── autodeeplab_1.5M_cityscapes.md │ │ │ ├── autodeeplab_1M_cityscapes.md │ │ │ ├── autodeeplab_cityscapes.md │ │ │ ├── autodis_criteo.md │ │ │ ├── autoslim_imagenet2012.md │ │ │ ├── avacifar_cifar10.md │ │ │ ├── avahpa_hpa.md │ │ │ ├── bertbase_cnnews128.md │ │ │ ├── bertbilstmcrf_ner.md │ │ │ ├── bertfinetuning_classifier_cola.md │ │ │ ├── bertfinetuning_nercrf_cluener.md │ │ │ ├── bertfinetuning_nersoftmax_cluener.md │ │ │ ├── bertfinetuning_squad_squadv1.1.md │ │ │ ├── bertlarge_cnnews128.md │ │ │ ├── bgcf_amazonbeauty.md │ │ │ ├── brdnet_waterloo.md │ │ │ ├── c3d_hmdb51.md │ │ │ ├── c3d_ucf101.md │ │ │ ├── cascadercnn_coco2017.md │ │ │ ├── cct_imagenet2012.md │ │ │ ├── centerface_widerface.md │ │ │ ├── centernet_coco2017.md │ │ │ ├── centernetdet_coco2017.md │ │ │ ├── centernetresnet101_coco2017.md │ │ │ ├── centernetresnet50v1_coco2017.md │ │ │ ├── cgan_G_mnist.md │ │ │ ├── cnnctc_mjstiiit.md │ │ │ ├── cnndirectionmodel_fsns.md │ │ │ ├── convnext_imagenet2012.md │ │ │ ├── crnn_synth.md │ │ │ ├── crnnseq2seqocr_fsns.md │ │ │ ├── csd_div2k.md │ │ │ ├── cspdarknet53_imagenet2012.md │ │ │ ├── ctcmodel_timit.md │ │ │ ├── ctpn_icdar2013.md │ │ │ ├── cyclegan_GA_apple2orange.md │ │ │ ├── cyclegan_GA_horse2zebra.md │ │ │ ├── cyclegan_GB_apple2orange.md │ │ │ ├── cyclegan_GB_horse2zebra.md │ │ │ ├── dam_douban.md │ │ │ ├── dam_ubuntu.md │ │ │ ├── dbpn_div2k.md │ │ │ ├── dbpngan_G_div2k.md │ │ │ ├── dcgan_imagenet2012.md │ │ │ ├── ddag_allresearch_sysumm01.md │ │ │ ├── ddag_indoorsearch_sysumm01.md │ │ │ ├── ddag_infraredtovisible_regdb.md │ │ │ ├── ddag_visibletoinfrared_regdb.md │ │ │ ├── ddrnet_imagenet2012.md │ │ │ ├── deepfm_criteo.md │ │ │ ├── deepid_youtubeface.md │ │ │ ├── deeplabv3plus_s16_voc2012.md │ │ │ ├── deeplabv3plus_s8r2_voc2012.md │ │ │ ├── deeplabv3s16_voc2012.md │ │ │ ├── deeplabv3s8r2_voc2012.md │ │ │ ├── deepsort_market1501.md │ │ │ ├── deeptext_icdar2013_scutforu_cocotextv2.md │ │ │ ├── delf_gldv2.md │ │ │ ├── delf_googlelandmarksdatasetv2.md │ │ │ ├── dem_att_cub.md │ │ │ ├── densenet121_imagenet2012.md │ │ │ ├── depthnet_coarsenet_nyu.md │ │ │ ├── depthnet_finenet_nyu.md │ │ │ ├── dgcn_citeseer.md │ │ │ ├── dgcn_cora.md │ │ │ ├── dgcn_pubmed.md │ │ │ ├── dgu_udc.md │ │ │ ├── dlinknet_DeepGlobeRoadExtractionDataset.md │ │ │ ├── dlrm_criteo.md │ │ │ ├── dncnn_bsd500.md │ │ │ ├── dpn_imagenet2012.md │ │ │ ├── drnet_classifier_imagenet2012.md │ │ │ ├── drnet_predictor_imagenet2012.md │ │ │ ├── dscnn_speechcommandsdatasetversion1.md │ │ │ ├── duconv_duconv.md │ │ │ ├── east_icdar2015.md │ │ │ ├── ecolite_ucf101.md │ │ │ ├── edcn_criteo.md │ │ │ ├── efficientnetb0_imagenet2012.md │ │ │ ├── efficientnetb1_imagenet2012.md │ │ │ ├── efficientnetb2_imagenet2012.md │ │ │ ├── efficientnetb3_imagenet2012.md │ │ │ ├── efficientnetv2_imagenet2012.md │ │ │ ├── egnet_resnet_dutstr.md │ │ │ ├── egnet_vgg_dutstr.md │ │ │ ├── emotect_baidu.md │ │ │ ├── enet_cityscapes.md │ │ │ ├── erfnet_cityscapes.md │ │ │ ├── ernie_bdqa.md │ │ │ ├── ernie_brcd.md │ │ │ ├── ernie_chnsenticorp.md │ │ │ ├── ernie_cmcr.md │ │ │ ├── ernie_msraner.md │ │ │ ├── ernie_xnli.md │ │ │ ├── esrgan_generator_div2k.md │ │ │ ├── faceattribute_fairface_rwmfd.md │ │ │ ├── facedetection_widerface.md │ │ │ ├── facequalityassessment_300wlp.md │ │ │ ├── facerecognition_ms1mv2.md │ │ │ ├── facerecognitionfortracking_lfw.md │ │ │ ├── fasterrcnndcn_coco2017.md │ │ │ ├── fasterrcnnresnetv1101_coco2017.md │ │ │ ├── fasterrcnnresnetv1152_coco2017.md │ │ │ ├── fasterrcnnresnetv150_coco2017.md │ │ │ ├── fastscnn_cityscapes.md │ │ │ ├── fastscnn_cityspaces.md │ │ │ ├── fasttext_agnews.md │ │ │ ├── fasttext_dbpedia.md │ │ │ ├── fasttext_yelp.md │ │ │ ├── fatdeepffm_criteo.md │ │ │ ├── fcanet_augmentedpascal.md │ │ │ ├── fcn4_musictagging.md │ │ │ ├── fcn8s_voc2012.md │ │ │ ├── fdabnn_cifar10.md │ │ │ ├── fishnet99_imagenet2012.md │ │ │ ├── gan_G_mnist.md │ │ │ ├── gat_citeseer.md │ │ │ ├── gat_cora.md │ │ │ ├── gcn_citesser.md │ │ │ ├── gcn_cora.md │ │ │ ├── genetres50_minux_imagenet2012.md │ │ │ ├── genetres50_plus_imagenet2012.md │ │ │ ├── genetres50_theta_imagenet2012.md │ │ │ ├── ghostnet_imagenet2012.md │ │ │ ├── ghostnetquant_imagenet2012.md │ │ │ ├── gnmtv2_wmtende.md │ │ │ ├── googlenet_cifar10.md │ │ │ ├── googlenet_imagenet2012.md │ │ │ ├── gpt3_openweb.md │ │ │ ├── gru_muti30k.md │ │ │ ├── hardnet_imagenet2012.md │ │ │ ├── hed_bsds500.md │ │ │ ├── hournas_cifar10.md │ │ │ ├── hrnet_cityscapes.md │ │ │ ├── hrnetw48cls_imagenet2012.md │ │ │ ├── hrnetw48seg_cityscapes.md │ │ │ ├── hypertext_iflytek.md │ │ │ ├── hypertext_tnews.md │ │ │ ├── ibnnet_imagenet2012.md │ │ │ ├── icnet_cityscapes.md │ │ │ ├── inceptionresnetv2_imagenet2012.md │ │ │ ├── inceptionv3_imagenet2012.md │ │ │ ├── inceptionv4_imagenet2012.md │ │ │ ├── ipt_denoise30_cbsd68.md │ │ │ ├── ipt_denoise50_cbsd68.md │ │ │ ├── ipt_derain_rain100l.md │ │ │ ├── ipt_x2_set5.md │ │ │ ├── ipt_x3_set5.md │ │ │ ├── ipt_x4_set5.md │ │ │ ├── irn_div2k.md │ │ │ ├── isynet_N0_imagenet2012.md │ │ │ ├── isynet_N1S1_imagenet2012.md │ │ │ ├── isynet_N1S2_imagenet2012.md │ │ │ ├── isynet_N1S3_imagenet2012.md │ │ │ ├── isynet_N1_imagenet2012.md │ │ │ ├── isynet_N2_imagenet2012.md │ │ │ ├── isynet_N3_imagenet2012.md │ │ │ ├── ktnet_record.md │ │ │ ├── ktnet_squad.md │ │ │ ├── learningtoseeinthedark_sony.md │ │ │ ├── lenet_mnist.md │ │ │ ├── lightcnn_msceleb1m.md │ │ │ ├── lpcnet_librispeech.md │ │ │ ├── lstm_aclimdbv1.md │ │ │ ├── lstmcrf_conll2000.md │ │ │ ├── luke_squad1.1.md │ │ │ ├── manidp_cifar10.md │ │ │ ├── maskrcnn_coco2017.md │ │ │ ├── maskrcnnmobilenetv1_coco2017.md │ │ │ ├── mass_newscrawl_gigaword_cornell.md │ │ │ ├── mcnn_shanghaitecha.md │ │ │ ├── melgan_ljspeech.md │ │ │ ├── metabaseline_miniimagenet.md │ │ │ ├── midas_redweb.md │ │ │ ├── mmoe_censusincome.md │ │ │ ├── mnasnet_imagenet2012.md │ │ │ ├── mobilenetv1_cifar10.md │ │ │ ├── mobilenetv1_imagenet2012.md │ │ │ ├── mobilenetv2_imagenet2012.md │ │ │ ├── mobilenetv3_imagenet2012.md │ │ │ ├── mobilenetv3large_imagenet2012.md │ │ │ ├── mobilenetv3small_imagenet2012.md │ │ │ ├── mobilenetv3smallx10_imagenet2012.md │ │ │ ├── moleculardynamics_water.md │ │ │ ├── multitasknet_veri.md │ │ │ ├── mvd_allresearch_sysumm01.md │ │ │ ├── mvd_indoorsearch_sysumm01.md │ │ │ ├── mvd_infraredtovisible_regdb.md │ │ │ ├── mvd_visibletoinfrared_regdb.md │ │ │ ├── naml_mindlarge.md │ │ │ ├── nasfpn_coco2017.md │ │ │ ├── nasnet_imagenet2012.md │ │ │ ├── ncf_movielens.md │ │ │ ├── neighbor2neighbor_kodak.md │ │ │ ├── nfnet_imagenet2012.md │ │ │ ├── nima_avadataset.md │ │ │ ├── ntsnet_cub2002011.md │ │ │ ├── ocrnet_cityscapes.md │ │ │ ├── octsqueeze_kitti.md │ │ │ ├── openpose_coco2017.md │ │ │ ├── opt_cococaption.md │ │ │ ├── osnet_dukemtmc.md │ │ │ ├── osnet_market1501.md │ │ │ ├── osnet_msmt17.md │ │ │ ├── pcb_market1501.md │ │ │ ├── pcbrpp_market1501.md │ │ │ ├── pdarts_cifar10.md │ │ │ ├── pgan_G_celeba.md │ │ │ ├── pix2pix_facades.md │ │ │ ├── pix2pix_maps.md │ │ │ ├── pnasnet_imagenet2012.md │ │ │ ├── pointnet2_modelnet40.md │ │ │ ├── pointnet_shapenet.md │ │ │ ├── poseestnet_veir.md │ │ │ ├── posenet_kingscollege.md │ │ │ ├── posenet_stmaryschurch.md │ │ │ ├── predrnnplusplus_movingmnist.md │ │ │ ├── protonet_omniglot.md │ │ │ ├── proxylessnas_imagenet2012.md │ │ │ ├── psenet_icdar2015.md │ │ │ ├── pspnet_ade20k.md │ │ │ ├── pspnet_voc2012.md │ │ │ ├── pvnet_ape_linemod.md │ │ │ ├── pvnet_cam_linemod.md │ │ │ ├── pvnet_can_linemod.md │ │ │ ├── pvnet_cat_linemod.md │ │ │ ├── pwcnet_flyingchairs.md │ │ │ ├── r2plus1d_ucf101.md │ │ │ ├── ras_dutstrain.md │ │ │ ├── rdn_div2k.md │ │ │ ├── rednet30_bsd300.md │ │ │ ├── refinedet_coco2017.md │ │ │ ├── refinenet_voc2012.md │ │ │ ├── relationnet_omniglot.md │ │ │ ├── renas_cifar10.md │ │ │ ├── res2net101_imagenet2012.md │ │ │ ├── res2net152_imagenet2012.md │ │ │ ├── res2net50_cifar10.md │ │ │ ├── res2net50_imagenet2012.md │ │ │ ├── res2netdeeplabv3_s16r1_voc2012.md │ │ │ ├── res2netdeeplabv3_s8r2_voc2012.md │ │ │ ├── res2netdeeplabv3_s8r2multiscale_voc2012.md │ │ │ ├── res2netdeeplabv3_s8r2multiscalefilp_voc2012.md │ │ │ ├── res2netfasterrcnn_coco2017.md │ │ │ ├── res2netyolov3_coco2017.md │ │ │ ├── resnest50_imagenet2012.md │ │ │ ├── resnet101_imagenet2012.md │ │ │ ├── resnet152_imagenet2012.md │ │ │ ├── resnet18_cifar10.md │ │ │ ├── resnet18_imagenet2012.md │ │ │ ├── resnet34_imagenet2012.md │ │ │ ├── resnet50_cifar10.md │ │ │ ├── resnet50_imagenet2012.md │ │ │ ├── resnet50_quadruplet_sop.md │ │ │ ├── resnet50_triplet_sop.md │ │ │ ├── resnet50advpruning_imagenet2012.md │ │ │ ├── resnet50bam_imagenet2012.md │ │ │ ├── resnet50quadruplet_sop.md │ │ │ ├── resnet50thor_imagenet2012.md │ │ │ ├── resnet50triplet_sop.md │ │ │ ├── resnetv2101_cifar10.md │ │ │ ├── resnetv2152_cifar10.md │ │ │ ├── resnetv250_cifar10.md │ │ │ ├── resnetv250frn_imagenet2012.md │ │ │ ├── resnext101_imagenet2012.md │ │ │ ├── resnext15264x4d_imagenet2012.md │ │ │ ├── resnext50_imagenet2012.md │ │ │ ├── retinafaceresnet50_widerface.md │ │ │ ├── retinanet_coco2017.md │ │ │ ├── retinanetresnet101_coco2017.md │ │ │ ├── rotate_wn18rr.md │ │ │ ├── sdne_wiki.md │ │ │ ├── semantichumanmatting_MattingHumanDatasets.md │ │ │ ├── senet_imagenet2012.md │ │ │ ├── senetresnet50_imagenet2012.md │ │ │ ├── seq2seq_wmt14.md │ │ │ ├── seres2net50_imagenet2012.md │ │ │ ├── seresnet50_imagenet2012.md │ │ │ ├── seresnext50_imagenet2012.md │ │ │ ├── shufflenetv1_imagenet2012.md │ │ │ ├── shufflenetv2_imagenet2012.md │ │ │ ├── siamfc_ilsvrc2015vid.md │ │ │ ├── siamrpn_vid.md │ │ │ ├── simclr_encoder_cifar10.md │ │ │ ├── simclr_linearclassifier_cifar10.md │ │ │ ├── simplebaselines_coco2017.md │ │ │ ├── simplepose_coco2017.md │ │ │ ├── singan_G_asinglenatureimage.md │ │ │ ├── singlepathnas_imagenet2012.md │ │ │ ├── skipgram_text8.md │ │ │ ├── sknet_cifar10.md │ │ │ ├── sphereface_casiawebface.md │ │ │ ├── sppnet_mult_imagenet2012.md │ │ │ ├── sppnet_single_imagenet2012.md │ │ │ ├── sppnet_zfnet_imagenet2012.md │ │ │ ├── squeezenet11_imagenet2012.md │ │ │ ├── squeezenet_cifar10.md │ │ │ ├── squeezenet_imagenet2012.md │ │ │ ├── squeezenetresidual_cifar10.md │ │ │ ├── squeezenetresidual_imagenet2012.md │ │ │ ├── srcnn_ilsvrc2013.md │ │ │ ├── srgan_div2k.md │ │ │ ├── ssd300_coco2017.md │ │ │ ├── ssdghostnet_coco2017.md │ │ │ ├── ssdinceptionv2_coco2014mini.md │ │ │ ├── ssdmobilenetv1fpn_coco2017.md │ │ │ ├── ssdmobilenetv2_coco2017.md │ │ │ ├── ssdmobilenetv2fpnlite_coco2017.md │ │ │ ├── ssdresnet34_coco2017.md │ │ │ ├── ssdresnet50_coco2017.md │ │ │ ├── ssdresnet50fpn_coco2017.md │ │ │ ├── ssdvgg16_coco2017.md │ │ │ ├── ssimae_mvtecadbottle.md │ │ │ ├── ssimae_mvtecadcable.md │ │ │ ├── ssimae_mvtecadcapsule.md │ │ │ ├── ssimae_mvtecadcarpet.md │ │ │ ├── ssimae_mvtecadgrid.md │ │ │ ├── ssimae_mvtecadmetalnut.md │ │ │ ├── stackedhourglass_mpii.md │ │ │ ├── stargan_G_celeba.md │ │ │ ├── stgan_celeba.md │ │ │ ├── stgcn_cheb15_pemsd7m.md │ │ │ ├── stgcn_cheb30_pemsd7m.md │ │ │ ├── stgcn_cheb45_pemsd7m.md │ │ │ ├── stgcn_gcn15_pemsd7m.md │ │ │ ├── stgcn_gcn30_pemsd7m.md │ │ │ ├── stgcn_gcn45_pemsd7m.md │ │ │ ├── stnet_kinetics400.md │ │ │ ├── stpm_mvtecadbottle.md │ │ │ ├── stpm_mvtecadcable.md │ │ │ ├── stpm_mvtecadcapsule.md │ │ │ ├── stpm_mvtecadcarpet.md │ │ │ ├── stpm_mvtecadgrid.md │ │ │ ├── stpm_mvtecadhazelnut.md │ │ │ ├── stpm_mvtecadleather.md │ │ │ ├── stpm_mvtecadmetalnut.md │ │ │ ├── stpm_mvtecadpill.md │ │ │ ├── stpm_mvtecadscrew.md │ │ │ ├── stpm_mvtecadtile.md │ │ │ ├── stpm_mvtecadtoothbrush.md │ │ │ ├── stpm_mvtecadtransistor.md │ │ │ ├── stpm_mvtecadwood.md │ │ │ ├── stpm_mvtecadzipper.md │ │ │ ├── swintransformer_imagenet2012.md │ │ │ ├── tacotron2_ljspeech1.1.md │ │ │ ├── tall_tacos.md │ │ │ ├── tbnet_steam.md │ │ │ ├── tcn_addingproblem.md │ │ │ ├── tcn_permutedmnist.md │ │ │ ├── textcnn_moviereview.md │ │ │ ├── textcnn_sst2.md │ │ │ ├── textcnn_subj.md │ │ │ ├── textcrnn_gru_polarity_moviereview.md │ │ │ ├── textcrnn_lstm_polarity_moviereview.md │ │ │ ├── textfusenet_totaltext.md │ │ │ ├── tgcn_losloop.md │ │ │ ├── tgcn_sztaxi.md │ │ │ ├── tinybert_enwiki128_mnli.md │ │ │ ├── tinybert_enwiki128_qnli.md │ │ │ ├── tinybert_enwiki128_sst2.md │ │ │ ├── tinydarknet_imagenet2012.md │ │ │ ├── tnt_imagenet2012.md │ │ │ ├── tprr_readeralbert_hotpotqa.md │ │ │ ├── tprr_readerdownstream_hotpotqa.md │ │ │ ├── tprr_rerankalbert_hotpotqa.md │ │ │ ├── tprr_rerankdownstream_hotpotqa.md │ │ │ ├── transformer_large_wmt.md │ │ │ ├── transformer_wmt.md │ │ │ ├── tsm_somethingsomethingv2.md │ │ │ ├── u2net_dutstr.md │ │ │ ├── ugatit_selfie2anime.md │ │ │ ├── unet2d_isbichallenge.md │ │ │ ├── unet3d_luna16.md │ │ │ ├── unet3plus_lits2017.md │ │ │ ├── unet_medical_isbi.md │ │ │ ├── unetnested_cellnuclei.md │ │ │ ├── vehiclenet_vehiclenetdataset.md │ │ │ ├── vgg16_bn_imagenet2012.md │ │ │ ├── vgg16_cifar10.md │ │ │ ├── vgg16_imagenet2012.md │ │ │ ├── vgg19_cifar10.md │ │ │ ├── vgg19_imagenet2012.md │ │ │ ├── vit_imagenet2012.md │ │ │ ├── vitbase_cifar10.md │ │ │ ├── vnet_promise2012.md │ │ │ ├── warpctc_captcha.md │ │ │ ├── wavemlp_imagenet2012.md │ │ │ ├── wdsr_div2k.md │ │ │ ├── wgan_batchnorm_G_lsunbedrooms.md │ │ │ ├── wgan_nobatchnorm_G_lsunbedrooms.md │ │ │ ├── wideanddeep_criteo.md │ │ │ ├── wideanddeep_dynamicshape_criteo.md │ │ │ ├── wideanddeep_hostdevice_criteo.md │ │ │ ├── wideanddeep_ps_criteo.md │ │ │ ├── wideanddeepmultitable_outbrain.md │ │ │ ├── wideresnet_cifar10.md │ │ │ ├── xception_imagenet2012.md │ │ │ ├── yolov3darknet53_coco2014.md │ │ │ ├── yolov3darknet53shape416_coco2014.md │ │ │ ├── yolov3resnet18_coco2017.md │ │ │ ├── yolov3tiny_coco2017.md │ │ │ ├── yolov4_coco2017.md │ │ │ ├── yolov4shape416_coco2017.md │ │ │ ├── yolov5shape640_coco2017.md │ │ │ └── yolox_coco2017.md │ │ ├── 1.8 │ │ │ ├── 3dcnn_brast2017.md │ │ │ ├── 3ddensenet_iseg2017.md │ │ │ ├── adnet_vot2013vot2014.md │ │ │ ├── advancedeast_icpr2018.md │ │ │ ├── aecrnet_reside.md │ │ │ ├── albert_mnli.md │ │ │ ├── albert_squadv1.1.md │ │ │ ├── albert_sst2.md │ │ │ ├── alexnet_cifar10.md │ │ │ ├── alexnet_imagenet2012.md │ │ │ ├── alphapose_coco2017.md │ │ │ ├── apdrawinggan_generator_apdrawingdb.md │ │ │ ├── arbitrarystyletransfer_mscoco.md │ │ │ ├── arcface_ms1mv2.md │ │ │ ├── attentioncluster_fc1natt1_mnist.md │ │ │ ├── attentioncluster_fc1natt2_mnist.md │ │ │ ├── attentioncluster_fc1natt4_mnist.md │ │ │ ├── attentioncluster_fc1natt8_mnist.md │ │ │ ├── attentioncluster_fc2natt1_mnist.md │ │ │ ├── attentioncluster_fc2natt4_mnist.md │ │ │ ├── attentioncluster_fc2natt64_mnist.md │ │ │ ├── attentioncluster_fc2natt8_mnist.md │ │ │ ├── attgan_Generator_celeba.md │ │ │ ├── augvit_cifar10.md │ │ │ ├── autoaugment_cifar10.md │ │ │ ├── autodeeplab_0.5M_cityscapes.md │ │ │ ├── autodeeplab_1.5M_cityscapes.md │ │ │ ├── autodeeplab_1M_cityscapes.md │ │ │ ├── autodeeplab_cityscapes.md │ │ │ ├── autodis_criteo.md │ │ │ ├── autoslim_imagenet2012.md │ │ │ ├── avacifar_cifar10.md │ │ │ ├── avahpa_hpa.md │ │ │ ├── bertbase_cnnews128.md │ │ │ ├── bertfinetune_15classifier_tnews.md │ │ │ ├── bertfinetune_bilstmcrf_chinesener.md │ │ │ ├── bertfinetune_crf_cluener.md │ │ │ ├── bertfinetune_ner_cluener.md │ │ │ ├── bertfinetune_squad_squad.md │ │ │ ├── bertlarge_boost_enwiki512.md │ │ │ ├── bertlarge_cnnews128.md │ │ │ ├── bertthor_mlperf.md │ │ │ ├── bgcf_amazonbeauty.md │ │ │ ├── brdnet_waterloo.md │ │ │ ├── c3d_hmdb51.md │ │ │ ├── c3d_ucf101.md │ │ │ ├── cascadercnn_coco2017.md │ │ │ ├── cct_imagenet2012.md │ │ │ ├── centerface_widerface.md │ │ │ ├── centernet_coco2017.md │ │ │ ├── centernetdet_coco2017.md │ │ │ ├── centernetresnet101_coco2017.md │ │ │ ├── centernetresnet50v1_coco2017.md │ │ │ ├── cgan_G_mnist.md │ │ │ ├── cnnctc_mjstiiit.md │ │ │ ├── cnndirectionmodel_fsns.md │ │ │ ├── convnext_imagenet2012.md │ │ │ ├── crnn_crnnds.md │ │ │ ├── crnnseq2seqocr_fsns.md │ │ │ ├── csd_div2k.md │ │ │ ├── cspdarknet53_imagenet2012.md │ │ │ ├── ctcmodel_timit.md │ │ │ ├── ctpn_finetune_icdar2013.md │ │ │ ├── ctpn_pretrain_icdar2013.md │ │ │ ├── cyclegan_GA_apple2orange.md │ │ │ ├── cyclegan_GA_horse2zebra.md │ │ │ ├── cyclegan_GB_apple2orange.md │ │ │ ├── cyclegan_GB_horse2zebra.md │ │ │ ├── dam_douban.md │ │ │ ├── dam_ubuntu.md │ │ │ ├── dbpn_div2k.md │ │ │ ├── dbpngan_G_div2k.md │ │ │ ├── dbpngan_Generator_div2k.md │ │ │ ├── dcgan_imagenet2012.md │ │ │ ├── ddag_allresearch_sysumm01.md │ │ │ ├── ddag_indoorsearch_sysumm01.md │ │ │ ├── ddag_infraredtovisible_regdb.md │ │ │ ├── ddag_visibletoinfrared_regdb.md │ │ │ ├── ddrnet_imagenet2012.md │ │ │ ├── deepfm_criteo.md │ │ │ ├── deepid_youtubeface.md │ │ │ ├── deeplabv3plus_s16_voc2012.md │ │ │ ├── deeplabv3plus_s8r1_voc2012.md │ │ │ ├── deeplabv3plus_s8r2_voc2012.md │ │ │ ├── deeplabv3s16_voc2012.md │ │ │ ├── deeplabv3s8r2_voc2012.md │ │ │ ├── deepsort_market1501.md │ │ │ ├── deeptext_icdar2013.md │ │ │ ├── delf_gldv2.md │ │ │ ├── delf_googlelandmarksdatasetv2.md │ │ │ ├── dem_att_cub.md │ │ │ ├── dem_cub.md │ │ │ ├── densenet121_imagenet2012.md │ │ │ ├── depthnet_coarsenet_nyu.md │ │ │ ├── depthnet_finenet_nyu.md │ │ │ ├── dgcn_citeseer.md │ │ │ ├── dgcn_cora.md │ │ │ ├── dgcn_pubmed.md │ │ │ ├── dgu_udc.md │ │ │ ├── dlinknet_DeepGlobeRoadExtractionDataset.md │ │ │ ├── dlrm_criteo.md │ │ │ ├── dncnn_bsd500.md │ │ │ ├── dpn_imagenet2012.md │ │ │ ├── drnet_classifier_imagenet2012.md │ │ │ ├── drnet_predictor_imagenet2012.md │ │ │ ├── dscnn_speechcommandsdatasetversion1.md │ │ │ ├── duconv_duconv.md │ │ │ ├── east_icdar2015.md │ │ │ ├── ecapatdnn_voxceleb.md │ │ │ ├── ecolite_ucf101.md │ │ │ ├── edcn_criteo.md │ │ │ ├── efficientdetd0_coco2017.md │ │ │ ├── efficientnetb0_imagenet2012.md │ │ │ ├── efficientnetb1_imagenet2012.md │ │ │ ├── efficientnetb2_imagenet2012.md │ │ │ ├── efficientnetb3_imagenet2012.md │ │ │ ├── efficientnetv2_imagenet2012.md │ │ │ ├── egnet_resnet_dutstr.md │ │ │ ├── egnet_vgg_dutstr.md │ │ │ ├── emotect_baidu.md │ │ │ ├── enet_cityscapes.md │ │ │ ├── erfnet_cityscapes.md │ │ │ ├── ernie_bdqa.md │ │ │ ├── ernie_brcd.md │ │ │ ├── ernie_chnsenticorp.md │ │ │ ├── ernie_cmcr.md │ │ │ ├── ernie_msraner.md │ │ │ ├── ernie_xnli.md │ │ │ ├── esrgan_generator_div2k.md │ │ │ ├── faceattribute_fairface_rwmfd.md │ │ │ ├── facedetection_widerface.md │ │ │ ├── facequalityassessment_300wlp.md │ │ │ ├── facerecognition_ms1mv2.md │ │ │ ├── facerecognitionfortracking_lfw.md │ │ │ ├── fasterrcnndcn_coco2017.md │ │ │ ├── fasterrcnnresnetv1101_coco2017.md │ │ │ ├── fasterrcnnresnetv1152_coco2017.md │ │ │ ├── fasterrcnnresnetv150_coco2017.md │ │ │ ├── fastscnn_cityscapes.md │ │ │ ├── fastscnn_cityspaces.md │ │ │ ├── fastspeech_ljspeech11.md │ │ │ ├── fasttext_agnews.md │ │ │ ├── fasttext_dbpedia.md │ │ │ ├── fasttext_yelp.md │ │ │ ├── fatdeepffm_criteo.md │ │ │ ├── fcanet_augmentedpascal.md │ │ │ ├── fcn4_musictagging.md │ │ │ ├── fcn8s_voc2012.md │ │ │ ├── fdabnn_cifar10.md │ │ │ ├── fishnet99_imagenet2012.md │ │ │ ├── gan_G_mnist.md │ │ │ ├── gat_citeseer.md │ │ │ ├── gat_cora.md │ │ │ ├── gcn_citesser.md │ │ │ ├── gcn_cora.md │ │ │ ├── genetres50_minux_imagenet2012.md │ │ │ ├── genetres50_plus_imagenet2012.md │ │ │ ├── genetres50_theta_imagenet2012.md │ │ │ ├── ghostnet_imagenet2012.md │ │ │ ├── ghostnetquant_imagenet2012.md │ │ │ ├── gnmtv2_wmtende.md │ │ │ ├── googlenet_cifar10.md │ │ │ ├── googlenet_imagenet2012.md │ │ │ ├── gpt3_openweb.md │ │ │ ├── gru_muti30k.md │ │ │ ├── hardnet_imagenet2012.md │ │ │ ├── hed_bsds500.md │ │ │ ├── hournas_cifar10.md │ │ │ ├── hrnet_cityscapes.md │ │ │ ├── hrnetw48cls_imagenet2012.md │ │ │ ├── hrnetw48seg_cityscapes.md │ │ │ ├── hypertext_iflytek.md │ │ │ ├── hypertext_tnews.md │ │ │ ├── ibnnet_imagenet2012.md │ │ │ ├── icnet_cityscapes.md │ │ │ ├── inceptionresnetv2_imagenet2012.md │ │ │ ├── inceptionv3_imagenet2012.md │ │ │ ├── inceptionv4_imagenet2012.md │ │ │ ├── ipt_denoise30_cbsd68.md │ │ │ ├── ipt_denoise50_cbsd68.md │ │ │ ├── ipt_derain_rain100l.md │ │ │ ├── ipt_x2_set5.md │ │ │ ├── ipt_x3_set5.md │ │ │ ├── ipt_x4_set5.md │ │ │ ├── irn_div2k.md │ │ │ ├── isynet_N0_imagenet2012.md │ │ │ ├── isynet_N1S1_imagenet2012.md │ │ │ ├── isynet_N1S2_imagenet2012.md │ │ │ ├── isynet_N1S3_imagenet2012.md │ │ │ ├── isynet_N1_imagenet2012.md │ │ │ ├── isynet_N2_imagenet2012.md │ │ │ ├── isynet_N3_imagenet2012.md │ │ │ ├── ktnet_record.md │ │ │ ├── ktnet_squad.md │ │ │ ├── learningtoseeinthedark_sony.md │ │ │ ├── lenet_mnist.md │ │ │ ├── lightcnn_msceleb1m.md │ │ │ ├── lpcnet_librispeech.md │ │ │ ├── lstm_aclimdbv1.md │ │ │ ├── lstmcrf_conll2000.md │ │ │ ├── luke_squad1.1.md │ │ │ ├── manidp_cifar10.md │ │ │ ├── maskrcnn_coco2017.md │ │ │ ├── maskrcnnmobilenetv1_coco2017.md │ │ │ ├── mass_newscrawl_gigaword_cornell.md │ │ │ ├── mcnn_shanghaitecha.md │ │ │ ├── melgan_Generator_ljspeech.md │ │ │ ├── metabaseline_miniimagenet.md │ │ │ ├── midas_redweb.md │ │ │ ├── mmoe_censusincome.md │ │ │ ├── mnasnet_imagenet2012.md │ │ │ ├── mobilenetv1_cifar10.md │ │ │ ├── mobilenetv1_imagenet2012.md │ │ │ ├── mobilenetv2_imagenet2012.md │ │ │ ├── mobilenetv3_imagenet2012.md │ │ │ ├── mobilenetv3large_imagenet2012.md │ │ │ ├── mobilenetv3small_imagenet2012.md │ │ │ ├── mobilenetv3smallx10_imagenet2012.md │ │ │ ├── moleculardynamics_water.md │ │ │ ├── multitasknet_veri.md │ │ │ ├── mvd_allresearch_sysumm01.md │ │ │ ├── mvd_indoorsearch_sysumm01.md │ │ │ ├── mvd_infraredtovisible_regdb.md │ │ │ ├── mvd_visibletoinfrared_regdb.md │ │ │ ├── naml_mindlarge.md │ │ │ ├── nasfpn_coco2017.md │ │ │ ├── nasnet_imagenet2012.md │ │ │ ├── ncf_movielens.md │ │ │ ├── neighbor2neighbor_kodak.md │ │ │ ├── nfnet_imagenet2012.md │ │ │ ├── nima_avadataset.md │ │ │ ├── ntsnet_cub2002011.md │ │ │ ├── ocrnet_cityscapes.md │ │ │ ├── octsqueeze_kitti.md │ │ │ ├── openpose_coco2017.md │ │ │ ├── opt_cococaption.md │ │ │ ├── osnet_dukemtmcreid.md │ │ │ ├── osnet_market1501.md │ │ │ ├── osnet_msmt17.md │ │ │ ├── patchcore_mvtecad.md │ │ │ ├── pcb_market1501.md │ │ │ ├── pcbrpp_market1501.md │ │ │ ├── pdarts_cifar10.md │ │ │ ├── pgan_G_celeba.md │ │ │ ├── pix2pix_facades.md │ │ │ ├── pix2pix_maps.md │ │ │ ├── pnasnet_imagenet2012.md │ │ │ ├── pointnet2_modelnet40.md │ │ │ ├── pointnet_shapenet.md │ │ │ ├── poseestnet_veir.md │ │ │ ├── posenet_kingscollege.md │ │ │ ├── posenet_stmaryschurch.md │ │ │ ├── predrnnplusplus_movingmnist.md │ │ │ ├── protonet_omniglot.md │ │ │ ├── proxylessnas_imagenet2012.md │ │ │ ├── psenet_icdar2015.md │ │ │ ├── pspnet_ade20k.md │ │ │ ├── pspnet_voc2012.md │ │ │ ├── pvnet_ape_linemod.md │ │ │ ├── pvnet_cam_linemod.md │ │ │ ├── pvnet_can_linemod.md │ │ │ ├── pvnet_cat_linemod.md │ │ │ ├── pwcnet_flyingchairs.md │ │ │ ├── r2plus1d_ucf101.md │ │ │ ├── ras_dutstrain.md │ │ │ ├── rcan_div2k.md │ │ │ ├── rdn_div2k.md │ │ │ ├── rednet30_bsd300.md │ │ │ ├── refinedet_coco2017.md │ │ │ ├── refinenet_voc2012.md │ │ │ ├── relationnet_omniglot.md │ │ │ ├── renas_cifar10.md │ │ │ ├── repvgg_imagenet2012.md │ │ │ ├── res2net101_imagenet2012.md │ │ │ ├── res2net152_imagenet2012.md │ │ │ ├── res2net50_cifar10.md │ │ │ ├── res2net50_imagenet2012.md │ │ │ ├── res2netdeeplabv3_s16r1_voc2012.md │ │ │ ├── res2netdeeplabv3_s8r2_voc2012.md │ │ │ ├── res2netdeeplabv3_s8r2multiscale_voc2012.md │ │ │ ├── res2netdeeplabv3_s8r2multiscalefilp_voc2012.md │ │ │ ├── res2netfasterrcnn_coco2017.md │ │ │ ├── res2netyolov3_coco2017.md │ │ │ ├── resnest50_imagenet2012.md │ │ │ ├── resnet101_imagenet2012.md │ │ │ ├── resnet152_imagenet2012.md │ │ │ ├── resnet18_cifar10.md │ │ │ ├── resnet18_imagenet2012.md │ │ │ ├── resnet34_imagenet2012.md │ │ │ ├── resnet50_cifar10.md │ │ │ ├── resnet50_imagenet2012.md │ │ │ ├── resnet50_quadruplet_sop.md │ │ │ ├── resnet50_triplet_sop.md │ │ │ ├── resnet50advpruning_imagenet2012.md │ │ │ ├── resnet50bam_imagenet2012.md │ │ │ ├── resnet50quadruplet_sop.md │ │ │ ├── resnet50thor_imagenet2012.md │ │ │ ├── resnet50triplet_sop.md │ │ │ ├── resnetv2101_cifar10.md │ │ │ ├── resnetv2152_cifar10.md │ │ │ ├── resnetv250_cifar10.md │ │ │ ├── resnetv250frn_imagenet2012.md │ │ │ ├── resnext101_imagenet2012.md │ │ │ ├── resnext15264x4d_imagenet2012.md │ │ │ ├── resnext50_imagenet2012.md │ │ │ ├── retinafaceresnet50_widerface.md │ │ │ ├── retinanet_coco2017.md │ │ │ ├── retinanetresnet101_coco2017.md │ │ │ ├── rotate_wn18rr.md │ │ │ ├── sdne_wiki.md │ │ │ ├── semantichumanmatting_MattingHumanDatasets.md │ │ │ ├── semantichumanmatting_mattinghumandatasets.md │ │ │ ├── senet_imagenet2012.md │ │ │ ├── senetresnet50_imagenet2012.md │ │ │ ├── seq2seq_wmt14.md │ │ │ ├── seres2net50_imagenet2012.md │ │ │ ├── seresnet50_imagenet2012.md │ │ │ ├── seresnext50_imagenet2012.md │ │ │ ├── shufflenetv1_imagenet2012.md │ │ │ ├── shufflenetv2_imagenet2012.md │ │ │ ├── siamfc_ilsvrc2015vid.md │ │ │ ├── siamrpn_vid.md │ │ │ ├── simclr_encoder_cifar10.md │ │ │ ├── simclr_linearclassifier_cifar10.md │ │ │ ├── simplebaselines_coco2017.md │ │ │ ├── simplepose_coco2017.md │ │ │ ├── singan_G_asinglenatureimage.md │ │ │ ├── singlepathnas_imagenet2012.md │ │ │ ├── skipgram_text8.md │ │ │ ├── sknet_cifar10.md │ │ │ ├── slowfast_ava.md │ │ │ ├── snnmlp_t_imagenet2012.md │ │ │ ├── sphereface_casiawebface.md │ │ │ ├── sppnet_mult_imagenet2012.md │ │ │ ├── sppnet_single_imagenet2012.md │ │ │ ├── sppnet_zfnet_imagenet2012.md │ │ │ ├── squeezenet11_imagenet2012.md │ │ │ ├── squeezenet_cifar10.md │ │ │ ├── squeezenet_imagenet2012.md │ │ │ ├── squeezenetresidual_cifar10.md │ │ │ ├── squeezenetresidual_imagenet2012.md │ │ │ ├── srcnn_ilsvrc2013.md │ │ │ ├── srgan_div2k.md │ │ │ ├── ssd300_coco2017.md │ │ │ ├── ssdghostnet_coco2017.md │ │ │ ├── ssdinceptionv2_coco2014mini.md │ │ │ ├── ssdmobilenetv1fpn_coco2017.md │ │ │ ├── ssdmobilenetv2_coco2017.md │ │ │ ├── ssdmobilenetv2fpnlite_coco2017.md │ │ │ ├── ssdresnet34_coco2017.md │ │ │ ├── ssdresnet50_coco2017.md │ │ │ ├── ssdresnet50fpn_coco2017.md │ │ │ ├── ssdvgg16_coco2017.md │ │ │ ├── ssimae_mvtecadbottle.md │ │ │ ├── ssimae_mvtecadcable.md │ │ │ ├── ssimae_mvtecadcapsule.md │ │ │ ├── ssimae_mvtecadcarpet.md │ │ │ ├── ssimae_mvtecadgrid.md │ │ │ ├── ssimae_mvtecadmetalnut.md │ │ │ ├── stackedhourglass_mpii.md │ │ │ ├── stargan_G_celeba.md │ │ │ ├── stgan_celeba.md │ │ │ ├── stgcn_cheb15_pemsd7m.md │ │ │ ├── stgcn_cheb30_pemsd7m.md │ │ │ ├── stgcn_cheb45_pemsd7m.md │ │ │ ├── stgcn_gcn15_pemsd7m.md │ │ │ ├── stgcn_gcn30_pemsd7m.md │ │ │ ├── stgcn_gcn45_pemsd7m.md │ │ │ ├── stnet_kinetics400.md │ │ │ ├── stpm_mvtecadbottle.md │ │ │ ├── stpm_mvtecadcable.md │ │ │ ├── stpm_mvtecadcapsule.md │ │ │ ├── stpm_mvtecadcarpet.md │ │ │ ├── stpm_mvtecadgrid.md │ │ │ ├── stpm_mvtecadhazelnut.md │ │ │ ├── stpm_mvtecadleather.md │ │ │ ├── stpm_mvtecadmetalnut.md │ │ │ ├── stpm_mvtecadpill.md │ │ │ ├── stpm_mvtecadscrew.md │ │ │ ├── stpm_mvtecadtile.md │ │ │ ├── stpm_mvtecadtoothbrush.md │ │ │ ├── stpm_mvtecadtransistor.md │ │ │ ├── stpm_mvtecadwood.md │ │ │ ├── stpm_mvtecadzipper.md │ │ │ ├── swintransformer_imagenet2012.md │ │ │ ├── tacotron2_ljspeech1.1.md │ │ │ ├── tacotron2_ljspeech11.md │ │ │ ├── tall_tacos.md │ │ │ ├── tbnet_steam.md │ │ │ ├── tcn_addingproblem.md │ │ │ ├── tcn_permutedmnist.md │ │ │ ├── textcnn_moviereview.md │ │ │ ├── textcnn_sst2.md │ │ │ ├── textcnn_subj.md │ │ │ ├── textcrnn_gru_polarity_moviereview.md │ │ │ ├── textcrnn_lstm_polarity_moviereview.md │ │ │ ├── textfusenet_totaltext.md │ │ │ ├── tgcn_losloop.md │ │ │ ├── tgcn_sztaxi.md │ │ │ ├── tinybert_enwiki128_mnli.md │ │ │ ├── tinybert_enwiki128_qnli.md │ │ │ ├── tinybert_enwiki128_sst2.md │ │ │ ├── tinydarknet_imagenet2012.md │ │ │ ├── tnt_imagenet2012.md │ │ │ ├── tokenfusion_nyudv2.md │ │ │ ├── tprr_readeralbert_hotpotqa.md │ │ │ ├── tprr_readerdownstream_hotpotqa.md │ │ │ ├── tprr_rerankalbert_hotpotqa.md │ │ │ ├── tprr_rerankdownstream_hotpotqa.md │ │ │ ├── transformer_large_wmt.md │ │ │ ├── transformer_wmt.md │ │ │ ├── tsm_somethingsomethingv2.md │ │ │ ├── u2net_dutstr.md │ │ │ ├── ugatit_selfie2anime.md │ │ │ ├── unet2d_isbichallenge.md │ │ │ ├── unet3d_luna16.md │ │ │ ├── unet3plus_lits2017.md │ │ │ ├── unet_medical_isbi.md │ │ │ ├── unetnested_cellnuclei.md │ │ │ ├── vehiclenet_vehiclenetdataset.md │ │ │ ├── vgg16_bn_imagenet2012.md │ │ │ ├── vgg16_cifar10.md │ │ │ ├── vgg16_imagenet2012.md │ │ │ ├── vgg19_cifar10.md │ │ │ ├── vgg19_imagenet2012.md │ │ │ ├── vit_imagenet2012.md │ │ │ ├── vitbase_cifar10.md │ │ │ ├── vnet_promise2012.md │ │ │ ├── warpctc_captcha.md │ │ │ ├── wavemlp_imagenet2012.md │ │ │ ├── wdsr_div2k.md │ │ │ ├── wgan_batchnorm_G_lsunbedrooms.md │ │ │ ├── wgan_nobatchnorm_G_lsunbedrooms.md │ │ │ ├── wideanddeep_criteo.md │ │ │ ├── wideanddeep_dynamicshape_criteo.md │ │ │ ├── wideanddeep_hostdevice_criteo.md │ │ │ ├── wideanddeep_ps_criteo.md │ │ │ ├── wideanddeepmultitable_outbrain.md │ │ │ ├── wideresnet_cifar10.md │ │ │ ├── xception_imagenet2012.md │ │ │ ├── yolov3darknet53_coco2014.md │ │ │ ├── yolov3darknet53shape416_coco2014.md │ │ │ ├── yolov3resnet18_coco2017.md │ │ │ ├── yolov3tiny_coco2017.md │ │ │ ├── yolov4_coco2017.md │ │ │ ├── yolov4shape416_coco2017.md │ │ │ ├── yolov5s_coco2017.md │ │ │ ├── yolox_coco2017.md │ │ │ └── yolox_darknet53_coco2017.md │ │ └── 1.9 │ │ │ ├── 3dcnn_brast2017.md │ │ │ ├── 3ddensenet_iseg2017.md │ │ │ ├── adnet_vot2013vot2014.md │ │ │ ├── advancedeast_icpr2018.md │ │ │ ├── aecrnet_reside.md │ │ │ ├── albert_mnli.md │ │ │ ├── albert_squadv1.1.md │ │ │ ├── albert_sst2.md │ │ │ ├── alexnet_cifar10.md │ │ │ ├── alexnet_imagenet2012.md │ │ │ ├── alphapose_coco2017.md │ │ │ ├── apdrawinggan_generator_apdrawingdb.md │ │ │ ├── arbitrarystyletransfer_mscoco.md │ │ │ ├── arcface_ms1mv2.md │ │ │ ├── attentioncluster_fc1natt1_mnist.md │ │ │ ├── attentioncluster_fc1natt2_mnist.md │ │ │ ├── attentioncluster_fc1natt4_mnist.md │ │ │ ├── attentioncluster_fc1natt8_mnist.md │ │ │ ├── attentioncluster_fc2natt1_mnist.md │ │ │ ├── attentioncluster_fc2natt4_mnist.md │ │ │ ├── attentioncluster_fc2natt64_mnist.md │ │ │ ├── attentioncluster_fc2natt8_mnist.md │ │ │ ├── attgan_Generator_celeba.md │ │ │ ├── augvit_cifar10.md │ │ │ ├── autoaugment_cifar10.md │ │ │ ├── autodeeplab_0.5M_cityscapes.md │ │ │ ├── autodeeplab_1.5M_cityscapes.md │ │ │ ├── autodeeplab_1M_cityscapes.md │ │ │ ├── autodeeplab_cityscapes.md │ │ │ ├── autodis_criteo.md │ │ │ ├── autoslim_imagenet2012.md │ │ │ ├── avacifar_cifar10.md │ │ │ ├── avahpa_hpa.md │ │ │ ├── bertbase_cnnews128.md │ │ │ ├── bertfinetune_15classifier_tnews.md │ │ │ ├── bertfinetune_bilstmcrf_chinesener.md │ │ │ ├── bertfinetune_crf_cluener.md │ │ │ ├── bertfinetune_ner_cluener.md │ │ │ ├── bertfinetune_squad_squad.md │ │ │ ├── bertlarge_boost_enwiki512.md │ │ │ ├── bertlarge_cnnews128.md │ │ │ ├── bertthor_mlperf.md │ │ │ ├── bgcf_amazonbeauty.md │ │ │ ├── brdnet_waterloo.md │ │ │ ├── c3d_hmdb51.md │ │ │ ├── c3d_ucf101.md │ │ │ ├── cascadercnn_coco2017.md │ │ │ ├── cct_imagenet2012.md │ │ │ ├── centerface_widerface.md │ │ │ ├── centernet_coco2017.md │ │ │ ├── centernetdet_coco2017.md │ │ │ ├── centernetresnet101_coco2017.md │ │ │ ├── centernetresnet50v1_coco2017.md │ │ │ ├── cgan_G_mnist.md │ │ │ ├── cnnctc_mjstiiit.md │ │ │ ├── cnndirectionmodel_fsns.md │ │ │ ├── convnext_imagenet2012.md │ │ │ ├── crnn_crnnds.md │ │ │ ├── crnnseq2seqocr_fsns.md │ │ │ ├── csd_div2k.md │ │ │ ├── cspdarknet53_imagenet2012.md │ │ │ ├── ctcmodel_timit.md │ │ │ ├── ctpn_finetune_icdar2013.md │ │ │ ├── ctpn_pretrain_icdar2013.md │ │ │ ├── cyclegan_GA_apple2orange.md │ │ │ ├── cyclegan_GA_horse2zebra.md │ │ │ ├── cyclegan_GB_apple2orange.md │ │ │ ├── cyclegan_GB_horse2zebra.md │ │ │ ├── dam_douban.md │ │ │ ├── dam_ubuntu.md │ │ │ ├── dbpn_div2k.md │ │ │ ├── dbpngan_G_div2k.md │ │ │ ├── dbpngan_Generator_div2k.md │ │ │ ├── dcgan_imagenet2012.md │ │ │ ├── ddag_allresearch_sysumm01.md │ │ │ ├── ddag_indoorsearch_sysumm01.md │ │ │ ├── ddag_infraredtovisible_regdb.md │ │ │ ├── ddag_visibletoinfrared_regdb.md │ │ │ ├── ddrnet_imagenet2012.md │ │ │ ├── deepfm_criteo.md │ │ │ ├── deepid_youtubeface.md │ │ │ ├── deeplabv3plus_s16_voc2012.md │ │ │ ├── deeplabv3plus_s8r1_voc2012.md │ │ │ ├── deeplabv3plus_s8r2_voc2012.md │ │ │ ├── deeplabv3s16_voc2012.md │ │ │ ├── deeplabv3s8r2_voc2012.md │ │ │ ├── deepsort_market1501.md │ │ │ ├── deeptext_icdar2013.md │ │ │ ├── delf_gldv2.md │ │ │ ├── delf_googlelandmarksdatasetv2.md │ │ │ ├── dem_att_cub.md │ │ │ ├── dem_cub.md │ │ │ ├── densenet121_imagenet2012.md │ │ │ ├── depthnet_coarsenet_nyu.md │ │ │ ├── depthnet_finenet_nyu.md │ │ │ ├── dgcn_citeseer.md │ │ │ ├── dgcn_cora.md │ │ │ ├── dgcn_pubmed.md │ │ │ ├── dgu_udc.md │ │ │ ├── dlinknet_DeepGlobeRoadExtractionDataset.md │ │ │ ├── dlrm_criteo.md │ │ │ ├── dncnn_bsd500.md │ │ │ ├── dpn_imagenet2012.md │ │ │ ├── drnet_classifier_imagenet2012.md │ │ │ ├── drnet_predictor_imagenet2012.md │ │ │ ├── dscnn_speechcommandsdatasetversion1.md │ │ │ ├── duconv_duconv.md │ │ │ ├── east_icdar2015.md │ │ │ ├── ecapatdnn_voxceleb.md │ │ │ ├── ecolite_ucf101.md │ │ │ ├── edcn_criteo.md │ │ │ ├── efficientdetd0_coco2017.md │ │ │ ├── efficientnetb0_imagenet2012.md │ │ │ ├── efficientnetb1_imagenet2012.md │ │ │ ├── efficientnetb2_imagenet2012.md │ │ │ ├── efficientnetb3_imagenet2012.md │ │ │ ├── efficientnetv2_imagenet2012.md │ │ │ ├── egnet_resnet_dutstr.md │ │ │ ├── egnet_vgg_dutstr.md │ │ │ ├── emotect_baidu.md │ │ │ ├── enet_cityscapes.md │ │ │ ├── erfnet_cityscapes.md │ │ │ ├── ernie_bdqa.md │ │ │ ├── ernie_brcd.md │ │ │ ├── ernie_chnsenticorp.md │ │ │ ├── ernie_cmcr.md │ │ │ ├── ernie_msraner.md │ │ │ ├── ernie_xnli.md │ │ │ ├── esrgan_generator_div2k.md │ │ │ ├── faceattribute_fairface_rwmfd.md │ │ │ ├── facedetection_widerface.md │ │ │ ├── facequalityassessment_300wlp.md │ │ │ ├── facerecognition_ms1mv2.md │ │ │ ├── facerecognitionfortracking_lfw.md │ │ │ ├── fasterrcnn_resnetv1101_coco2017.md │ │ │ ├── fasterrcnn_resnetv1152_coco2017.md │ │ │ ├── fasterrcnn_resnetv150_coco2017.md │ │ │ ├── fasterrcnndcn_coco2017.md │ │ │ ├── fastscnn_cityscapes.md │ │ │ ├── fastspeech_ljspeech11.md │ │ │ ├── fasttext_agnews.md │ │ │ ├── fasttext_dbpedia.md │ │ │ ├── fasttext_yelp.md │ │ │ ├── fatdeepffm_criteo.md │ │ │ ├── fcanet_augmentedpascal.md │ │ │ ├── fcn4_musictagging.md │ │ │ ├── fcn8s_voc2012.md │ │ │ ├── fdabnn_cifar10.md │ │ │ ├── fishnet99_imagenet2012.md │ │ │ ├── gan_G_mnist.md │ │ │ ├── gat_citeseer.md │ │ │ ├── gat_cora.md │ │ │ ├── gcn_citesser.md │ │ │ ├── gcn_cora.md │ │ │ ├── genetres50_minux_imagenet2012.md │ │ │ ├── genetres50_plus_imagenet2012.md │ │ │ ├── genetres50_theta_imagenet2012.md │ │ │ ├── ghostnet_imagenet2012.md │ │ │ ├── ghostnetquant_imagenet2012.md │ │ │ ├── gnmtv2_wmtende.md │ │ │ ├── googlenet_cifar10.md │ │ │ ├── googlenet_imagenet2012.md │ │ │ ├── gpt3_openweb.md │ │ │ ├── gru_muti30k.md │ │ │ ├── hardnet_imagenet2012.md │ │ │ ├── hed_bsds500.md │ │ │ ├── hournas_cifar10.md │ │ │ ├── hrnet_cityscapes.md │ │ │ ├── hrnetw48cls_imagenet2012.md │ │ │ ├── hrnetw48seg_cityscapes.md │ │ │ ├── hypertext_iflytek.md │ │ │ ├── hypertext_tnews.md │ │ │ ├── ibnnet_imagenet2012.md │ │ │ ├── icnet_cityscapes.md │ │ │ ├── inceptionresnetv2_imagenet2012.md │ │ │ ├── inceptionv3_imagenet2012.md │ │ │ ├── inceptionv4_imagenet2012.md │ │ │ ├── ipt_denoise30_cbsd68.md │ │ │ ├── ipt_denoise50_cbsd68.md │ │ │ ├── ipt_derain_rain100l.md │ │ │ ├── ipt_x2_set5.md │ │ │ ├── ipt_x3_set5.md │ │ │ ├── ipt_x4_set5.md │ │ │ ├── irn_div2k.md │ │ │ ├── isynet_N0_imagenet2012.md │ │ │ ├── isynet_N1S1_imagenet2012.md │ │ │ ├── isynet_N1S2_imagenet2012.md │ │ │ ├── isynet_N1S3_imagenet2012.md │ │ │ ├── isynet_N1_imagenet2012.md │ │ │ ├── isynet_N2_imagenet2012.md │ │ │ ├── isynet_N3_imagenet2012.md │ │ │ ├── ktnet_record.md │ │ │ ├── ktnet_squad.md │ │ │ ├── learningtoseeinthedark_sony.md │ │ │ ├── lenet_mnist.md │ │ │ ├── lightcnn_msceleb1m.md │ │ │ ├── lpcnet_librispeech.md │ │ │ ├── lstm_aclimdbv1.md │ │ │ ├── lstmcrf_conll2000.md │ │ │ ├── luke_squad1.1.md │ │ │ ├── manidp_cifar10.md │ │ │ ├── maskrcnn_coco2017.md │ │ │ ├── maskrcnnmobilenetv1_coco2017.md │ │ │ ├── mass_newscrawl_gigaword_cornell.md │ │ │ ├── mcnn_shanghaitecha.md │ │ │ ├── melgan_Generator_ljspeech.md │ │ │ ├── metabaseline_miniimagenet.md │ │ │ ├── midas_redweb.md │ │ │ ├── mmoe_censusincome.md │ │ │ ├── mnasnet_imagenet2012.md │ │ │ ├── mobilenetv1_cifar10.md │ │ │ ├── mobilenetv1_imagenet2012.md │ │ │ ├── mobilenetv2_imagenet2012.md │ │ │ ├── mobilenetv3_imagenet2012.md │ │ │ ├── mobilenetv3large_imagenet2012.md │ │ │ ├── mobilenetv3smallx10_imagenet2012.md │ │ │ ├── moleculardynamics_water.md │ │ │ ├── multitasknet_veri.md │ │ │ ├── mvd_allresearch_sysumm01.md │ │ │ ├── mvd_indoorsearch_sysumm01.md │ │ │ ├── mvd_infraredtovisible_regdb.md │ │ │ ├── mvd_visibletoinfrared_regdb.md │ │ │ ├── naml_mindlarge.md │ │ │ ├── nasfpn_coco2017.md │ │ │ ├── nasnet_imagenet2012.md │ │ │ ├── ncf_movielens.md │ │ │ ├── neighbor2neighbor_kodak.md │ │ │ ├── nfnet_imagenet2012.md │ │ │ ├── nima_avadataset.md │ │ │ ├── ntsnet_cub2002011.md │ │ │ ├── ocrnet_cityscapes.md │ │ │ ├── octsqueeze_kitti.md │ │ │ ├── openpose_coco2017.md │ │ │ ├── opt_cococaption.md │ │ │ ├── osnet_dukemtmcreid.md │ │ │ ├── osnet_market1501.md │ │ │ ├── osnet_msmt17.md │ │ │ ├── patchcore_mvtecad.md │ │ │ ├── pcbrpp_market1501.md │ │ │ ├── pdarts_cifar10.md │ │ │ ├── pgan_G_celeba.md │ │ │ ├── pix2pix_Generator_facades.md │ │ │ ├── pix2pix_Generator_maps.md │ │ │ ├── pix2pix_facades.md │ │ │ ├── pix2pix_maps.md │ │ │ ├── pnasnet_imagenet2012.md │ │ │ ├── pointnet2_modelnet40.md │ │ │ ├── pointnet_shapenet.md │ │ │ ├── poseestnet_veir.md │ │ │ ├── posenet_kingscollege.md │ │ │ ├── posenet_stmaryschurch.md │ │ │ ├── predrnnplusplus_movingmnist.md │ │ │ ├── protonet_omniglot.md │ │ │ ├── proxylessnas_imagenet2012.md │ │ │ ├── psenet_icdar2015.md │ │ │ ├── pspnet_ade20k.md │ │ │ ├── pspnet_voc2012.md │ │ │ ├── pvnet_ape_linemod.md │ │ │ ├── pvnet_cam_linemod.md │ │ │ ├── pvnet_can_linemod.md │ │ │ ├── pvnet_cat_linemod.md │ │ │ ├── pwcnet_flyingchairs.md │ │ │ ├── r2plus1d_ucf101.md │ │ │ ├── ras_dutstrain.md │ │ │ ├── rcan_div2k.md │ │ │ ├── rdn_div2k.md │ │ │ ├── rednet30_bsd300.md │ │ │ ├── refinedet_coco2017.md │ │ │ ├── refinenet_voc2012.md │ │ │ ├── relationnet_omniglot.md │ │ │ ├── renas_cifar10.md │ │ │ ├── repvgg_imagenet2012.md │ │ │ ├── res2net101_imagenet2012.md │ │ │ ├── res2net152_imagenet2012.md │ │ │ ├── res2net50_cifar10.md │ │ │ ├── res2net50_imagenet2012.md │ │ │ ├── res2netdeeplabv3_s16r1_voc2012.md │ │ │ ├── res2netdeeplabv3_s8r2_voc2012.md │ │ │ ├── res2netdeeplabv3_s8r2multiscale_voc2012.md │ │ │ ├── res2netdeeplabv3_s8r2multiscalefilp_voc2012.md │ │ │ ├── res2netfasterrcnn_coco2017.md │ │ │ ├── res2netyolov3_coco2017.md │ │ │ ├── resnest50_imagenet2012.md │ │ │ ├── resnet101_imagenet2012.md │ │ │ ├── resnet152_imagenet2012.md │ │ │ ├── resnet18_cifar10.md │ │ │ ├── resnet18_imagenet2012.md │ │ │ ├── resnet34_imagenet2012.md │ │ │ ├── resnet50_cifar10.md │ │ │ ├── resnet50_imagenet2012.md │ │ │ ├── resnet50_quadruplet_sop.md │ │ │ ├── resnet50_triplet_sop.md │ │ │ ├── resnet50advpruning_imagenet2012.md │ │ │ ├── resnet50bam_imagenet2012.md │ │ │ ├── resnet50quadruplet_sop.md │ │ │ ├── resnet50triplet_sop.md │ │ │ ├── resnetv2101_cifar10.md │ │ │ ├── resnetv2152_cifar10.md │ │ │ ├── resnetv250_cifar10.md │ │ │ ├── resnetv250frn_imagenet2012.md │ │ │ ├── resnext101_imagenet2012.md │ │ │ ├── resnext15264x4d_imagenet2012.md │ │ │ ├── resnext50_imagenet2012.md │ │ │ ├── retinafaceresnet50_widerface.md │ │ │ ├── retinanet_coco2017.md │ │ │ ├── retinanetresnet101_coco2017.md │ │ │ ├── rotate_wn18rr.md │ │ │ ├── sdne_wiki.md │ │ │ ├── semantichumanmatting_mattinghumandatasets.md │ │ │ ├── senet_imagenet2012.md │ │ │ ├── senetresnet50_imagenet2012.md │ │ │ ├── senta_sst2.md │ │ │ ├── seq2seq_wmt14.md │ │ │ ├── seres2net50_imagenet2012.md │ │ │ ├── seresnet50_imagenet2012.md │ │ │ ├── seresnext50_imagenet2012.md │ │ │ ├── shufflenetv1_imagenet2012.md │ │ │ ├── shufflenetv2_imagenet2012.md │ │ │ ├── siamfc_ilsvrc2015vid.md │ │ │ ├── siamrpn_vid.md │ │ │ ├── simclr_encoder_cifar10.md │ │ │ ├── simclr_linearclassifier_cifar10.md │ │ │ ├── simplebaselines_coco2017.md │ │ │ ├── simplepose_coco2017.md │ │ │ ├── singan_G_asinglenatureimage.md │ │ │ ├── singlepathnas_imagenet2012.md │ │ │ ├── skipgram_text8.md │ │ │ ├── sknet_cifar10.md │ │ │ ├── slowfast_ava.md │ │ │ ├── snnmlp_t_imagenet2012.md │ │ │ ├── sphereface_casiawebface.md │ │ │ ├── sppnet_mult_imagenet2012.md │ │ │ ├── sppnet_single_imagenet2012.md │ │ │ ├── sppnet_zfnet_imagenet2012.md │ │ │ ├── squeezenet11_imagenet2012.md │ │ │ ├── squeezenet_cifar10.md │ │ │ ├── squeezenet_imagenet2012.md │ │ │ ├── squeezenetresidual_cifar10.md │ │ │ ├── squeezenetresidual_imagenet2012.md │ │ │ ├── srcnn_ilsvrc2013.md │ │ │ ├── srgan_div2k.md │ │ │ ├── ssd300_coco2017.md │ │ │ ├── ssdghostnet_coco2017.md │ │ │ ├── ssdinceptionv2_coco2014mini.md │ │ │ ├── ssdmobilenetv1fpn_coco2017.md │ │ │ ├── ssdmobilenetv2_coco2017.md │ │ │ ├── ssdmobilenetv2fpnlite_coco2017.md │ │ │ ├── ssdresnet34_coco2017.md │ │ │ ├── ssdresnet50_coco2017.md │ │ │ ├── ssdresnet50fpn_coco2017.md │ │ │ ├── ssdvgg16_coco2017.md │ │ │ ├── ssimae_mvtecadbottle.md │ │ │ ├── ssimae_mvtecadcable.md │ │ │ ├── ssimae_mvtecadcapsule.md │ │ │ ├── ssimae_mvtecadcarpet.md │ │ │ ├── ssimae_mvtecadgrid.md │ │ │ ├── ssimae_mvtecadmetalnut.md │ │ │ ├── stackedhourglass_mpii.md │ │ │ ├── stargan_Generator_celeba.md │ │ │ ├── stgan_celeba.md │ │ │ ├── stgcn_cheb15_pemsd7m.md │ │ │ ├── stgcn_cheb30_pemsd7m.md │ │ │ ├── stgcn_cheb45_pemsd7m.md │ │ │ ├── stgcn_gcn15_pemsd7m.md │ │ │ ├── stgcn_gcn30_pemsd7m.md │ │ │ ├── stgcn_gcn45_pemsd7m.md │ │ │ ├── stnet_kinetics400.md │ │ │ ├── stpm_mvtecadbottle.md │ │ │ ├── stpm_mvtecadcable.md │ │ │ ├── stpm_mvtecadcapsule.md │ │ │ ├── stpm_mvtecadcarpet.md │ │ │ ├── stpm_mvtecadgrid.md │ │ │ ├── stpm_mvtecadhazelnut.md │ │ │ ├── stpm_mvtecadleather.md │ │ │ ├── stpm_mvtecadmetalnut.md │ │ │ ├── stpm_mvtecadpill.md │ │ │ ├── stpm_mvtecadscrew.md │ │ │ ├── stpm_mvtecadtile.md │ │ │ ├── stpm_mvtecadtoothbrush.md │ │ │ ├── stpm_mvtecadtransistor.md │ │ │ ├── stpm_mvtecadwood.md │ │ │ ├── stpm_mvtecadzipper.md │ │ │ ├── swintransformer_imagenet2012.md │ │ │ ├── tacotron2_ljspeech11.md │ │ │ ├── tall_tacos.md │ │ │ ├── tbnet_steam.md │ │ │ ├── tcn_addingproblem.md │ │ │ ├── tcn_permutedmnist.md │ │ │ ├── textcnn_moviereview.md │ │ │ ├── textcnn_sst2.md │ │ │ ├── textcnn_subj.md │ │ │ ├── textcrnn_gru_polarity_moviereview.md │ │ │ ├── textcrnn_lstm_polarity_moviereview.md │ │ │ ├── textfusenet_totaltext.md │ │ │ ├── tgcn_losloop.md │ │ │ ├── tgcn_sztaxi.md │ │ │ ├── tinybert_enwiki128_mnli.md │ │ │ ├── tinybert_enwiki128_qnli.md │ │ │ ├── tinybert_enwiki128_sst2.md │ │ │ ├── tinydarknet_imagenet2012.md │ │ │ ├── tnt_imagenet2012.md │ │ │ ├── tokenfusion_nyudv2.md │ │ │ ├── tprr_readeralbert_hotpotqa.md │ │ │ ├── tprr_readerdownstream_hotpotqa.md │ │ │ ├── tprr_rerankalbert_hotpotqa.md │ │ │ ├── tprr_rerankdownstream_hotpotqa.md │ │ │ ├── transformer_large_wmt.md │ │ │ ├── transformer_wmt.md │ │ │ ├── tsm_somethingsomethingv2.md │ │ │ ├── tsn_flow_ucf101.md │ │ │ ├── tsn_rgb_ucf101.md │ │ │ ├── u2net_dutstr.md │ │ │ ├── ugatit_selfie2anime.md │ │ │ ├── unet2d_isbichallenge.md │ │ │ ├── unet3d_luna16.md │ │ │ ├── unet3plus_lits2017.md │ │ │ ├── unet_medical_isbi.md │ │ │ ├── unetnested_cellnuclei.md │ │ │ ├── vehiclenet_vehiclenetdataset.md │ │ │ ├── vgg16_bn_imagenet2012.md │ │ │ ├── vgg16_cifar10.md │ │ │ ├── vgg16_imagenet2012.md │ │ │ ├── vgg19_cifar10.md │ │ │ ├── vgg19_imagenet2012.md │ │ │ ├── vit_imagenet2012.md │ │ │ ├── vitbase_cifar10.md │ │ │ ├── vnet_promise2012.md │ │ │ ├── warpctc_captcha.md │ │ │ ├── wavemlp_imagenet2012.md │ │ │ ├── wdsr_div2k.md │ │ │ ├── wgan_batchnorm_G_lsunbedrooms.md │ │ │ ├── wgan_nobatchnorm_G_lsunbedrooms.md │ │ │ ├── wideanddeep_criteo.md │ │ │ ├── wideanddeep_dynamicshape_criteo.md │ │ │ ├── wideanddeep_hostdevice_criteo.md │ │ │ ├── wideanddeep_ps_criteo.md │ │ │ ├── wideanddeepmultitable_outbrain.md │ │ │ ├── wideresnet_cifar10.md │ │ │ ├── xception_imagenet2012.md │ │ │ ├── yolov3darknet53_coco2014.md │ │ │ ├── yolov3darknet53shape416_coco2014.md │ │ │ ├── yolov3resnet18_coco2017.md │ │ │ ├── yolov3tiny_coco2017.md │ │ │ ├── yolov4_coco2017.md │ │ │ ├── yolov4shape416_coco2017.md │ │ │ ├── yolov5s_coco2017.md │ │ │ ├── yolox_coco2017.md │ │ │ └── yolox_darknet53_coco2017.md │ └── noah-cvlab │ │ ├── ascend │ │ ├── 0.7 │ │ │ ├── ghostnet_ssd_v1.0.md │ │ │ └── ghostnet_ssd_v1.0_cn.md │ │ ├── 1.0 │ │ │ ├── ghostnet_ssd_v1.0.md │ │ │ └── ghostnet_ssd_v1.0_cn.md │ │ ├── 1.1 │ │ │ ├── ipt_v1.0_Rain100L_derain.md │ │ │ ├── ipt_v1.0_Set14_SR_x2.md │ │ │ ├── ipt_v1.0_Set14_SR_x3.md │ │ │ └── ipt_v1.0_Set14_SR_x4.md │ │ └── 1.8 │ │ │ └── adabin_cifar10.md │ │ ├── gpu │ │ ├── 1.0 │ │ │ ├── VGG-Small-low-bit_cifar10.md │ │ │ ├── VGG-Small-low-bit_cifar10_cn.md │ │ │ ├── ghostnet-600_v1.0_oxford_pets.md │ │ │ ├── ghostnet-600_v1.0_oxford_pets_cn.md │ │ │ ├── ghostnet_int8_v1.0_oxford_pets.md │ │ │ ├── ghostnet_int8_v1.0_oxford_pets_cn.md │ │ │ ├── ghostnet_v1.0_oxford_pets.md │ │ │ ├── ghostnet_v1.0_oxford_pets_cn.md │ │ │ ├── plain-CNN-resnet18_v1.0_cifar_10.md │ │ │ ├── plain-CNN-resnet18_v1.0_cifar_10_cn.md │ │ │ ├── plain-CNN-resnet34_v1.0_cifar10.md │ │ │ ├── plain-CNN-resnet34_v1.0_cifar10_cn.md │ │ │ ├── plain-CNN-resnet50_v1.0_cifar10.md │ │ │ ├── plain-CNN-resnet50_v1.0_cifar10_cn.md │ │ │ ├── resnet-0.65x_v1.0_oxford_pets.md │ │ │ └── resnet-0.65x_v1.0_oxford_pets_cn.md │ │ ├── 1.1 │ │ │ ├── HourNAS-F_v1.0_cifar10.md │ │ │ ├── ipt_v1.0_Rain100L_derain.md │ │ │ ├── ipt_v1.0_Set14_SR_x2.md │ │ │ ├── ipt_v1.0_Set14_SR_x3.md │ │ │ ├── ipt_v1.0_Set14_SR_x4.md │ │ │ ├── manidp_v1.0_cifar10.md │ │ │ ├── renas_v1.0_cifar10.md │ │ │ └── tnt_v1.0_oxford_pets.md │ │ ├── 1.3 │ │ │ └── fdabnn_cifar10.md │ │ └── 1.8 │ │ │ ├── fastmim_v1.0_imagenet2012.md │ │ │ ├── localmim_v1.0_imagenet2012.md │ │ │ ├── manifold_kd_v1.0_imagenet2012.md │ │ │ ├── networkexpansion_v1.0_imagenet2012.md │ │ │ ├── ofakd_v1.0_imagenet2012.md │ │ │ ├── tokenfusion_v1.0_nyudv2.md │ │ │ └── vanillakd_v1.0_imagenet2012.md │ │ └── static │ │ └── images │ │ └── ghostnet_static_img.png ├── examples │ ├── commit_example.md │ └── commit_example_cn.md └── tools │ ├── get_sha256.py │ ├── load_markdown.py │ └── md_validator.py ├── requirements.txt ├── setup.py └── tests ├── __init__.py ├── st └── __init__.py └── ut ├── __init__.py ├── runtest.sh ├── test.py ├── test_list.py ├── test_load.py └── test_validator_md.py /.gitee/PULL_REQUEST_TEMPLATE.md: -------------------------------------------------------------------------------- 1 | 5 | 6 | **What type of PR is this?** 7 | > Uncomment only one ` /kind <>` line, hit enter to put that in a new line, and remove leading whitespaces from that line: 8 | > 9 | > /kind bug 10 | > /kind task 11 | > /kind feature 12 | 13 | 14 | **What does this PR do / why do we need it**: 15 | 16 | 17 | **Which issue(s) this PR fixes**: 18 | 22 | Fixes # 23 | 24 | **Special notes for your reviewers**: 25 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # MindSpore 2 | build/ 3 | output 4 | 5 | # Python files 6 | *__pycache__* 7 | .pytest_cache 8 | 9 | # Editor 10 | .vscode 11 | .idea/ 12 | 13 | # Mac files 14 | *.DS_Store -------------------------------------------------------------------------------- /NOTICE: -------------------------------------------------------------------------------- 1 | MindSpore Hub 2 | Copyright 2020 Huawei Technologies Co., Ltd 3 | -------------------------------------------------------------------------------- /OWNERS: -------------------------------------------------------------------------------- 1 | approvers: 2 | - kingxian 3 | - guoqi1024 4 | - baochong 5 | - c_34 6 | - zhao_ting_v 7 | - gengdongjie 8 | - liangxhao 9 | - qujianwei 10 | - Shawny233 11 | - oacjiewen -------------------------------------------------------------------------------- /build.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | # Copyright 2020 Huawei Technologies Co., Ltd 3 | # 4 | # Licensed under the Apache License, Version 2.0 (the "License"); 5 | # you may not use this file except in compliance with the License. 6 | # You may obtain a copy of the License at 7 | # 8 | # http://www.apache.org/licenses/LICENSE-2.0 9 | # 10 | # Unless required by applicable law or agreed to in writing, software 11 | # distributed under the License is distributed on an "AS IS" BASIS, 12 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 13 | # See the License for the specific language governing permissions and 14 | # limitations under the License. 15 | 16 | set -e 17 | BASEPATH=$(cd "$(dirname $0)" || exit; pwd) 18 | PYTHON=$(which python3) 19 | OUTPUT_PATH="${BASEPATH}/output" 20 | 21 | mk_new_dir() { 22 | local create_dir="$1" # the target to make 23 | 24 | if [[ -d "${create_dir}" ]];then 25 | rm -rf "${create_dir}" 26 | fi 27 | 28 | mkdir -pv "${create_dir}" 29 | } 30 | 31 | write_checksum() { 32 | cd "$OUTPUT_PATH" || exit 33 | PACKAGE_LIST=$(ls *.whl) || exit 34 | for PACKAGE_NAME in $PACKAGE_LIST; do 35 | echo $PACKAGE_NAME 36 | sha256sum -b "$PACKAGE_NAME" >"$PACKAGE_NAME.sha256" 37 | done 38 | } 39 | 40 | cd $BASEPATH || exit 41 | rm -rf build 42 | mk_new_dir "${OUTPUT_PATH}" 43 | ${PYTHON} ${BASEPATH}/setup.py bdist_wheel 44 | mv *.egg-info build 45 | mv ${BASEPATH}/dist/*whl ${OUTPUT_PATH} 46 | rm -rf ${BASEPATH}/dist 47 | write_checksum 48 | echo "------Successfully created mindspore hub package------" 49 | 50 | -------------------------------------------------------------------------------- /docs/MindSpore-logo.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mindspore-ai/hub/7351873cc950650acd2c67c5d705c93f04667387/docs/MindSpore-logo.png -------------------------------------------------------------------------------- /docs/api_python/hub.rst: -------------------------------------------------------------------------------- 1 | mindspore_hub 2 | ========================= 3 | 4 | MindSpore Hub是MindSpore生态系统的预训练模型应用工具，作为模型开发者和应用开发者的通道。 5 | 6 | .. py:function:: mindspore_hub.load(name, *args, source='gitee', pretrained=True, force_reload=False, **kwargs) 7 | 8 | 用于加载指定网络。加载完成后，可用于推理验证、迁移学习等。如果 `source` 为 ``'local'``，会先检查本地是否存在对应模型文件，如果存在 9 | 就加载本地的，反之会自动下载模型文件并加载；如果 `pretrained` 为 ``True``,会先检查本地是否存在对应的ckpt文件，存在就会直接加载， 10 | 不存在就会自动下载到本地并加载。 11 | 12 | 参数： 13 | - **name** (str) - 网络的 ``'uid'`` 或 ``'url'`` ，或者md文件的本地路径。 14 | - **args** (tuple) - 网络初始化的参数。 15 | - **source** (str，可选) - 是否将 `name` 解析为gitee模型URI、github模型URI或本地资源。默认值：``'gitee'``。 16 | - **pretrained** (bool，可选) - 是否加载预训练模型。 默认值：``True``。 17 | - **force_reload** (bool，可选) - 是否从 `url` 重新加载网络和ckpt。 默认值：``False``。 18 | - **kwargs** (dict) - 网络初始化的关键字参数。 19 | 20 | 返回： 21 | Cell，网络。 22 | 23 | .. py:function:: mindspore_hub.hub_list(version=None, force_reload=False) 24 | 25 | 列出MindSpore Hub支持的所有asset。 26 | 27 | 参数： 28 | - **version** (str) - 指定要列出的版本。``None`` 表示最新版本。默认值：``None``。 29 | - **force_reload** (bool) - 是否从 `url` 重新加载网络。 默认值：``False``。 30 | 31 | 返回： 32 | list，MindSpore Hub支持的asset列表。 33 | -------------------------------------------------------------------------------- /docs/api_python_en/hub.rst: -------------------------------------------------------------------------------- 1 | .. MindSpore documentation master file, created by 2 | sphinx-quickstart on Thu Mar 24 11:00:00 2020. 3 | You can adapt this file completely to your liking, but it should at least 4 | contain the root `toctree` directive. 5 | 6 | mindspore_hub 7 | ================= 8 | 9 | .. automodule:: mindspore_hub 10 | :members: 11 | -------------------------------------------------------------------------------- /mindspore_hub/_utils/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright 2020 Huawei Technologies Co., Ltd 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | # ============================================================================ 15 | """Utils""" 16 | -------------------------------------------------------------------------------- /mindspore_hub/_utils/full_download.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | # Copyright 2019 Huawei Technologies Co., Ltd 3 | # 4 | # Licensed under the Apache License, Version 2.0 (the "License"); 5 | # you may not use this file except in compliance with the License. 6 | # You may obtain a copy of the License at 7 | # 8 | # http://www.apache.org/licenses/LICENSE-2.0 9 | # 10 | # Unless required by applicable law or agreed to in writing, software 11 | # distributed under the License is distributed on an "AS IS" BASIS, 12 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 13 | # See the License for the specific language governing permissions and 14 | # limitations under the License. 15 | # ============================================================================ 16 | 17 | #1.git_dir 2.save_path:.mscache 3.model_path 4.repo git 5.branch 18 | git_dir=$1 19 | save_path=$2 20 | model_path=$3 21 | repo=$4 22 | branch=$5 23 | 24 | cd $git_dir || exit 25 | git clone -b $branch $repo 26 | cd .. 27 | rm $git_dir/$model_path/.git -rf 28 | cp $git_dir/* $save_path -r 29 | rm ${git_dir:?}/* -rf 30 | if [ $? -ne 0 ]; then 31 | echo "failed" 32 | else 33 | echo "succeed" 34 | fi 35 | -------------------------------------------------------------------------------- /mindspore_hub/_utils/sparse_download.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | # Copyright 2019 Huawei Technologies Co., Ltd 3 | # 4 | # Licensed under the Apache License, Version 2.0 (the "License"); 5 | # you may not use this file except in compliance with the License. 6 | # You may obtain a copy of the License at 7 | # 8 | # http://www.apache.org/licenses/LICENSE-2.0 9 | # 10 | # Unless required by applicable law or agreed to in writing, software 11 | # distributed under the License is distributed on an "AS IS" BASIS, 12 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 13 | # See the License for the specific language governing permissions and 14 | # limitations under the License. 15 | # ============================================================================ 16 | 17 | #1.git_dir 2.save_path:.mscache 3.model_path: model_zoo/official/cv/mobilenetv2 4.repo git 5.branch 18 | git_dir=$1 19 | save_path=$2 20 | model_path=$3 21 | repo=$4 22 | branch=$5 23 | 24 | cd $git_dir || exit 25 | git init 26 | git config core.sparsecheckout true 27 | echo $model_path >> .git/info/sparse-checkout 28 | git remote add -f origin $repo 29 | git pull origin $branch 30 | cp $model_path $save_path -r 31 | cd .. 32 | rm ${git_dir:?}/* -rf 33 | if [ $? -ne 0 ]; then 34 | echo "failed" 35 | else 36 | echo "succeed" 37 | fi 38 | -------------------------------------------------------------------------------- /mindspore_hub/_utils/whitelist.py: -------------------------------------------------------------------------------- 1 | # Copyright 2020 Huawei Technologies Co., Ltd 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | # ============================================================================ 15 | """White list""" 16 | import socket 17 | from urllib.parse import urlparse 18 | 19 | _WHITE_LIST = ('https://download.mindspore.cn', 20 | 'https://www.mindspore.cn', 21 | 'https://gitee.com/mindspore/mindspore', 22 | 'https://gitee.com/mindspore/models', 23 | 'https://github.com/mindspore-ai/mindspore') 24 | _WHITE_LIST_INFO = None 25 | 26 | 27 | class UrlInfo: 28 | def __init__(self, url): 29 | parsed = urlparse(url) 30 | self.domain = parsed.netloc 31 | self.ip = socket.gethostbyname(parsed.netloc) 32 | self.path = parsed.path 33 | 34 | 35 | def _get_whitelist_info(): 36 | global _WHITE_LIST_INFO 37 | if _WHITE_LIST_INFO is None: 38 | _WHITE_LIST_INFO = set() 39 | for url in _WHITE_LIST: 40 | info = UrlInfo(url) 41 | _WHITE_LIST_INFO.add(info) 42 | return _WHITE_LIST_INFO 43 | 44 | 45 | def verify_url(url): 46 | """ 47 | Verify that the URL is in the whitelist. 48 | 49 | Args: 50 | url (str): A string of url. 51 | 52 | Returns: 53 | bool, whether the url in whitlist. 54 | """ 55 | target_info = UrlInfo(url) 56 | whitelist_info = _get_whitelist_info() 57 | for info in whitelist_info: 58 | if (target_info.ip == info.ip or target_info.domain == info.domain) and \ 59 | target_info.path.startswith(info.path): 60 | return True 61 | return False 62 | -------------------------------------------------------------------------------- /mindspore_hub/deploy/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright 2020 Huawei Technologies Co., Ltd 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | # ============================================================================ 15 | """Deploy module.""" 16 | -------------------------------------------------------------------------------- /mindspore_hub/deploy/benchmark.py: -------------------------------------------------------------------------------- 1 | # Copyright 2020 Huawei Technologies Co., Ltd 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | # ============================================================================ 15 | """Benchmark interface.""" 16 | 17 | def run_benchmark(name, device, address, script): 18 | """ 19 | Run benchmark. 20 | 21 | Args: 22 | name (str): The name or url of network. 23 | device (list): A list of target device. 24 | address (str): A string of device address to run benchmark. 25 | script (str): A path of script to run benchmark. 26 | 27 | Returns: 28 | dict, return a dictory of benchmark result. 29 | """ 30 | raise NotImplementedError() 31 | -------------------------------------------------------------------------------- /mindspore_hub/deploy/service.py: -------------------------------------------------------------------------------- 1 | # Copyright 2020 Huawei Technologies Co., Ltd 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | # ============================================================================ 15 | """Serving interface.""" 16 | 17 | def serving(name, port=8080): 18 | """ 19 | Deploy a network inference server. 20 | 21 | Args: 22 | name (str): A name or url of network. 23 | port (int): Port number of server. Default: 8080. 24 | """ 25 | raise NotImplementedError() 26 | -------------------------------------------------------------------------------- /mindspore_hub/info.py: -------------------------------------------------------------------------------- 1 | # Copyright 2020 Huawei Technologies Co., Ltd 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | # ============================================================================ 15 | """ 16 | The information of network in mindspore_hub. 17 | """ 18 | 19 | from ._utils.check import ValidMarkdown 20 | 21 | 22 | class CellInfo: 23 | """ 24 | Information of network. 25 | """ 26 | def __init__(self, md_path): 27 | json_dict = ValidMarkdown(md_path).check_markdown_file() 28 | self.name = json_dict.get('model-name') 29 | self.backbone_name = json_dict.get('backbone-name') 30 | self.type = json_dict.get('module-type') 31 | self.fine_tunable = json_dict.get('fine-tunable') 32 | self.input_shape = json_dict.get('input-shape') 33 | self.author = json_dict.get('author') 34 | self.update_time = json_dict.get('update-time') 35 | self.repo_link = json_dict.get('repo-link') 36 | self.user_id = json_dict.get('user-id') 37 | self.backend = json_dict.get('backend') 38 | if json_dict.get('allow-cache-ckpt') is not None: 39 | self.allow_cache_ckpt = json_dict.get('allow-cache-ckpt') 40 | self.dataset = json_dict.get('train-dataset') 41 | self.license = json_dict.get('license') 42 | self.accuracy = json_dict.get('accuracy') 43 | self.used_for = json_dict.get('used-for') 44 | self.model_version = json_dict.get('model-version') 45 | self.mindspore_version = json_dict.get('mindspore-version') 46 | self.asset = json_dict.get('asset') 47 | self.asset_id = json_dict.get('asset-id') 48 | self.evaluation = json_dict.get('evaluation') 49 | -------------------------------------------------------------------------------- /mindspore_hub/manage.py: -------------------------------------------------------------------------------- 1 | # Copyright 2020 Huawei Technologies Co., Ltd 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | # ============================================================================ 15 | """Manage network in mindspore_hub.""" 16 | 17 | import os 18 | 19 | CACHE_DIR = '~/.mscache' 20 | 21 | 22 | def set_hub_dir(dir_path='~/.mscache'): 23 | """ 24 | Set the path of cache. 25 | 26 | Args: 27 | dir (str): The path of cache. 28 | """ 29 | global CACHE_DIR 30 | CACHE_DIR = dir_path 31 | 32 | 33 | def get_hub_dir(): 34 | """ 35 | Get the path of cache. 36 | 37 | Returns: 38 | str, return a string of path. 39 | """ 40 | return os.path.abspath(os.path.expanduser(CACHE_DIR)) 41 | -------------------------------------------------------------------------------- /mindspore_hub/optimize/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright 2020 Huawei Technologies Co., Ltd 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | # ============================================================================ 15 | """Optimize module.""" 16 | -------------------------------------------------------------------------------- /mindspore_hub/optimize/transfer.py: -------------------------------------------------------------------------------- 1 | # Copyright 2020 Huawei Technologies Co., Ltd 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | # ============================================================================ 15 | """Transfer interface.""" 16 | 17 | def transform(net, model, method, device, dataset, use_sync=False): 18 | """ 19 | Transform model for model acceleration or model lightweight. 20 | 21 | Args: 22 | net (Cell): Network. 23 | model (str): A string of model name or a url path of model. The value is None, if not set model. 24 | method (str): Method used to transforming model. 25 | device (str): Target device. 26 | dataset (Dataset): Dataset used to transforming model. 27 | use_sync (bool): Whether to synchronize cache on cloud side. 28 | 29 | Returns: 30 | Cell, return network after transformed. 31 | """ 32 | raise NotImplementedError() 33 | -------------------------------------------------------------------------------- /mindspore_hub/version.py: -------------------------------------------------------------------------------- 1 | #pylint: disable=C0111 2 | __version__ = '2.0.0' 3 | -------------------------------------------------------------------------------- /mshub_res/assets/mindspore/1.10/avacifar_cifar10.md: -------------------------------------------------------------------------------- 1 | # AVA_cifar 2 | 3 | --- 4 | 5 | model-name: AVA_cifar 6 | 7 | backbone-name: AVA_cifar 8 | 9 | module-type: cv 10 | 11 | fine-tunable: True 12 | 13 | model-version: 1.10 14 | 15 | train-dataset: cifar10 16 | 17 | evaluation: top1acc90.87 18 | 19 | author: MindSpore team 20 | 21 | update-time: 2023-2-17 22 | 23 | repo-link: 24 | 25 | user-id: MindSpore 26 | 27 | used-for: inference 28 | 29 | mindspore-version: 1.10 30 | 31 | asset: 32 | 33 | - 34 | file-format: ckpt 35 | asset-link: 36 | asset-sha256: 955f6c567108af733ea82f8efb1eec0accd44cbc79a41b2d216ca83d09a646c6 37 | 38 | license: Apache2.0 39 | 40 | summary: AVA_cifar is used for cv 41 | 42 | --- 43 | 44 | ## Introduction 45 | 46 | This MindSpore Hub model uses the implementation of AVA_cifar from the MindSpore model zoo on Gitee at research/cv/AVA_cifar. 47 | 48 | AVA_cifar is a cv network. More details please refer to the MindSpore model zoo on Gitee at [research/cv/AVA_cifar](https://gitee.com/mindspore/models/blob/r1.10/research/cv/AVA_cifar/README.md). 49 | 50 | All parameters in the module are trainable. 51 | 52 | ## Disclaimer 53 | 54 | MindSpore ("we") do not own any ownership or intellectual property rights of the datasets, and the trained models are provided on an "as is" and "as available" basis. We make no representations or warranties of any kind of the datasets and trained models (collectively, “materials”) and will not be liable for any loss, damage, expense or cost arising from the materials. Please ensure that you have permission to use the dataset under the appropriate license for the dataset and in accordance with the terms of the relevant license agreement. The trained models provided are only for research and education purposes. 55 | 56 | To Dataset Owners: If you do not wish to have a dataset included in MindSpore, or wish to update it in any way, we will remove or update the content at your request. Please contact us through GitHub or Gitee. Your understanding and contributions to the community are greatly appreciated. 57 | 58 | MindSpore is available under the Apache 2.0 license, please see the LICENSE file. 59 | -------------------------------------------------------------------------------- /mshub_res/assets/mindspore/1.10/avahpa_hpa.md: -------------------------------------------------------------------------------- 1 | # AVA_hpa 2 | 3 | --- 4 | 5 | model-name: AVA_hpa 6 | 7 | backbone-name: AVA_hpa 8 | 9 | module-type: cv 10 | 11 | fine-tunable: True 12 | 13 | model-version: 1.10 14 | 15 | train-dataset: hpa 16 | 17 | evaluation: macroF1acc70.05 | microF1acc77.97 | auc94.23 18 | 19 | author: MindSpore team 20 | 21 | update-time: 2023-2-17 22 | 23 | repo-link: 24 | 25 | user-id: MindSpore 26 | 27 | used-for: inference 28 | 29 | mindspore-version: 1.10 30 | 31 | asset: 32 | 33 | - 34 | file-format: ckpt 35 | asset-link: 36 | asset-sha256: 50d6c1d61a750a3524076c1088bda669d492713a6b8be9d71dd9c6b5e2656102 37 | 38 | license: Apache2.0 39 | 40 | summary: AVA_hpa is used for cv 41 | 42 | --- 43 | 44 | ## Introduction 45 | 46 | This MindSpore Hub model uses the implementation of AVA_hpa from the MindSpore model zoo on Gitee at research/cv/AVA_hpa. 47 | 48 | AVA_hpa is a cv network. More details please refer to the MindSpore model zoo on Gitee at [research/cv/AVA_hpa](https://gitee.com/mindspore/models/blob/r1.10/research/cv/AVA_hpa/README.md). 49 | 50 | All parameters in the module are trainable. 51 | 52 | ## Disclaimer 53 | 54 | MindSpore ("we") do not own any ownership or intellectual property rights of the datasets, and the trained models are provided on an "as is" and "as available" basis. We make no representations or warranties of any kind of the datasets and trained models (collectively, “materials”) and will not be liable for any loss, damage, expense or cost arising from the materials. Please ensure that you have permission to use the dataset under the appropriate license for the dataset and in accordance with the terms of the relevant license agreement. The trained models provided are only for research and education purposes. 55 | 56 | To Dataset Owners: If you do not wish to have a dataset included in MindSpore, or wish to update it in any way, we will remove or update the content at your request. Please contact us through GitHub or Gitee. Your understanding and contributions to the community are greatly appreciated. 57 | 58 | MindSpore is available under the Apache 2.0 license, please see the LICENSE file. 59 | -------------------------------------------------------------------------------- /mshub_res/assets/mindspore/1.10/cct_imagenet2012.md: -------------------------------------------------------------------------------- 1 | # cct 2 | 3 | --- 4 | 5 | model-name: cct 6 | 7 | backbone-name: cct 8 | 9 | module-type: cv 10 | 11 | fine-tunable: True 12 | 13 | model-version: 1.10 14 | 15 | train-dataset: imagenet2012 16 | 17 | evaluation: top1acc80.24 | top5acc94.93 18 | 19 | author: MindSpore team 20 | 21 | update-time: 2023-2-17 22 | 23 | repo-link: 24 | 25 | user-id: MindSpore 26 | 27 | used-for: inference 28 | 29 | mindspore-version: 1.10 30 | 31 | asset: 32 | 33 | - 34 | file-format: ckpt 35 | asset-link: 36 | asset-sha256: 7df9c4c1e9d190f289aadfe79fdbef5c330228b1694230eeedf552dc15188261 37 | 38 | license: Apache2.0 39 | 40 | summary: cct is used for cv 41 | 42 | --- 43 | 44 | ## Introduction 45 | 46 | This MindSpore Hub model uses the implementation of cct from the MindSpore model zoo on Gitee at research/cv/cct. 47 | 48 | cct is a cv network. More details please refer to the MindSpore model zoo on Gitee at [research/cv/cct](https://gitee.com/mindspore/models/blob/r1.10/research/cv/cct/README_CN.md). 49 | 50 | All parameters in the module are trainable. 51 | 52 | ## Disclaimer 53 | 54 | MindSpore ("we") do not own any ownership or intellectual property rights of the datasets, and the trained models are provided on an "as is" and "as available" basis. We make no representations or warranties of any kind of the datasets and trained models (collectively, “materials”) and will not be liable for any loss, damage, expense or cost arising from the materials. Please ensure that you have permission to use the dataset under the appropriate license for the dataset and in accordance with the terms of the relevant license agreement. The trained models provided are only for research and education purposes. 55 | 56 | To Dataset Owners: If you do not wish to have a dataset included in MindSpore, or wish to update it in any way, we will remove or update the content at your request. Please contact us through GitHub or Gitee. Your understanding and contributions to the community are greatly appreciated. 57 | 58 | MindSpore is available under the Apache 2.0 license, please see the LICENSE file. 59 | -------------------------------------------------------------------------------- /mshub_res/assets/mindspore/1.10/dgu_udc.md: -------------------------------------------------------------------------------- 1 | # dgu 2 | 3 | --- 4 | 5 | model-name: dgu 6 | 7 | backbone-name: dgu 8 | 9 | module-type: nlp 10 | 11 | fine-tunable: True 12 | 13 | model-version: 1.10 14 | 15 | train-dataset: udc 16 | 17 | evaluation: R1acc82.14 | R2acc90.45 | R5acc97.75 18 | 19 | author: MindSpore team 20 | 21 | update-time: 2023-2-17 22 | 23 | repo-link: 24 | 25 | user-id: MindSpore 26 | 27 | used-for: inference 28 | 29 | mindspore-version: 1.10 30 | 31 | asset: 32 | 33 | - 34 | file-format: ckpt 35 | asset-link: 36 | asset-sha256: ef323d57071720e70df35c28d0d8cffcfafdefdfe694ca4c39b21d013f2b8542 37 | 38 | license: Apache2.0 39 | 40 | summary: dgu is used for nlp 41 | 42 | --- 43 | 44 | ## Introduction 45 | 46 | This MindSpore Hub model uses the implementation of dgu from the MindSpore model zoo on Gitee at official/nlp/dgu. 47 | 48 | dgu is a nlp network. More details please refer to the MindSpore model zoo on Gitee at [official/nlp/dgu](https://gitee.com/mindspore/models/blob/r1.10/official/nlp/dgu/README_CN.md). 49 | 50 | All parameters in the module are trainable. 51 | 52 | ## Disclaimer 53 | 54 | MindSpore ("we") do not own any ownership or intellectual property rights of the datasets, and the trained models are provided on an "as is" and "as available" basis. We make no representations or warranties of any kind of the datasets and trained models (collectively, “materials”) and will not be liable for any loss, damage, expense or cost arising from the materials. Please ensure that you have permission to use the dataset under the appropriate license for the dataset and in accordance with the terms of the relevant license agreement. The trained models provided are only for research and education purposes. 55 | 56 | To Dataset Owners: If you do not wish to have a dataset included in MindSpore, or wish to update it in any way, we will remove or update the content at your request. Please contact us through GitHub or Gitee. Your understanding and contributions to the community are greatly appreciated. 57 | 58 | MindSpore is available under the Apache 2.0 license, please see the LICENSE file. 59 | -------------------------------------------------------------------------------- /mshub_res/assets/mindspore/1.10/emotect_baidu.md: -------------------------------------------------------------------------------- 1 | # emotect 2 | 3 | --- 4 | 5 | model-name: emotect 6 | 7 | backbone-name: emotect 8 | 9 | module-type: nlp 10 | 11 | fine-tunable: True 12 | 13 | model-version: 1.10 14 | 15 | train-dataset: baidu 16 | 17 | evaluation: acc90.44 18 | 19 | author: MindSpore team 20 | 21 | update-time: 2023-2-17 22 | 23 | repo-link: 24 | 25 | user-id: MindSpore 26 | 27 | used-for: inference 28 | 29 | mindspore-version: 1.10 30 | 31 | asset: 32 | 33 | - 34 | file-format: ckpt 35 | asset-link: 36 | asset-sha256: 5b0ddb04f17f27d706007353d15faf93b627dcaf0ce7fcfcf1b61d4f2ddc228b 37 | 38 | license: Apache2.0 39 | 40 | summary: emotect is used for nlp 41 | 42 | --- 43 | 44 | ## Introduction 45 | 46 | This MindSpore Hub model uses the implementation of emotect from the MindSpore model zoo on Gitee at official/nlp/emotect. 47 | 48 | emotect is a nlp network. More details please refer to the MindSpore model zoo on Gitee at [official/nlp/emotect](https://gitee.com/mindspore/models/blob/r1.10/official/nlp/emotect/README_CN.md). 49 | 50 | All parameters in the module are trainable. 51 | 52 | ## Disclaimer 53 | 54 | MindSpore ("we") do not own any ownership or intellectual property rights of the datasets, and the trained models are provided on an "as is" and "as available" basis. We make no representations or warranties of any kind of the datasets and trained models (collectively, “materials”) and will not be liable for any loss, damage, expense or cost arising from the materials. Please ensure that you have permission to use the dataset under the appropriate license for the dataset and in accordance with the terms of the relevant license agreement. The trained models provided are only for research and education purposes. 55 | 56 | To Dataset Owners: If you do not wish to have a dataset included in MindSpore, or wish to update it in any way, we will remove or update the content at your request. Please contact us through GitHub or Gitee. Your understanding and contributions to the community are greatly appreciated. 57 | 58 | MindSpore is available under the Apache 2.0 license, please see the LICENSE file. 59 | -------------------------------------------------------------------------------- /mshub_res/assets/mindspore/1.10/rcan_div2k.md: -------------------------------------------------------------------------------- 1 | # RCAN 2 | 3 | --- 4 | 5 | model-name: RCAN 6 | 7 | backbone-name: RCAN 8 | 9 | module-type: cv 10 | 11 | fine-tunable: True 12 | 13 | model-version: 1.10 14 | 15 | train-dataset: DIV2K 16 | 17 | evaluation: PSNR38.26 | SSIM0.9614 18 | 19 | author: MindSpore team 20 | 21 | update-time: 2023-2-17 22 | 23 | repo-link: 24 | 25 | user-id: MindSpore 26 | 27 | used-for: inference 28 | 29 | mindspore-version: 1.10 30 | 31 | asset: 32 | 33 | - 34 | file-format: ckpt 35 | asset-link: 36 | asset-sha256: f75418c61a5daa58c3e3499ad0551033bf3a1cce217054c7e01ab42615977286 37 | 38 | license: Apache2.0 39 | 40 | summary: RCAN is used for cv 41 | 42 | --- 43 | 44 | ## Introduction 45 | 46 | This MindSpore Hub model uses the implementation of RCAN from the MindSpore model zoo on Gitee at research/cv/RCAN. 47 | 48 | RCAN is a cv network. More details please refer to the MindSpore model zoo on Gitee at [research/cv/RCAN](https://gitee.com/mindspore/models/blob/r1.10/research/cv/RCAN/README.md). 49 | 50 | All parameters in the module are trainable. 51 | 52 | ## Disclaimer 53 | 54 | MindSpore ("we") do not own any ownership or intellectual property rights of the datasets, and the trained models are provided on an "as is" and "as available" basis. We make no representations or warranties of any kind of the datasets and trained models (collectively, “materials”) and will not be liable for any loss, damage, expense or cost arising from the materials. Please ensure that you have permission to use the dataset under the appropriate license for the dataset and in accordance with the terms of the relevant license agreement. The trained models provided are only for research and education purposes. 55 | 56 | To Dataset Owners: If you do not wish to have a dataset included in MindSpore, or wish to update it in any way, we will remove or update the content at your request. Please contact us through GitHub or Gitee. Your understanding and contributions to the community are greatly appreciated. 57 | 58 | MindSpore is available under the Apache 2.0 license, please see the LICENSE file. 59 | -------------------------------------------------------------------------------- /mshub_res/assets/mindspore/1.3/3ddensenet_iseg2017.md: -------------------------------------------------------------------------------- 1 | # 3D_DenseNet 2 | 3 | --- 4 | 5 | model-name: 3D_DenseNet 6 | 7 | backbone-name: 3D_DenseNet 8 | 9 | module-type: cv 10 | 11 | fine-tunable: True 12 | 13 | model-version: 1.3 14 | 15 | train-dataset: iseg2017 16 | 17 | evaluation: acc92 18 | 19 | author: MindSpore team 20 | 21 | update-time: 2022-03-23 22 | 23 | repo-link: 24 | 25 | user-id: MindSpore 26 | 27 | used-for: inference 28 | 29 | mindspore-version: 1.3 30 | 31 | asset: 32 | 33 | - 34 | file-format: ckpt 35 | asset-link: 36 | asset-sha256: 81196ef44f3db51e3d22b69d01373c162639fa31362dd71388906fc307ccc3fe 37 | 38 | license: Apache2.0 39 | 40 | summary: 3D_DenseNet is used for cv 41 | 42 | --- 43 | 44 | ## Introduction 45 | 46 | This MindSpore Hub model uses the implementation of 3D_DenseNet from the MindSpore model zoo on Gitee at research/cv/3D_DenseNet. 47 | 48 | 3D_DenseNet is a cv network. More details please refer to the MindSpore model zoo on Gitee at [research/cv/3D_DenseNet](https://gitee.com/mindspore/models/blob/r1.3/research/cv/3D_DenseNet/README.md). 49 | 50 | All parameters in the module are trainable. 51 | 52 | ## Disclaimer 53 | 54 | MindSpore ("we") do not own any ownership or intellectual property rights of the datasets, and the trained models are provided on an "as is" and "as available" basis. We make no representations or warranties of any kind of the datasets and trained models (collectively, “materials”) and will not be liable for any loss, damage, expense or cost arising from the materials. Please ensure that you have permission to use the dataset under the appropriate license for the dataset and in accordance with the terms of the relevant license agreement. The trained models provided are only for research and education purposes. 55 | 56 | To Dataset Owners: If you do not wish to have a dataset included in MindSpore, or wish to update it in any way, we will remove or update the content at your request. Please contact us through GitHub or Gitee. Your understanding and contributions to the community are greatly appreciated. 57 | 58 | MindSpore is available under the Apache 2.0 license, please see the LICENSE file. 59 | -------------------------------------------------------------------------------- /mshub_res/assets/mindspore/1.3/cct_imagenet2012.md: -------------------------------------------------------------------------------- 1 | # cct 2 | 3 | --- 4 | 5 | model-name: cct 6 | 7 | backbone-name: cct 8 | 9 | module-type: cv 10 | 11 | fine-tunable: True 12 | 13 | model-version: 1.3 14 | 15 | train-dataset: imagenet2012 16 | 17 | evaluation: top1acc80.24 | top5acc94.93 18 | 19 | author: MindSpore team 20 | 21 | update-time: 2022-04-12 22 | 23 | repo-link: 24 | 25 | user-id: MindSpore 26 | 27 | used-for: inference 28 | 29 | mindspore-version: 1.3 30 | 31 | asset: 32 | 33 | - 34 | file-format: ckpt 35 | asset-link: 36 | asset-sha256: 7df9c4c1e9d190f289aadfe79fdbef5c330228b1694230eeedf552dc15188261 37 | 38 | license: Apache2.0 39 | 40 | summary: cct is used for cv 41 | 42 | --- 43 | 44 | ## Introduction 45 | 46 | This MindSpore Hub model uses the implementation of cct from the MindSpore model zoo on Gitee at research/cv/cct. 47 | 48 | cct is a cv network. More details please refer to the MindSpore model zoo on Gitee at [research/cv/cct](https://gitee.com/mindspore/models/blob/r1.3/research/cv/cct/README_CN.md). 49 | 50 | All parameters in the module are trainable. 51 | 52 | ## Disclaimer 53 | 54 | MindSpore ("we") do not own any ownership or intellectual property rights of the datasets, and the trained models are provided on an "as is" and "as available" basis. We make no representations or warranties of any kind of the datasets and trained models (collectively, “materials”) and will not be liable for any loss, damage, expense or cost arising from the materials. Please ensure that you have permission to use the dataset under the appropriate license for the dataset and in accordance with the terms of the relevant license agreement. The trained models provided are only for research and education purposes. 55 | 56 | To Dataset Owners: If you do not wish to have a dataset included in MindSpore, or wish to update it in any way, we will remove or update the content at your request. Please contact us through GitHub or Gitee. Your understanding and contributions to the community are greatly appreciated. 57 | 58 | MindSpore is available under the Apache 2.0 license, please see the LICENSE file. 59 | -------------------------------------------------------------------------------- /mshub_res/assets/mindspore/1.3/cgan_G_mnist.md: -------------------------------------------------------------------------------- 1 | # CGAN 2 | 3 | --- 4 | 5 | model-name: CGAN 6 | 7 | backbone-name: CGAN 8 | 9 | module-type: cv 10 | 11 | fine-tunable: True 12 | 13 | model-version: 1.3 14 | 15 | train-dataset: mnist 16 | 17 | evaluation: - 18 | 19 | author: MindSpore team 20 | 21 | update-time: 2022-03-23 22 | 23 | repo-link: 24 | 25 | user-id: MindSpore 26 | 27 | used-for: inference 28 | 29 | mindspore-version: 1.3 30 | 31 | asset: 32 | 33 | - 34 | file-format: ckpt 35 | asset-link: 36 | asset-sha256: 360a455b190fc1a56cfb487ed25278794e86ea615458cf3066ca06e3b8ad3d41 37 | 38 | license: Apache2.0 39 | 40 | summary: CGAN is used for cv 41 | 42 | --- 43 | 44 | ## Introduction 45 | 46 | This MindSpore Hub model uses the implementation of CGAN from the MindSpore model zoo on Gitee at research/cv/CGAN. 47 | 48 | CGAN is a cv network. More details please refer to the MindSpore model zoo on Gitee at [research/cv/CGAN](https://gitee.com/mindspore/models/blob/r1.3/research/cv/CGAN/README.md). 49 | 50 | All parameters in the module are trainable. 51 | 52 | ## Citation 53 | 54 | Conditional Generative Adversarial Nets. 55 | 56 | ## Disclaimer 57 | 58 | MindSpore ("we") do not own any ownership or intellectual property rights of the datasets, and the trained models are provided on an "as is" and "as available" basis. We make no representations or warranties of any kind of the datasets and trained models (collectively, “materials”) and will not be liable for any loss, damage, expense or cost arising from the materials. Please ensure that you have permission to use the dataset under the appropriate license for the dataset and in accordance with the terms of the relevant license agreement. The trained models provided are only for research and education purposes. 59 | 60 | To Dataset Owners: If you do not wish to have a dataset included in MindSpore, or wish to update it in any way, we will remove or update the content at your request. Please contact us through GitHub or Gitee. Your understanding and contributions to the community are greatly appreciated. 61 | 62 | MindSpore is available under the Apache 2.0 license, please see the LICENSE file. 63 | -------------------------------------------------------------------------------- /mshub_res/assets/mindspore/1.3/dbpn_div2k.md: -------------------------------------------------------------------------------- 1 | # DBPN 2 | 3 | --- 4 | 5 | model-name: DBPN 6 | 7 | backbone-name: DBPN 8 | 9 | module-type: cv 10 | 11 | fine-tunable: True 12 | 13 | model-version: 1.3 14 | 15 | train-dataset: div2k 16 | 17 | evaluation: PSNR31.93 18 | 19 | author: MindSpore team 20 | 21 | update-time: 2022-03-23 22 | 23 | repo-link: 24 | 25 | user-id: MindSpore 26 | 27 | used-for: inference 28 | 29 | mindspore-version: 1.3 30 | 31 | asset: 32 | 33 | - 34 | file-format: ckpt 35 | asset-link: 36 | asset-sha256: 75d74c46e4efc3a4f2e81df53a3ab8817618d8193a6032a971fe02a5e307ef77 37 | 38 | license: Apache2.0 39 | 40 | summary: DBPN is used for cv 41 | 42 | --- 43 | 44 | ## Introduction 45 | 46 | This MindSpore Hub model uses the implementation of DBPN from the MindSpore model zoo on Gitee at research/cv/DBPN. 47 | 48 | DBPN is a cv network. More details please refer to the MindSpore model zoo on Gitee at [research/cv/DBPN](https://gitee.com/mindspore/models/blob/r1.3/research/cv/DBPN/README.md). 49 | 50 | All parameters in the module are trainable. 51 | 52 | ## Citation 53 | 54 | Muhammad Haris, Greg Shakhnarovich, Norimichi Ukita 55 | 56 | ## Disclaimer 57 | 58 | MindSpore ("we") do not own any ownership or intellectual property rights of the datasets, and the trained models are provided on an "as is" and "as available" basis. We make no representations or warranties of any kind of the datasets and trained models (collectively, “materials”) and will not be liable for any loss, damage, expense or cost arising from the materials. Please ensure that you have permission to use the dataset under the appropriate license for the dataset and in accordance with the terms of the relevant license agreement. The trained models provided are only for research and education purposes. 59 | 60 | To Dataset Owners: If you do not wish to have a dataset included in MindSpore, or wish to update it in any way, we will remove or update the content at your request. Please contact us through GitHub or Gitee. Your understanding and contributions to the community are greatly appreciated. 61 | 62 | MindSpore is available under the Apache 2.0 license, please see the LICENSE file. 63 | -------------------------------------------------------------------------------- /mshub_res/assets/mindspore/1.3/dbpngan_G_div2k.md: -------------------------------------------------------------------------------- 1 | # DBPN 2 | 3 | --- 4 | 5 | model-name: DBPN 6 | 7 | backbone-name: DBPN 8 | 9 | module-type: cv 10 | 11 | fine-tunable: True 12 | 13 | model-version: 1.3 14 | 15 | train-dataset: div2k 16 | 17 | evaluation: PSNR29.23 18 | 19 | author: MindSpore team 20 | 21 | update-time: 2022-03-23 22 | 23 | repo-link: 24 | 25 | user-id: MindSpore 26 | 27 | used-for: inference 28 | 29 | mindspore-version: 1.3 30 | 31 | asset: 32 | 33 | - 34 | file-format: ckpt 35 | asset-link: 36 | asset-sha256: 00ad7584fde0e5a926596ed91bebd5c13aa04086c22ac6def15a9d14dcd7eec1 37 | 38 | license: Apache2.0 39 | 40 | summary: DBPN is used for cv 41 | 42 | --- 43 | 44 | ## Introduction 45 | 46 | This MindSpore Hub model uses the implementation of DBPN from the MindSpore model zoo on Gitee at research/cv/DBPN. 47 | 48 | DBPN is a cv network. More details please refer to the MindSpore model zoo on Gitee at [research/cv/DBPN](https://gitee.com/mindspore/models/blob/r1.3/research/cv/DBPN/README.md). 49 | 50 | All parameters in the module are trainable. 51 | 52 | ## Citation 53 | 54 | Muhammad Haris, Greg Shakhnarovich, Norimichi Ukita 55 | 56 | ## Disclaimer 57 | 58 | MindSpore ("we") do not own any ownership or intellectual property rights of the datasets, and the trained models are provided on an "as is" and "as available" basis. We make no representations or warranties of any kind of the datasets and trained models (collectively, “materials”) and will not be liable for any loss, damage, expense or cost arising from the materials. Please ensure that you have permission to use the dataset under the appropriate license for the dataset and in accordance with the terms of the relevant license agreement. The trained models provided are only for research and education purposes. 59 | 60 | To Dataset Owners: If you do not wish to have a dataset included in MindSpore, or wish to update it in any way, we will remove or update the content at your request. Please contact us through GitHub or Gitee. Your understanding and contributions to the community are greatly appreciated. 61 | 62 | MindSpore is available under the Apache 2.0 license, please see the LICENSE file. 63 | -------------------------------------------------------------------------------- /mshub_res/assets/mindspore/1.3/ddrnet_imagenet2012.md: -------------------------------------------------------------------------------- 1 | # DDRNet 2 | 3 | --- 4 | 5 | model-name: DDRNet 6 | 7 | backbone-name: DDRNet 8 | 9 | module-type: cv 10 | 11 | fine-tunable: True 12 | 13 | model-version: 1.3 14 | 15 | train-dataset: imagenet2012 16 | 17 | evaluation: top1acc76.36 | top5acc93.24 18 | 19 | author: MindSpore team 20 | 21 | update-time: 2022-04-18 22 | 23 | repo-link: 24 | 25 | user-id: MindSpore 26 | 27 | used-for: inference 28 | 29 | mindspore-version: 1.3 30 | 31 | asset: 32 | 33 | - 34 | file-format: ckpt 35 | asset-link: 36 | asset-sha256: 6acbc32e242e674a61b812adfb1a7972348e013f0c8bd0beccaeca0446305997 37 | 38 | license: Apache2.0 39 | 40 | summary: DDRNet is used for cv 41 | 42 | --- 43 | 44 | ## Introduction 45 | 46 | This MindSpore Hub model uses the implementation of DDRNet from the MindSpore model zoo on Gitee at research/cv/DDRNet. 47 | 48 | DDRNet is a cv network. More details please refer to the MindSpore model zoo on Gitee at [research/cv/DDRNet](https://gitee.com/mindspore/models/blob/r1.3/research/cv/DDRNet/README_CN.md). 49 | 50 | All parameters in the module are trainable. 51 | 52 | ## Disclaimer 53 | 54 | MindSpore ("we") do not own any ownership or intellectual property rights of the datasets, and the trained models are provided on an "as is" and "as available" basis. We make no representations or warranties of any kind of the datasets and trained models (collectively, “materials”) and will not be liable for any loss, damage, expense or cost arising from the materials. Please ensure that you have permission to use the dataset under the appropriate license for the dataset and in accordance with the terms of the relevant license agreement. The trained models provided are only for research and education purposes. 55 | 56 | To Dataset Owners: If you do not wish to have a dataset included in MindSpore, or wish to update it in any way, we will remove or update the content at your request. Please contact us through GitHub or Gitee. Your understanding and contributions to the community are greatly appreciated. 57 | 58 | MindSpore is available under the Apache 2.0 license, please see the LICENSE file. 59 | -------------------------------------------------------------------------------- /mshub_res/assets/mindspore/1.3/dgu_udc.md: -------------------------------------------------------------------------------- 1 | # dgu 2 | 3 | --- 4 | 5 | model-name: dgu 6 | 7 | backbone-name: dgu 8 | 9 | module-type: nlp 10 | 11 | fine-tunable: True 12 | 13 | model-version: 1.3 14 | 15 | train-dataset: udc 16 | 17 | evaluation: R1acc82.14 | R2acc90.45 | R5acc97.75 18 | 19 | author: MindSpore team 20 | 21 | update-time: 2022-03-23 22 | 23 | repo-link: 24 | 25 | user-id: MindSpore 26 | 27 | used-for: inference 28 | 29 | mindspore-version: 1.3 30 | 31 | asset: 32 | 33 | - 34 | file-format: ckpt 35 | asset-link: 36 | asset-sha256: ef323d57071720e70df35c28d0d8cffcfafdefdfe694ca4c39b21d013f2b8542 37 | 38 | license: Apache2.0 39 | 40 | summary: dgu is used for nlp 41 | 42 | --- 43 | 44 | ## Introduction 45 | 46 | This MindSpore Hub model uses the implementation of dgu from the MindSpore model zoo on Gitee at official/nlp/dgu. 47 | 48 | dgu is a nlp network. More details please refer to the MindSpore model zoo on Gitee at [official/nlp/dgu](https://gitee.com/mindspore/models/blob/r1.3/official/nlp/dgu/README_CN.md). 49 | 50 | All parameters in the module are trainable. 51 | 52 | ## Disclaimer 53 | 54 | MindSpore ("we") do not own any ownership or intellectual property rights of the datasets, and the trained models are provided on an "as is" and "as available" basis. We make no representations or warranties of any kind of the datasets and trained models (collectively, “materials”) and will not be liable for any loss, damage, expense or cost arising from the materials. Please ensure that you have permission to use the dataset under the appropriate license for the dataset and in accordance with the terms of the relevant license agreement. The trained models provided are only for research and education purposes. 55 | 56 | To Dataset Owners: If you do not wish to have a dataset included in MindSpore, or wish to update it in any way, we will remove or update the content at your request. Please contact us through GitHub or Gitee. Your understanding and contributions to the community are greatly appreciated. 57 | 58 | MindSpore is available under the Apache 2.0 license, please see the LICENSE file. 59 | -------------------------------------------------------------------------------- /mshub_res/assets/mindspore/1.3/emotect_baidu.md: -------------------------------------------------------------------------------- 1 | # emotect 2 | 3 | --- 4 | 5 | model-name: emotect 6 | 7 | backbone-name: emotect 8 | 9 | module-type: nlp 10 | 11 | fine-tunable: True 12 | 13 | model-version: 1.3 14 | 15 | train-dataset: baidu 16 | 17 | evaluation: acc90.44 18 | 19 | author: MindSpore team 20 | 21 | update-time: 2022-03-23 22 | 23 | repo-link: 24 | 25 | user-id: MindSpore 26 | 27 | used-for: inference 28 | 29 | mindspore-version: 1.3 30 | 31 | asset: 32 | 33 | - 34 | file-format: ckpt 35 | asset-link: 36 | asset-sha256: 5b0ddb04f17f27d706007353d15faf93b627dcaf0ce7fcfcf1b61d4f2ddc228b 37 | 38 | license: Apache2.0 39 | 40 | summary: emotect is used for nlp 41 | 42 | --- 43 | 44 | ## Introduction 45 | 46 | This MindSpore Hub model uses the implementation of emotect from the MindSpore model zoo on Gitee at official/nlp/emotect. 47 | 48 | emotect is a nlp network. More details please refer to the MindSpore model zoo on Gitee at [official/nlp/emotect](https://gitee.com/mindspore/models/blob/r1.3/official/nlp/emotect/README_CN.md). 49 | 50 | All parameters in the module are trainable. 51 | 52 | ## Disclaimer 53 | 54 | MindSpore ("we") do not own any ownership or intellectual property rights of the datasets, and the trained models are provided on an "as is" and "as available" basis. We make no representations or warranties of any kind of the datasets and trained models (collectively, “materials”) and will not be liable for any loss, damage, expense or cost arising from the materials. Please ensure that you have permission to use the dataset under the appropriate license for the dataset and in accordance with the terms of the relevant license agreement. The trained models provided are only for research and education purposes. 55 | 56 | To Dataset Owners: If you do not wish to have a dataset included in MindSpore, or wish to update it in any way, we will remove or update the content at your request. Please contact us through GitHub or Gitee. Your understanding and contributions to the community are greatly appreciated. 57 | 58 | MindSpore is available under the Apache 2.0 license, please see the LICENSE file. 59 | -------------------------------------------------------------------------------- /mshub_res/assets/mindspore/1.3/nfnet_imagenet2012.md: -------------------------------------------------------------------------------- 1 | # NFNet 2 | 3 | --- 4 | 5 | model-name: NFNet 6 | 7 | backbone-name: NFNet 8 | 9 | module-type: cv 10 | 11 | fine-tunable: True 12 | 13 | model-version: 1.3 14 | 15 | train-dataset: imagenet2012 16 | 17 | evaluation: top1acc83.26 18 | 19 | author: MindSpore team 20 | 21 | update-time: 2022-04-24 22 | 23 | repo-link: 24 | 25 | user-id: MindSpore 26 | 27 | used-for: inference 28 | 29 | mindspore-version: 1.3 30 | 31 | asset: 32 | 33 | - 34 | file-format: ckpt 35 | asset-link: 36 | asset-sha256: 773b0098a124e13beaee3a14b3dfc09b73139cc185f11fc43d9d2124d6905c26 37 | 38 | license: Apache2.0 39 | 40 | summary: NFNet is used for cv 41 | 42 | --- 43 | 44 | ## Introduction 45 | 46 | This MindSpore Hub model uses the implementation of NFNet from the MindSpore model zoo on Gitee at research/cv/NFNet. 47 | 48 | NFNet is a cv network. More details please refer to the MindSpore model zoo on Gitee at [research/cv/NFNet](https://gitee.com/mindspore/models/blob/r1.3/research/cv/NFNet/README_CN.md). 49 | 50 | All parameters in the module are trainable. 51 | 52 | ## Disclaimer 53 | 54 | MindSpore ("we") do not own any ownership or intellectual property rights of the datasets, and the trained models are provided on an "as is" and "as available" basis. We make no representations or warranties of any kind of the datasets and trained models (collectively, “materials”) and will not be liable for any loss, damage, expense or cost arising from the materials. Please ensure that you have permission to use the dataset under the appropriate license for the dataset and in accordance with the terms of the relevant license agreement. The trained models provided are only for research and education purposes. 55 | 56 | To Dataset Owners: If you do not wish to have a dataset included in MindSpore, or wish to update it in any way, we will remove or update the content at your request. Please contact us through GitHub or Gitee. Your understanding and contributions to the community are greatly appreciated. 57 | 58 | MindSpore is available under the Apache 2.0 license, please see the LICENSE file. 59 | -------------------------------------------------------------------------------- /mshub_res/assets/mindspore/1.3/tnt_imagenet2012.md: -------------------------------------------------------------------------------- 1 | # TNT 2 | 3 | --- 4 | 5 | model-name: TNT 6 | 7 | backbone-name: TNT 8 | 9 | module-type: cv 10 | 11 | fine-tunable: True 12 | 13 | model-version: 1.3 14 | 15 | train-dataset: imagenet2012 16 | 17 | evaluation: top1acc81.03 | top5acc95.41 18 | 19 | author: MindSpore team 20 | 21 | update-time: 2022-04-18 22 | 23 | repo-link: 24 | 25 | user-id: MindSpore 26 | 27 | used-for: inference 28 | 29 | mindspore-version: 1.3 30 | 31 | asset: 32 | 33 | - 34 | file-format: ckpt 35 | asset-link: 36 | asset-sha256: 028493c50c666b32c37a036c217c0416a81cc822c591a6662f7136606d66b38d 37 | 38 | license: Apache2.0 39 | 40 | summary: TNT is used for cv 41 | 42 | --- 43 | 44 | ## Introduction 45 | 46 | This MindSpore Hub model uses the implementation of TNT from the MindSpore model zoo on Gitee at research/cv/TNT. 47 | 48 | TNT is a cv network. More details please refer to the MindSpore model zoo on Gitee at [research/cv/TNT](https://gitee.com/mindspore/models/blob/r1.3/research/cv/TNT/README_CN.md). 49 | 50 | All parameters in the module are trainable. 51 | 52 | ## Disclaimer 53 | 54 | MindSpore ("we") do not own any ownership or intellectual property rights of the datasets, and the trained models are provided on an "as is" and "as available" basis. We make no representations or warranties of any kind of the datasets and trained models (collectively, “materials”) and will not be liable for any loss, damage, expense or cost arising from the materials. Please ensure that you have permission to use the dataset under the appropriate license for the dataset and in accordance with the terms of the relevant license agreement. The trained models provided are only for research and education purposes. 55 | 56 | To Dataset Owners: If you do not wish to have a dataset included in MindSpore, or wish to update it in any way, we will remove or update the content at your request. Please contact us through GitHub or Gitee. Your understanding and contributions to the community are greatly appreciated. 57 | 58 | MindSpore is available under the Apache 2.0 license, please see the LICENSE file. 59 | -------------------------------------------------------------------------------- /mshub_res/assets/mindspore/1.5/avacifar_cifar10.md: -------------------------------------------------------------------------------- 1 | # AVA_cifar 2 | 3 | --- 4 | 5 | model-name: AVA_cifar 6 | 7 | backbone-name: AVA_cifar 8 | 9 | module-type: cv 10 | 11 | fine-tunable: True 12 | 13 | model-version: 1.5 14 | 15 | train-dataset: cifar10 16 | 17 | evaluation: top1acc90.87 18 | 19 | author: MindSpore team 20 | 21 | update-time: 2022-03-30 22 | 23 | repo-link: 24 | 25 | user-id: MindSpore 26 | 27 | used-for: inference 28 | 29 | mindspore-version: 1.5 30 | 31 | asset: 32 | 33 | - 34 | file-format: ckpt 35 | asset-link: 36 | asset-sha256: 955f6c567108af733ea82f8efb1eec0accd44cbc79a41b2d216ca83d09a646c6 37 | 38 | license: Apache2.0 39 | 40 | summary: AVA_cifar is used for cv 41 | 42 | --- 43 | 44 | ## Introduction 45 | 46 | This MindSpore Hub model uses the implementation of AVA_cifar from the MindSpore model zoo on Gitee at research/cv/AVA_cifar. 47 | 48 | AVA_cifar is a cv network. More details please refer to the MindSpore model zoo on Gitee at [research/cv/AVA_cifar](https://gitee.com/mindspore/models/blob/r1.5/research/cv/AVA_cifar/README.md). 49 | 50 | All parameters in the module are trainable. 51 | 52 | ## Disclaimer 53 | 54 | MindSpore ("we") do not own any ownership or intellectual property rights of the datasets, and the trained models are provided on an "as is" and "as available" basis. We make no representations or warranties of any kind of the datasets and trained models (collectively, “materials”) and will not be liable for any loss, damage, expense or cost arising from the materials. Please ensure that you have permission to use the dataset under the appropriate license for the dataset and in accordance with the terms of the relevant license agreement. The trained models provided are only for research and education purposes. 55 | 56 | To Dataset Owners: If you do not wish to have a dataset included in MindSpore, or wish to update it in any way, we will remove or update the content at your request. Please contact us through GitHub or Gitee. Your understanding and contributions to the community are greatly appreciated. 57 | 58 | MindSpore is available under the Apache 2.0 license, please see the LICENSE file. 59 | -------------------------------------------------------------------------------- /mshub_res/assets/mindspore/1.5/avahpa_hpa.md: -------------------------------------------------------------------------------- 1 | # AVA_hpa 2 | 3 | --- 4 | 5 | model-name: AVA_hpa 6 | 7 | backbone-name: AVA_hpa 8 | 9 | module-type: cv 10 | 11 | fine-tunable: True 12 | 13 | model-version: 1.5 14 | 15 | train-dataset: hpa 16 | 17 | evaluation: macroF1acc70.05 | microF1acc77.97 | auc94.23 18 | 19 | author: MindSpore team 20 | 21 | update-time: 2022-04-18 22 | 23 | repo-link: 24 | 25 | user-id: MindSpore 26 | 27 | used-for: inference 28 | 29 | mindspore-version: 1.5 30 | 31 | asset: 32 | 33 | - 34 | file-format: ckpt 35 | asset-link: 36 | asset-sha256: 50d6c1d61a750a3524076c1088bda669d492713a6b8be9d71dd9c6b5e2656102 37 | 38 | license: Apache2.0 39 | 40 | summary: AVA_hpa is used for cv 41 | 42 | --- 43 | 44 | ## Introduction 45 | 46 | This MindSpore Hub model uses the implementation of AVA_hpa from the MindSpore model zoo on Gitee at research/cv/AVA_hpa. 47 | 48 | AVA_hpa is a cv network. More details please refer to the MindSpore model zoo on Gitee at [research/cv/AVA_hpa](https://gitee.com/mindspore/models/blob/r1.5/research/cv/AVA_hpa/README.md). 49 | 50 | All parameters in the module are trainable. 51 | 52 | ## Disclaimer 53 | 54 | MindSpore ("we") do not own any ownership or intellectual property rights of the datasets, and the trained models are provided on an "as is" and "as available" basis. We make no representations or warranties of any kind of the datasets and trained models (collectively, “materials”) and will not be liable for any loss, damage, expense or cost arising from the materials. Please ensure that you have permission to use the dataset under the appropriate license for the dataset and in accordance with the terms of the relevant license agreement. The trained models provided are only for research and education purposes. 55 | 56 | To Dataset Owners: If you do not wish to have a dataset included in MindSpore, or wish to update it in any way, we will remove or update the content at your request. Please contact us through GitHub or Gitee. Your understanding and contributions to the community are greatly appreciated. 57 | 58 | MindSpore is available under the Apache 2.0 license, please see the LICENSE file. 59 | -------------------------------------------------------------------------------- /mshub_res/assets/mindspore/1.5/c3d_hmdb51.md: -------------------------------------------------------------------------------- 1 | # c3d 2 | 3 | --- 4 | 5 | model-name: c3d 6 | 7 | backbone-name: c3d 8 | 9 | module-type: cv 10 | 11 | fine-tunable: True 12 | 13 | model-version: 1.5 14 | 15 | train-dataset: hmdb51 16 | 17 | evaluation: top1acc49.67 18 | 19 | author: MindSpore team 20 | 21 | update-time: 2022-03-30 22 | 23 | repo-link: 24 | 25 | user-id: MindSpore 26 | 27 | used-for: inference 28 | 29 | mindspore-version: 1.5 30 | 31 | asset: 32 | 33 | - 34 | file-format: ckpt 35 | asset-link: 36 | asset-sha256: 76ca14dbd7c499255af90cee53f9df858632989a6deef1be0a56f63d319b9789 37 | 38 | license: Apache2.0 39 | 40 | summary: c3d is used for cv 41 | 42 | --- 43 | 44 | ## Introduction 45 | 46 | This MindSpore Hub model uses the implementation of c3d from the MindSpore model zoo on Gitee at official/cv/c3d. 47 | 48 | c3d is a cv network. More details please refer to the MindSpore model zoo on Gitee at [official/cv/c3d](https://gitee.com/mindspore/models/blob/r1.5/official/cv/c3d/README.md). 49 | 50 | All parameters in the module are trainable. 51 | 52 | ## Citation 53 | 54 | Learning Spatiotemporal Features with 3D Convolutional Networks 55 | 56 | ## Disclaimer 57 | 58 | MindSpore ("we") do not own any ownership or intellectual property rights of the datasets, and the trained models are provided on an "as is" and "as available" basis. We make no representations or warranties of any kind of the datasets and trained models (collectively, “materials”) and will not be liable for any loss, damage, expense or cost arising from the materials. Please ensure that you have permission to use the dataset under the appropriate license for the dataset and in accordance with the terms of the relevant license agreement. The trained models provided are only for research and education purposes. 59 | 60 | To Dataset Owners: If you do not wish to have a dataset included in MindSpore, or wish to update it in any way, we will remove or update the content at your request. Please contact us through GitHub or Gitee. Your understanding and contributions to the community are greatly appreciated. 61 | 62 | MindSpore is available under the Apache 2.0 license, please see the LICENSE file. 63 | -------------------------------------------------------------------------------- /mshub_res/assets/mindspore/1.5/c3d_ucf101.md: -------------------------------------------------------------------------------- 1 | # c3d 2 | 3 | --- 4 | 5 | model-name: c3d 6 | 7 | backbone-name: c3d 8 | 9 | module-type: cv 10 | 11 | fine-tunable: True 12 | 13 | model-version: 1.5 14 | 15 | train-dataset: ucf101 16 | 17 | evaluation: top1acc79.51 18 | 19 | author: MindSpore team 20 | 21 | update-time: 2022-04-12 22 | 23 | repo-link: 24 | 25 | user-id: MindSpore 26 | 27 | used-for: inference 28 | 29 | mindspore-version: 1.5 30 | 31 | asset: 32 | 33 | - 34 | file-format: ckpt 35 | asset-link: 36 | asset-sha256: 44e707636ce4ada89ee1f97ce1fecbe1a0373b7dfe21a6d711c1db827676002f 37 | 38 | license: Apache2.0 39 | 40 | summary: c3d is used for cv 41 | 42 | --- 43 | 44 | ## Introduction 45 | 46 | This MindSpore Hub model uses the implementation of c3d from the MindSpore model zoo on Gitee at official/cv/c3d. 47 | 48 | c3d is a cv network. More details please refer to the MindSpore model zoo on Gitee at [official/cv/c3d](https://gitee.com/mindspore/models/blob/r1.5/official/cv/c3d/README.md). 49 | 50 | All parameters in the module are trainable. 51 | 52 | ## Citation 53 | 54 | Learning Spatiotemporal Features with 3D Convolutional Networks 55 | 56 | ## Disclaimer 57 | 58 | MindSpore ("we") do not own any ownership or intellectual property rights of the datasets, and the trained models are provided on an "as is" and "as available" basis. We make no representations or warranties of any kind of the datasets and trained models (collectively, “materials”) and will not be liable for any loss, damage, expense or cost arising from the materials. Please ensure that you have permission to use the dataset under the appropriate license for the dataset and in accordance with the terms of the relevant license agreement. The trained models provided are only for research and education purposes. 59 | 60 | To Dataset Owners: If you do not wish to have a dataset included in MindSpore, or wish to update it in any way, we will remove or update the content at your request. Please contact us through GitHub or Gitee. Your understanding and contributions to the community are greatly appreciated. 61 | 62 | MindSpore is available under the Apache 2.0 license, please see the LICENSE file. 63 | -------------------------------------------------------------------------------- /mshub_res/assets/mindspore/1.5/cgan_G_mnist.md: -------------------------------------------------------------------------------- 1 | # CGAN 2 | 3 | --- 4 | 5 | model-name: CGAN 6 | 7 | backbone-name: CGAN 8 | 9 | module-type: cv 10 | 11 | fine-tunable: True 12 | 13 | model-version: 1.5 14 | 15 | train-dataset: mnist 16 | 17 | evaluation: - 18 | 19 | author: MindSpore team 20 | 21 | update-time: 2022-03-30 22 | 23 | repo-link: 24 | 25 | user-id: MindSpore 26 | 27 | used-for: inference 28 | 29 | mindspore-version: 1.5 30 | 31 | asset: 32 | 33 | - 34 | file-format: ckpt 35 | asset-link: 36 | asset-sha256: 360a455b190fc1a56cfb487ed25278794e86ea615458cf3066ca06e3b8ad3d41 37 | 38 | license: Apache2.0 39 | 40 | summary: CGAN is used for cv 41 | 42 | --- 43 | 44 | ## Introduction 45 | 46 | This MindSpore Hub model uses the implementation of CGAN from the MindSpore model zoo on Gitee at research/cv/CGAN. 47 | 48 | CGAN is a cv network. More details please refer to the MindSpore model zoo on Gitee at [research/cv/CGAN](https://gitee.com/mindspore/models/blob/r1.5/research/cv/CGAN/README.md). 49 | 50 | All parameters in the module are trainable. 51 | 52 | ## Citation 53 | 54 | Conditional Generative Adversarial Nets. 55 | 56 | ## Disclaimer 57 | 58 | MindSpore ("we") do not own any ownership or intellectual property rights of the datasets, and the trained models are provided on an "as is" and "as available" basis. We make no representations or warranties of any kind of the datasets and trained models (collectively, “materials”) and will not be liable for any loss, damage, expense or cost arising from the materials. Please ensure that you have permission to use the dataset under the appropriate license for the dataset and in accordance with the terms of the relevant license agreement. The trained models provided are only for research and education purposes. 59 | 60 | To Dataset Owners: If you do not wish to have a dataset included in MindSpore, or wish to update it in any way, we will remove or update the content at your request. Please contact us through GitHub or Gitee. Your understanding and contributions to the community are greatly appreciated. 61 | 62 | MindSpore is available under the Apache 2.0 license, please see the LICENSE file. 63 | -------------------------------------------------------------------------------- /mshub_res/assets/mindspore/1.5/dgu_udc.md: -------------------------------------------------------------------------------- 1 | # dgu 2 | 3 | --- 4 | 5 | model-name: dgu 6 | 7 | backbone-name: dgu 8 | 9 | module-type: nlp 10 | 11 | fine-tunable: True 12 | 13 | model-version: 1.5 14 | 15 | train-dataset: udc 16 | 17 | evaluation: R1acc82.14 | R2acc90.45 | R5acc97.75 18 | 19 | author: MindSpore team 20 | 21 | update-time: 2022-03-30 22 | 23 | repo-link: 24 | 25 | user-id: MindSpore 26 | 27 | used-for: inference 28 | 29 | mindspore-version: 1.5 30 | 31 | asset: 32 | 33 | - 34 | file-format: ckpt 35 | asset-link: 36 | asset-sha256: ef323d57071720e70df35c28d0d8cffcfafdefdfe694ca4c39b21d013f2b8542 37 | 38 | license: Apache2.0 39 | 40 | summary: dgu is used for nlp 41 | 42 | --- 43 | 44 | ## Introduction 45 | 46 | This MindSpore Hub model uses the implementation of dgu from the MindSpore model zoo on Gitee at official/nlp/dgu. 47 | 48 | dgu is a nlp network. More details please refer to the MindSpore model zoo on Gitee at [official/nlp/dgu](https://gitee.com/mindspore/models/blob/r1.5/official/nlp/dgu/README_CN.md). 49 | 50 | All parameters in the module are trainable. 51 | 52 | ## Disclaimer 53 | 54 | MindSpore ("we") do not own any ownership or intellectual property rights of the datasets, and the trained models are provided on an "as is" and "as available" basis. We make no representations or warranties of any kind of the datasets and trained models (collectively, “materials”) and will not be liable for any loss, damage, expense or cost arising from the materials. Please ensure that you have permission to use the dataset under the appropriate license for the dataset and in accordance with the terms of the relevant license agreement. The trained models provided are only for research and education purposes. 55 | 56 | To Dataset Owners: If you do not wish to have a dataset included in MindSpore, or wish to update it in any way, we will remove or update the content at your request. Please contact us through GitHub or Gitee. Your understanding and contributions to the community are greatly appreciated. 57 | 58 | MindSpore is available under the Apache 2.0 license, please see the LICENSE file. 59 | -------------------------------------------------------------------------------- /mshub_res/assets/mindspore/1.5/emotect_baidu.md: -------------------------------------------------------------------------------- 1 | # emotect 2 | 3 | --- 4 | 5 | model-name: emotect 6 | 7 | backbone-name: emotect 8 | 9 | module-type: nlp 10 | 11 | fine-tunable: True 12 | 13 | model-version: 1.5 14 | 15 | train-dataset: baidu 16 | 17 | evaluation: acc90.44 18 | 19 | author: MindSpore team 20 | 21 | update-time: 2022-03-30 22 | 23 | repo-link: 24 | 25 | user-id: MindSpore 26 | 27 | used-for: inference 28 | 29 | mindspore-version: 1.5 30 | 31 | asset: 32 | 33 | - 34 | file-format: ckpt 35 | asset-link: 36 | asset-sha256: 5b0ddb04f17f27d706007353d15faf93b627dcaf0ce7fcfcf1b61d4f2ddc228b 37 | 38 | license: Apache2.0 39 | 40 | summary: emotect is used for nlp 41 | 42 | --- 43 | 44 | ## Introduction 45 | 46 | This MindSpore Hub model uses the implementation of emotect from the MindSpore model zoo on Gitee at official/nlp/emotect. 47 | 48 | emotect is a nlp network. More details please refer to the MindSpore model zoo on Gitee at [official/nlp/emotect](https://gitee.com/mindspore/models/blob/r1.5/official/nlp/emotect/README_CN.md). 49 | 50 | All parameters in the module are trainable. 51 | 52 | ## Disclaimer 53 | 54 | MindSpore ("we") do not own any ownership or intellectual property rights of the datasets, and the trained models are provided on an "as is" and "as available" basis. We make no representations or warranties of any kind of the datasets and trained models (collectively, “materials”) and will not be liable for any loss, damage, expense or cost arising from the materials. Please ensure that you have permission to use the dataset under the appropriate license for the dataset and in accordance with the terms of the relevant license agreement. The trained models provided are only for research and education purposes. 55 | 56 | To Dataset Owners: If you do not wish to have a dataset included in MindSpore, or wish to update it in any way, we will remove or update the content at your request. Please contact us through GitHub or Gitee. Your understanding and contributions to the community are greatly appreciated. 57 | 58 | MindSpore is available under the Apache 2.0 license, please see the LICENSE file. 59 | -------------------------------------------------------------------------------- /mshub_res/assets/mindspore/1.5/relationnet_omniglot.md: -------------------------------------------------------------------------------- 1 | # relationnet 2 | 3 | --- 4 | 5 | model-name: relationnet 6 | 7 | backbone-name: relationnet 8 | 9 | module-type: cv 10 | 11 | fine-tunable: True 12 | 13 | model-version: 1.5 14 | 15 | train-dataset: omniglot 16 | 17 | evaluation: acc99.08 18 | 19 | author: MindSpore team 20 | 21 | update-time: 2022-04-12 22 | 23 | repo-link: 24 | 25 | user-id: MindSpore 26 | 27 | used-for: inference 28 | 29 | mindspore-version: 1.5 30 | 31 | asset: 32 | 33 | - 34 | file-format: ckpt 35 | asset-link: 36 | asset-sha256: b0a2b8a3864b71c719e63f7de3760cff36f75e456991c35371cea6ed0525179d 37 | 38 | license: Apache2.0 39 | 40 | summary: relationnet is used for cv 41 | 42 | --- 43 | 44 | ## Introduction 45 | 46 | This MindSpore Hub model uses the implementation of relationnet from the MindSpore model zoo on Gitee at research/cv/relationnet. 47 | 48 | relationnet is a cv network. More details please refer to the MindSpore model zoo on Gitee at [research/cv/relationnet](https://gitee.com/mindspore/models/blob/r1.5/research/cv/relationnet/README.md). 49 | 50 | All parameters in the module are trainable. 51 | 52 | ## Disclaimer 53 | 54 | MindSpore ("we") do not own any ownership or intellectual property rights of the datasets, and the trained models are provided on an "as is" and "as available" basis. We make no representations or warranties of any kind of the datasets and trained models (collectively, “materials”) and will not be liable for any loss, damage, expense or cost arising from the materials. Please ensure that you have permission to use the dataset under the appropriate license for the dataset and in accordance with the terms of the relevant license agreement. The trained models provided are only for research and education purposes. 55 | 56 | To Dataset Owners: If you do not wish to have a dataset included in MindSpore, or wish to update it in any way, we will remove or update the content at your request. Please contact us through GitHub or Gitee. Your understanding and contributions to the community are greatly appreciated. 57 | 58 | MindSpore is available under the Apache 2.0 license, please see the LICENSE file. 59 | -------------------------------------------------------------------------------- /mshub_res/assets/mindspore/1.5/tnt_imagenet2012.md: -------------------------------------------------------------------------------- 1 | # TNT 2 | 3 | --- 4 | 5 | model-name: TNT 6 | 7 | backbone-name: TNT 8 | 9 | module-type: cv 10 | 11 | fine-tunable: True 12 | 13 | model-version: 1.5 14 | 15 | train-dataset: imagenet2012 16 | 17 | evaluation: top1acc81.03 | top5acc95.41 18 | 19 | author: MindSpore team 20 | 21 | update-time: 2022-04-18 22 | 23 | repo-link: 24 | 25 | user-id: MindSpore 26 | 27 | used-for: inference 28 | 29 | mindspore-version: 1.5 30 | 31 | asset: 32 | 33 | - 34 | file-format: ckpt 35 | asset-link: 36 | asset-sha256: 028493c50c666b32c37a036c217c0416a81cc822c591a6662f7136606d66b38d 37 | 38 | license: Apache2.0 39 | 40 | summary: TNT is used for cv 41 | 42 | --- 43 | 44 | ## Introduction 45 | 46 | This MindSpore Hub model uses the implementation of TNT from the MindSpore model zoo on Gitee at research/cv/TNT. 47 | 48 | TNT is a cv network. More details please refer to the MindSpore model zoo on Gitee at [research/cv/TNT](https://gitee.com/mindspore/models/blob/r1.5/research/cv/TNT/readme.md). 49 | 50 | All parameters in the module are trainable. 51 | 52 | ## Disclaimer 53 | 54 | MindSpore ("we") do not own any ownership or intellectual property rights of the datasets, and the trained models are provided on an "as is" and "as available" basis. We make no representations or warranties of any kind of the datasets and trained models (collectively, “materials”) and will not be liable for any loss, damage, expense or cost arising from the materials. Please ensure that you have permission to use the dataset under the appropriate license for the dataset and in accordance with the terms of the relevant license agreement. The trained models provided are only for research and education purposes. 55 | 56 | To Dataset Owners: If you do not wish to have a dataset included in MindSpore, or wish to update it in any way, we will remove or update the content at your request. Please contact us through GitHub or Gitee. Your understanding and contributions to the community are greatly appreciated. 57 | 58 | MindSpore is available under the Apache 2.0 license, please see the LICENSE file. 59 | -------------------------------------------------------------------------------- /mshub_res/assets/mindspore/1.5/yolox_coco2017.md: -------------------------------------------------------------------------------- 1 | # yolox 2 | 3 | --- 4 | 5 | model-name: yolox 6 | 7 | backbone-name: yolox 8 | 9 | module-type: cv 10 | 11 | fine-tunable: True 12 | 13 | model-version: 1.5 14 | 15 | train-dataset: coco2017 16 | 17 | evaluation: map47.3 18 | 19 | author: MindSpore team 20 | 21 | update-time: 2022-04-24 22 | 23 | repo-link: 24 | 25 | user-id: MindSpore 26 | 27 | used-for: inference 28 | 29 | mindspore-version: 1.5 30 | 31 | asset: 32 | 33 | - 34 | file-format: ckpt 35 | asset-link: 36 | asset-sha256: 31ee25297c89aa6632de562c891a8fb8f85cfb52d86a2145de303452b591cdb6 37 | 38 | license: Apache2.0 39 | 40 | summary: yolox is used for cv 41 | 42 | --- 43 | 44 | ## Introduction 45 | 46 | This MindSpore Hub model uses the implementation of yolox from the MindSpore model zoo on Gitee at research/cv/yolox. 47 | 48 | yolox is a cv network. More details please refer to the MindSpore model zoo on Gitee at [research/cv/yolox](https://gitee.com/mindspore/models/blob/r1.5/research/cv/yolox/README_CN.md). 49 | 50 | All parameters in the module are trainable. 51 | 52 | ## Citation 53 | 54 | YOLOX: Exceeding YOLO Series in 2021 55 | 56 | ## Disclaimer 57 | 58 | MindSpore ("we") do not own any ownership or intellectual property rights of the datasets, and the trained models are provided on an "as is" and "as available" basis. We make no representations or warranties of any kind of the datasets and trained models (collectively, “materials”) and will not be liable for any loss, damage, expense or cost arising from the materials. Please ensure that you have permission to use the dataset under the appropriate license for the dataset and in accordance with the terms of the relevant license agreement. The trained models provided are only for research and education purposes. 59 | 60 | To Dataset Owners: If you do not wish to have a dataset included in MindSpore, or wish to update it in any way, we will remove or update the content at your request. Please contact us through GitHub or Gitee. Your understanding and contributions to the community are greatly appreciated. 61 | 62 | MindSpore is available under the Apache 2.0 license, please see the LICENSE file. 63 | -------------------------------------------------------------------------------- /mshub_res/assets/mindspore/1.6/3ddensenet_iseg2017.md: -------------------------------------------------------------------------------- 1 | # 3D_DenseNet 2 | 3 | --- 4 | 5 | model-name: 3D_DenseNet 6 | 7 | backbone-name: 3D_DenseNet 8 | 9 | module-type: cv 10 | 11 | fine-tunable: True 12 | 13 | model-version: 1.6 14 | 15 | train-dataset: iseg2017 16 | 17 | evaluation: acc92 18 | 19 | author: MindSpore team 20 | 21 | update-time: 2022-03-30 22 | 23 | repo-link: 24 | 25 | user-id: MindSpore 26 | 27 | used-for: inference 28 | 29 | mindspore-version: 1.6 30 | 31 | asset: 32 | 33 | - 34 | file-format: ckpt 35 | asset-link: 36 | asset-sha256: 81196ef44f3db51e3d22b69d01373c162639fa31362dd71388906fc307ccc3fe 37 | 38 | license: Apache2.0 39 | 40 | summary: 3D_DenseNet is used for cv 41 | 42 | --- 43 | 44 | ## Introduction 45 | 46 | This MindSpore Hub model uses the implementation of 3D_DenseNet from the MindSpore model zoo on Gitee at research/cv/3D_DenseNet. 47 | 48 | 3D_DenseNet is a cv network. More details please refer to the MindSpore model zoo on Gitee at [research/cv/3D_DenseNet](https://gitee.com/mindspore/models/blob/r1.6/research/cv/3D_DenseNet/README.md). 49 | 50 | All parameters in the module are trainable. 51 | 52 | ## Disclaimer 53 | 54 | MindSpore ("we") do not own any ownership or intellectual property rights of the datasets, and the trained models are provided on an "as is" and "as available" basis. We make no representations or warranties of any kind of the datasets and trained models (collectively, “materials”) and will not be liable for any loss, damage, expense or cost arising from the materials. Please ensure that you have permission to use the dataset under the appropriate license for the dataset and in accordance with the terms of the relevant license agreement. The trained models provided are only for research and education purposes. 55 | 56 | To Dataset Owners: If you do not wish to have a dataset included in MindSpore, or wish to update it in any way, we will remove or update the content at your request. Please contact us through GitHub or Gitee. Your understanding and contributions to the community are greatly appreciated. 57 | 58 | MindSpore is available under the Apache 2.0 license, please see the LICENSE file. 59 | -------------------------------------------------------------------------------- /mshub_res/assets/mindspore/1.6/avacifar_cifar10.md: -------------------------------------------------------------------------------- 1 | # AVA_cifar 2 | 3 | --- 4 | 5 | model-name: AVA_cifar 6 | 7 | backbone-name: AVA_cifar 8 | 9 | module-type: cv 10 | 11 | fine-tunable: True 12 | 13 | model-version: 1.6 14 | 15 | train-dataset: cifar10 16 | 17 | evaluation: top1acc90.87 18 | 19 | author: MindSpore team 20 | 21 | update-time: 2022-03-30 22 | 23 | repo-link: 24 | 25 | user-id: MindSpore 26 | 27 | used-for: inference 28 | 29 | mindspore-version: 1.6 30 | 31 | asset: 32 | 33 | - 34 | file-format: ckpt 35 | asset-link: 36 | asset-sha256: 955f6c567108af733ea82f8efb1eec0accd44cbc79a41b2d216ca83d09a646c6 37 | 38 | license: Apache2.0 39 | 40 | summary: AVA_cifar is used for cv 41 | 42 | --- 43 | 44 | ## Introduction 45 | 46 | This MindSpore Hub model uses the implementation of AVA_cifar from the MindSpore model zoo on Gitee at research/cv/AVA_cifar. 47 | 48 | AVA_cifar is a cv network. More details please refer to the MindSpore model zoo on Gitee at [research/cv/AVA_cifar](https://gitee.com/mindspore/models/blob/r1.6/research/cv/AVA_cifar/README.md). 49 | 50 | All parameters in the module are trainable. 51 | 52 | ## Disclaimer 53 | 54 | MindSpore ("we") do not own any ownership or intellectual property rights of the datasets, and the trained models are provided on an "as is" and "as available" basis. We make no representations or warranties of any kind of the datasets and trained models (collectively, “materials”) and will not be liable for any loss, damage, expense or cost arising from the materials. Please ensure that you have permission to use the dataset under the appropriate license for the dataset and in accordance with the terms of the relevant license agreement. The trained models provided are only for research and education purposes. 55 | 56 | To Dataset Owners: If you do not wish to have a dataset included in MindSpore, or wish to update it in any way, we will remove or update the content at your request. Please contact us through GitHub or Gitee. Your understanding and contributions to the community are greatly appreciated. 57 | 58 | MindSpore is available under the Apache 2.0 license, please see the LICENSE file. 59 | -------------------------------------------------------------------------------- /mshub_res/assets/mindspore/1.6/avahpa_hpa.md: -------------------------------------------------------------------------------- 1 | # AVA_hpa 2 | 3 | --- 4 | 5 | model-name: AVA_hpa 6 | 7 | backbone-name: AVA_hpa 8 | 9 | module-type: cv 10 | 11 | fine-tunable: True 12 | 13 | model-version: 1.6 14 | 15 | train-dataset: hpa 16 | 17 | evaluation: macroF1acc70.05 | microF1acc77.97 | auc94.23 18 | 19 | author: MindSpore team 20 | 21 | update-time: 2022-04-18 22 | 23 | repo-link: 24 | 25 | user-id: MindSpore 26 | 27 | used-for: inference 28 | 29 | mindspore-version: 1.6 30 | 31 | asset: 32 | 33 | - 34 | file-format: ckpt 35 | asset-link: 36 | asset-sha256: 50d6c1d61a750a3524076c1088bda669d492713a6b8be9d71dd9c6b5e2656102 37 | 38 | license: Apache2.0 39 | 40 | summary: AVA_hpa is used for cv 41 | 42 | --- 43 | 44 | ## Introduction 45 | 46 | This MindSpore Hub model uses the implementation of AVA_hpa from the MindSpore model zoo on Gitee at research/cv/AVA_hpa. 47 | 48 | AVA_hpa is a cv network. More details please refer to the MindSpore model zoo on Gitee at [research/cv/AVA_hpa](https://gitee.com/mindspore/models/blob/r1.6/research/cv/AVA_hpa/README.md). 49 | 50 | All parameters in the module are trainable. 51 | 52 | ## Disclaimer 53 | 54 | MindSpore ("we") do not own any ownership or intellectual property rights of the datasets, and the trained models are provided on an "as is" and "as available" basis. We make no representations or warranties of any kind of the datasets and trained models (collectively, “materials”) and will not be liable for any loss, damage, expense or cost arising from the materials. Please ensure that you have permission to use the dataset under the appropriate license for the dataset and in accordance with the terms of the relevant license agreement. The trained models provided are only for research and education purposes. 55 | 56 | To Dataset Owners: If you do not wish to have a dataset included in MindSpore, or wish to update it in any way, we will remove or update the content at your request. Please contact us through GitHub or Gitee. Your understanding and contributions to the community are greatly appreciated. 57 | 58 | MindSpore is available under the Apache 2.0 license, please see the LICENSE file. 59 | -------------------------------------------------------------------------------- /mshub_res/assets/mindspore/1.6/c3d_hmdb51.md: -------------------------------------------------------------------------------- 1 | # c3d 2 | 3 | --- 4 | 5 | model-name: c3d 6 | 7 | backbone-name: c3d 8 | 9 | module-type: cv 10 | 11 | fine-tunable: True 12 | 13 | model-version: 1.6 14 | 15 | train-dataset: hmdb51 16 | 17 | evaluation: top1acc49.67 18 | 19 | author: MindSpore team 20 | 21 | update-time: 2022-03-30 22 | 23 | repo-link: 24 | 25 | user-id: MindSpore 26 | 27 | used-for: inference 28 | 29 | mindspore-version: 1.6 30 | 31 | asset: 32 | 33 | - 34 | file-format: ckpt 35 | asset-link: 36 | asset-sha256: 76ca14dbd7c499255af90cee53f9df858632989a6deef1be0a56f63d319b9789 37 | 38 | license: Apache2.0 39 | 40 | summary: c3d is used for cv 41 | 42 | --- 43 | 44 | ## Introduction 45 | 46 | This MindSpore Hub model uses the implementation of c3d from the MindSpore model zoo on Gitee at official/cv/c3d. 47 | 48 | c3d is a cv network. More details please refer to the MindSpore model zoo on Gitee at [official/cv/c3d](https://gitee.com/mindspore/models/blob/r1.6/official/cv/c3d/README.md). 49 | 50 | All parameters in the module are trainable. 51 | 52 | ## Citation 53 | 54 | Learning Spatiotemporal Features with 3D Convolutional Networks 55 | 56 | ## Disclaimer 57 | 58 | MindSpore ("we") do not own any ownership or intellectual property rights of the datasets, and the trained models are provided on an "as is" and "as available" basis. We make no representations or warranties of any kind of the datasets and trained models (collectively, “materials”) and will not be liable for any loss, damage, expense or cost arising from the materials. Please ensure that you have permission to use the dataset under the appropriate license for the dataset and in accordance with the terms of the relevant license agreement. The trained models provided are only for research and education purposes. 59 | 60 | To Dataset Owners: If you do not wish to have a dataset included in MindSpore, or wish to update it in any way, we will remove or update the content at your request. Please contact us through GitHub or Gitee. Your understanding and contributions to the community are greatly appreciated. 61 | 62 | MindSpore is available under the Apache 2.0 license, please see the LICENSE file. 63 | -------------------------------------------------------------------------------- /mshub_res/assets/mindspore/1.6/c3d_ucf101.md: -------------------------------------------------------------------------------- 1 | # c3d 2 | 3 | --- 4 | 5 | model-name: c3d 6 | 7 | backbone-name: c3d 8 | 9 | module-type: cv 10 | 11 | fine-tunable: True 12 | 13 | model-version: 1.6 14 | 15 | train-dataset: ucf101 16 | 17 | evaluation: top1acc79.51 18 | 19 | author: MindSpore team 20 | 21 | update-time: 2022-04-12 22 | 23 | repo-link: 24 | 25 | user-id: MindSpore 26 | 27 | used-for: inference 28 | 29 | mindspore-version: 1.6 30 | 31 | asset: 32 | 33 | - 34 | file-format: ckpt 35 | asset-link: 36 | asset-sha256: 44e707636ce4ada89ee1f97ce1fecbe1a0373b7dfe21a6d711c1db827676002f 37 | 38 | license: Apache2.0 39 | 40 | summary: c3d is used for cv 41 | 42 | --- 43 | 44 | ## Introduction 45 | 46 | This MindSpore Hub model uses the implementation of c3d from the MindSpore model zoo on Gitee at official/cv/c3d. 47 | 48 | c3d is a cv network. More details please refer to the MindSpore model zoo on Gitee at [official/cv/c3d](https://gitee.com/mindspore/models/blob/r1.6/official/cv/c3d/README.md). 49 | 50 | All parameters in the module are trainable. 51 | 52 | ## Citation 53 | 54 | Learning Spatiotemporal Features with 3D Convolutional Networks 55 | 56 | ## Disclaimer 57 | 58 | MindSpore ("we") do not own any ownership or intellectual property rights of the datasets, and the trained models are provided on an "as is" and "as available" basis. We make no representations or warranties of any kind of the datasets and trained models (collectively, “materials”) and will not be liable for any loss, damage, expense or cost arising from the materials. Please ensure that you have permission to use the dataset under the appropriate license for the dataset and in accordance with the terms of the relevant license agreement. The trained models provided are only for research and education purposes. 59 | 60 | To Dataset Owners: If you do not wish to have a dataset included in MindSpore, or wish to update it in any way, we will remove or update the content at your request. Please contact us through GitHub or Gitee. Your understanding and contributions to the community are greatly appreciated. 61 | 62 | MindSpore is available under the Apache 2.0 license, please see the LICENSE file. 63 | -------------------------------------------------------------------------------- /mshub_res/assets/mindspore/1.6/cct_imagenet2012.md: -------------------------------------------------------------------------------- 1 | # cct 2 | 3 | --- 4 | 5 | model-name: cct 6 | 7 | backbone-name: cct 8 | 9 | module-type: cv 10 | 11 | fine-tunable: True 12 | 13 | model-version: 1.6 14 | 15 | train-dataset: imagenet2012 16 | 17 | evaluation: top1acc80.24 | top5acc94.93 18 | 19 | author: MindSpore team 20 | 21 | update-time: 2022-05-12 22 | 23 | repo-link: 24 | 25 | user-id: MindSpore 26 | 27 | used-for: inference 28 | 29 | mindspore-version: 1.6 30 | 31 | asset: 32 | 33 | - 34 | file-format: ckpt 35 | asset-link: 36 | asset-sha256: 7df9c4c1e9d190f289aadfe79fdbef5c330228b1694230eeedf552dc15188261 37 | 38 | license: Apache2.0 39 | 40 | summary: cct is used for cv 41 | 42 | --- 43 | 44 | ## Introduction 45 | 46 | This MindSpore Hub model uses the implementation of cct from the MindSpore model zoo on Gitee at research/cv/cct. 47 | 48 | cct is a cv network. More details please refer to the MindSpore model zoo on Gitee at [research/cv/cct](https://gitee.com/mindspore/models/blob/r1.6/research/cv/cct/README_CN.md). 49 | 50 | All parameters in the module are trainable. 51 | 52 | ## Disclaimer 53 | 54 | MindSpore ("we") do not own any ownership or intellectual property rights of the datasets, and the trained models are provided on an "as is" and "as available" basis. We make no representations or warranties of any kind of the datasets and trained models (collectively, “materials”) and will not be liable for any loss, damage, expense or cost arising from the materials. Please ensure that you have permission to use the dataset under the appropriate license for the dataset and in accordance with the terms of the relevant license agreement. The trained models provided are only for research and education purposes. 55 | 56 | To Dataset Owners: If you do not wish to have a dataset included in MindSpore, or wish to update it in any way, we will remove or update the content at your request. Please contact us through GitHub or Gitee. Your understanding and contributions to the community are greatly appreciated. 57 | 58 | MindSpore is available under the Apache 2.0 license, please see the LICENSE file. 59 | -------------------------------------------------------------------------------- /mshub_res/assets/mindspore/1.6/cgan_G_mnist.md: -------------------------------------------------------------------------------- 1 | # CGAN 2 | 3 | --- 4 | 5 | model-name: CGAN 6 | 7 | backbone-name: CGAN 8 | 9 | module-type: cv 10 | 11 | fine-tunable: True 12 | 13 | model-version: 1.6 14 | 15 | train-dataset: mnist 16 | 17 | evaluation: - 18 | 19 | author: MindSpore team 20 | 21 | update-time: 2022-03-30 22 | 23 | repo-link: 24 | 25 | user-id: MindSpore 26 | 27 | used-for: inference 28 | 29 | mindspore-version: 1.6 30 | 31 | asset: 32 | 33 | - 34 | file-format: ckpt 35 | asset-link: 36 | asset-sha256: 360a455b190fc1a56cfb487ed25278794e86ea615458cf3066ca06e3b8ad3d41 37 | 38 | license: Apache2.0 39 | 40 | summary: CGAN is used for cv 41 | 42 | --- 43 | 44 | ## Introduction 45 | 46 | This MindSpore Hub model uses the implementation of CGAN from the MindSpore model zoo on Gitee at research/cv/CGAN. 47 | 48 | CGAN is a cv network. More details please refer to the MindSpore model zoo on Gitee at [research/cv/CGAN](https://gitee.com/mindspore/models/blob/r1.6/research/cv/CGAN/README.md). 49 | 50 | All parameters in the module are trainable. 51 | 52 | ## Citation 53 | 54 | Conditional Generative Adversarial Nets. 55 | 56 | ## Disclaimer 57 | 58 | MindSpore ("we") do not own any ownership or intellectual property rights of the datasets, and the trained models are provided on an "as is" and "as available" basis. We make no representations or warranties of any kind of the datasets and trained models (collectively, “materials”) and will not be liable for any loss, damage, expense or cost arising from the materials. Please ensure that you have permission to use the dataset under the appropriate license for the dataset and in accordance with the terms of the relevant license agreement. The trained models provided are only for research and education purposes. 59 | 60 | To Dataset Owners: If you do not wish to have a dataset included in MindSpore, or wish to update it in any way, we will remove or update the content at your request. Please contact us through GitHub or Gitee. Your understanding and contributions to the community are greatly appreciated. 61 | 62 | MindSpore is available under the Apache 2.0 license, please see the LICENSE file. 63 | -------------------------------------------------------------------------------- /mshub_res/assets/mindspore/1.6/dbpn_div2k.md: -------------------------------------------------------------------------------- 1 | # DBPN 2 | 3 | --- 4 | 5 | model-name: DBPN 6 | 7 | backbone-name: DBPN 8 | 9 | module-type: cv 10 | 11 | fine-tunable: True 12 | 13 | model-version: 1.6 14 | 15 | train-dataset: div2k 16 | 17 | evaluation: PSNR31.93 18 | 19 | author: MindSpore team 20 | 21 | update-time: 2022-03-30 22 | 23 | repo-link: 24 | 25 | user-id: MindSpore 26 | 27 | used-for: inference 28 | 29 | mindspore-version: 1.6 30 | 31 | asset: 32 | 33 | - 34 | file-format: ckpt 35 | asset-link: 36 | asset-sha256: 75d74c46e4efc3a4f2e81df53a3ab8817618d8193a6032a971fe02a5e307ef77 37 | 38 | license: Apache2.0 39 | 40 | summary: DBPN is used for cv 41 | 42 | --- 43 | 44 | ## Introduction 45 | 46 | This MindSpore Hub model uses the implementation of DBPN from the MindSpore model zoo on Gitee at research/cv/DBPN. 47 | 48 | DBPN is a cv network. More details please refer to the MindSpore model zoo on Gitee at [research/cv/DBPN](https://gitee.com/mindspore/models/blob/r1.6/research/cv/DBPN/README.md). 49 | 50 | All parameters in the module are trainable. 51 | 52 | ## Citation 53 | 54 | Muhammad Haris, Greg Shakhnarovich, Norimichi Ukita 55 | 56 | ## Disclaimer 57 | 58 | MindSpore ("we") do not own any ownership or intellectual property rights of the datasets, and the trained models are provided on an "as is" and "as available" basis. We make no representations or warranties of any kind of the datasets and trained models (collectively, “materials”) and will not be liable for any loss, damage, expense or cost arising from the materials. Please ensure that you have permission to use the dataset under the appropriate license for the dataset and in accordance with the terms of the relevant license agreement. The trained models provided are only for research and education purposes. 59 | 60 | To Dataset Owners: If you do not wish to have a dataset included in MindSpore, or wish to update it in any way, we will remove or update the content at your request. Please contact us through GitHub or Gitee. Your understanding and contributions to the community are greatly appreciated. 61 | 62 | MindSpore is available under the Apache 2.0 license, please see the LICENSE file. 63 | -------------------------------------------------------------------------------- /mshub_res/assets/mindspore/1.6/dbpngan_G_div2k.md: -------------------------------------------------------------------------------- 1 | # DBPN 2 | 3 | --- 4 | 5 | model-name: DBPN 6 | 7 | backbone-name: DBPN 8 | 9 | module-type: cv 10 | 11 | fine-tunable: True 12 | 13 | model-version: 1.6 14 | 15 | train-dataset: div2k 16 | 17 | evaluation: PSNR29.23 18 | 19 | author: MindSpore team 20 | 21 | update-time: 2022-03-30 22 | 23 | repo-link: 24 | 25 | user-id: MindSpore 26 | 27 | used-for: inference 28 | 29 | mindspore-version: 1.6 30 | 31 | asset: 32 | 33 | - 34 | file-format: ckpt 35 | asset-link: 36 | asset-sha256: 00ad7584fde0e5a926596ed91bebd5c13aa04086c22ac6def15a9d14dcd7eec1 37 | 38 | license: Apache2.0 39 | 40 | summary: DBPN is used for cv 41 | 42 | --- 43 | 44 | ## Introduction 45 | 46 | This MindSpore Hub model uses the implementation of DBPN from the MindSpore model zoo on Gitee at research/cv/DBPN. 47 | 48 | DBPN is a cv network. More details please refer to the MindSpore model zoo on Gitee at [research/cv/DBPN](https://gitee.com/mindspore/models/blob/r1.6/research/cv/DBPN/README.md). 49 | 50 | All parameters in the module are trainable. 51 | 52 | ## Citation 53 | 54 | Muhammad Haris, Greg Shakhnarovich, Norimichi Ukita 55 | 56 | ## Disclaimer 57 | 58 | MindSpore ("we") do not own any ownership or intellectual property rights of the datasets, and the trained models are provided on an "as is" and "as available" basis. We make no representations or warranties of any kind of the datasets and trained models (collectively, “materials”) and will not be liable for any loss, damage, expense or cost arising from the materials. Please ensure that you have permission to use the dataset under the appropriate license for the dataset and in accordance with the terms of the relevant license agreement. The trained models provided are only for research and education purposes. 59 | 60 | To Dataset Owners: If you do not wish to have a dataset included in MindSpore, or wish to update it in any way, we will remove or update the content at your request. Please contact us through GitHub or Gitee. Your understanding and contributions to the community are greatly appreciated. 61 | 62 | MindSpore is available under the Apache 2.0 license, please see the LICENSE file. 63 | -------------------------------------------------------------------------------- /mshub_res/assets/mindspore/1.6/ddrnet_imagenet2012.md: -------------------------------------------------------------------------------- 1 | # DDRNet 2 | 3 | --- 4 | 5 | model-name: DDRNet 6 | 7 | backbone-name: DDRNet 8 | 9 | module-type: cv 10 | 11 | fine-tunable: True 12 | 13 | model-version: 1.6 14 | 15 | train-dataset: imagenet2012 16 | 17 | evaluation: top1acc76.36 | top5acc93.24 18 | 19 | author: MindSpore team 20 | 21 | update-time: 2022-05-12 22 | 23 | repo-link: 24 | 25 | user-id: MindSpore 26 | 27 | used-for: inference 28 | 29 | mindspore-version: 1.6 30 | 31 | asset: 32 | 33 | - 34 | file-format: ckpt 35 | asset-link: 36 | asset-sha256: 6acbc32e242e674a61b812adfb1a7972348e013f0c8bd0beccaeca0446305997 37 | 38 | license: Apache2.0 39 | 40 | summary: DDRNet is used for cv 41 | 42 | --- 43 | 44 | ## Introduction 45 | 46 | This MindSpore Hub model uses the implementation of DDRNet from the MindSpore model zoo on Gitee at research/cv/DDRNet. 47 | 48 | DDRNet is a cv network. More details please refer to the MindSpore model zoo on Gitee at [research/cv/DDRNet](https://gitee.com/mindspore/models/blob/r1.6/research/cv/DDRNet/README_CN.md). 49 | 50 | All parameters in the module are trainable. 51 | 52 | ## Disclaimer 53 | 54 | MindSpore ("we") do not own any ownership or intellectual property rights of the datasets, and the trained models are provided on an "as is" and "as available" basis. We make no representations or warranties of any kind of the datasets and trained models (collectively, “materials”) and will not be liable for any loss, damage, expense or cost arising from the materials. Please ensure that you have permission to use the dataset under the appropriate license for the dataset and in accordance with the terms of the relevant license agreement. The trained models provided are only for research and education purposes. 55 | 56 | To Dataset Owners: If you do not wish to have a dataset included in MindSpore, or wish to update it in any way, we will remove or update the content at your request. Please contact us through GitHub or Gitee. Your understanding and contributions to the community are greatly appreciated. 57 | 58 | MindSpore is available under the Apache 2.0 license, please see the LICENSE file. 59 | -------------------------------------------------------------------------------- /mshub_res/assets/mindspore/1.6/dgu_udc.md: -------------------------------------------------------------------------------- 1 | # dgu 2 | 3 | --- 4 | 5 | model-name: dgu 6 | 7 | backbone-name: dgu 8 | 9 | module-type: nlp 10 | 11 | fine-tunable: True 12 | 13 | model-version: 1.6 14 | 15 | train-dataset: udc 16 | 17 | evaluation: R1acc82.14 | R2acc90.45 | R5acc97.75 18 | 19 | author: MindSpore team 20 | 21 | update-time: 2022-03-30 22 | 23 | repo-link: 24 | 25 | user-id: MindSpore 26 | 27 | used-for: inference 28 | 29 | mindspore-version: 1.6 30 | 31 | asset: 32 | 33 | - 34 | file-format: ckpt 35 | asset-link: 36 | asset-sha256: ef323d57071720e70df35c28d0d8cffcfafdefdfe694ca4c39b21d013f2b8542 37 | 38 | license: Apache2.0 39 | 40 | summary: dgu is used for nlp 41 | 42 | --- 43 | 44 | ## Introduction 45 | 46 | This MindSpore Hub model uses the implementation of dgu from the MindSpore model zoo on Gitee at official/nlp/dgu. 47 | 48 | dgu is a nlp network. More details please refer to the MindSpore model zoo on Gitee at [official/nlp/dgu](https://gitee.com/mindspore/models/blob/r1.6/official/nlp/dgu/README_CN.md). 49 | 50 | All parameters in the module are trainable. 51 | 52 | ## Disclaimer 53 | 54 | MindSpore ("we") do not own any ownership or intellectual property rights of the datasets, and the trained models are provided on an "as is" and "as available" basis. We make no representations or warranties of any kind of the datasets and trained models (collectively, “materials”) and will not be liable for any loss, damage, expense or cost arising from the materials. Please ensure that you have permission to use the dataset under the appropriate license for the dataset and in accordance with the terms of the relevant license agreement. The trained models provided are only for research and education purposes. 55 | 56 | To Dataset Owners: If you do not wish to have a dataset included in MindSpore, or wish to update it in any way, we will remove or update the content at your request. Please contact us through GitHub or Gitee. Your understanding and contributions to the community are greatly appreciated. 57 | 58 | MindSpore is available under the Apache 2.0 license, please see the LICENSE file. 59 | -------------------------------------------------------------------------------- /mshub_res/assets/mindspore/1.6/emotect_baidu.md: -------------------------------------------------------------------------------- 1 | # emotect 2 | 3 | --- 4 | 5 | model-name: emotect 6 | 7 | backbone-name: emotect 8 | 9 | module-type: nlp 10 | 11 | fine-tunable: True 12 | 13 | model-version: 1.6 14 | 15 | train-dataset: baidu 16 | 17 | evaluation: acc90.44 18 | 19 | author: MindSpore team 20 | 21 | update-time: 2022-05-12 22 | 23 | repo-link: 24 | 25 | user-id: MindSpore 26 | 27 | used-for: inference 28 | 29 | mindspore-version: 1.6 30 | 31 | asset: 32 | 33 | - 34 | file-format: ckpt 35 | asset-link: 36 | asset-sha256: 5b0ddb04f17f27d706007353d15faf93b627dcaf0ce7fcfcf1b61d4f2ddc228b 37 | 38 | license: Apache2.0 39 | 40 | summary: emotect is used for nlp 41 | 42 | --- 43 | 44 | ## Introduction 45 | 46 | This MindSpore Hub model uses the implementation of emotect from the MindSpore model zoo on Gitee at official/nlp/emotect. 47 | 48 | emotect is a nlp network. More details please refer to the MindSpore model zoo on Gitee at [official/nlp/emotect](https://gitee.com/mindspore/models/blob/r1.6/official/nlp/emotect/README_CN.md). 49 | 50 | All parameters in the module are trainable. 51 | 52 | ## Disclaimer 53 | 54 | MindSpore ("we") do not own any ownership or intellectual property rights of the datasets, and the trained models are provided on an "as is" and "as available" basis. We make no representations or warranties of any kind of the datasets and trained models (collectively, “materials”) and will not be liable for any loss, damage, expense or cost arising from the materials. Please ensure that you have permission to use the dataset under the appropriate license for the dataset and in accordance with the terms of the relevant license agreement. The trained models provided are only for research and education purposes. 55 | 56 | To Dataset Owners: If you do not wish to have a dataset included in MindSpore, or wish to update it in any way, we will remove or update the content at your request. Please contact us through GitHub or Gitee. Your understanding and contributions to the community are greatly appreciated. 57 | 58 | MindSpore is available under the Apache 2.0 license, please see the LICENSE file. 59 | -------------------------------------------------------------------------------- /mshub_res/assets/mindspore/1.6/nfnet_imagenet2012.md: -------------------------------------------------------------------------------- 1 | # NFNet 2 | 3 | --- 4 | 5 | model-name: NFNet 6 | 7 | backbone-name: NFNet 8 | 9 | module-type: cv 10 | 11 | fine-tunable: True 12 | 13 | model-version: 1.6 14 | 15 | train-dataset: imagenet2012 16 | 17 | evaluation: top1acc83.26 18 | 19 | author: MindSpore team 20 | 21 | update-time: 2022-04-24 22 | 23 | repo-link: 24 | 25 | user-id: MindSpore 26 | 27 | used-for: inference 28 | 29 | mindspore-version: 1.6 30 | 31 | asset: 32 | 33 | - 34 | file-format: ckpt 35 | asset-link: 36 | asset-sha256: 773b0098a124e13beaee3a14b3dfc09b73139cc185f11fc43d9d2124d6905c26 37 | 38 | license: Apache2.0 39 | 40 | summary: NFNet is used for cv 41 | 42 | --- 43 | 44 | ## Introduction 45 | 46 | This MindSpore Hub model uses the implementation of NFNet from the MindSpore model zoo on Gitee at research/cv/NFNet. 47 | 48 | NFNet is a cv network. More details please refer to the MindSpore model zoo on Gitee at [research/cv/NFNet](https://gitee.com/mindspore/models/blob/r1.6/research/cv/NFNet/README_CN.md). 49 | 50 | All parameters in the module are trainable. 51 | 52 | ## Disclaimer 53 | 54 | MindSpore ("we") do not own any ownership or intellectual property rights of the datasets, and the trained models are provided on an "as is" and "as available" basis. We make no representations or warranties of any kind of the datasets and trained models (collectively, “materials”) and will not be liable for any loss, damage, expense or cost arising from the materials. Please ensure that you have permission to use the dataset under the appropriate license for the dataset and in accordance with the terms of the relevant license agreement. The trained models provided are only for research and education purposes. 55 | 56 | To Dataset Owners: If you do not wish to have a dataset included in MindSpore, or wish to update it in any way, we will remove or update the content at your request. Please contact us through GitHub or Gitee. Your understanding and contributions to the community are greatly appreciated. 57 | 58 | MindSpore is available under the Apache 2.0 license, please see the LICENSE file. 59 | -------------------------------------------------------------------------------- /mshub_res/assets/mindspore/1.6/rdn_div2k.md: -------------------------------------------------------------------------------- 1 | # RDN 2 | 3 | --- 4 | 5 | model-name: RDN 6 | 7 | backbone-name: RDN 8 | 9 | module-type: cv 10 | 11 | fine-tunable: True 12 | 13 | model-version: 1.6 14 | 15 | train-dataset: div2k 16 | 17 | evaluation: PSNR38.2 | SSIM0.9611 18 | 19 | author: MindSpore team 20 | 21 | update-time: 2022-07-04 22 | 23 | repo-link: 24 | 25 | user-id: MindSpore 26 | 27 | used-for: inference 28 | 29 | mindspore-version: 1.6 30 | 31 | asset: 32 | 33 | - 34 | file-format: ckpt 35 | asset-link: 36 | asset-sha256: 497b58c96ae06c2dc30813a98ddf17dabc8bcad23baa8a546d98fdd09cab4c07 37 | 38 | license: Apache2.0 39 | 40 | summary: RDN is used for cv 41 | 42 | --- 43 | 44 | ## Introduction 45 | 46 | This MindSpore Hub model uses the implementation of RDN from the MindSpore model zoo on Gitee at official/cv/RDN. 47 | 48 | RDN is a cv network. More details please refer to the MindSpore model zoo on Gitee at [official/cv/RDN](https://gitee.com/mindspore/models/blob/r1.6/official/cv/RDN/README.md). 49 | 50 | All parameters in the module are trainable. 51 | 52 | ## Disclaimer 53 | 54 | MindSpore ("we") do not own any ownership or intellectual property rights of the datasets, and the trained models are provided on an "as is" and "as available" basis. We make no representations or warranties of any kind of the datasets and trained models (collectively, “materials”) and will not be liable for any loss, damage, expense or cost arising from the materials. Please ensure that you have permission to use the dataset under the appropriate license for the dataset and in accordance with the terms of the relevant license agreement. The trained models provided are only for research and education purposes. 55 | 56 | To Dataset Owners: If you do not wish to have a dataset included in MindSpore, or wish to update it in any way, we will remove or update the content at your request. Please contact us through GitHub or Gitee. Your understanding and contributions to the community are greatly appreciated. 57 | 58 | MindSpore is available under the Apache 2.0 license, please see the LICENSE file. 59 | -------------------------------------------------------------------------------- /mshub_res/assets/mindspore/1.6/tnt_imagenet2012.md: -------------------------------------------------------------------------------- 1 | # TNT 2 | 3 | --- 4 | 5 | model-name: TNT 6 | 7 | backbone-name: TNT 8 | 9 | module-type: cv 10 | 11 | fine-tunable: True 12 | 13 | model-version: 1.6 14 | 15 | train-dataset: imagenet2012 16 | 17 | evaluation: top1acc81.03 | top5acc95.41 18 | 19 | author: MindSpore team 20 | 21 | update-time: 2022-05-12 22 | 23 | repo-link: 24 | 25 | user-id: MindSpore 26 | 27 | used-for: inference 28 | 29 | mindspore-version: 1.6 30 | 31 | asset: 32 | 33 | - 34 | file-format: ckpt 35 | asset-link: 36 | asset-sha256: 94dd6f5b772d6b87c3cb017beffe9c5784a237d7e08217355828084690eb90c5 37 | 38 | license: Apache2.0 39 | 40 | summary: TNT is used for cv 41 | 42 | --- 43 | 44 | ## Introduction 45 | 46 | This MindSpore Hub model uses the implementation of TNT from the MindSpore model zoo on Gitee at research/cv/TNT. 47 | 48 | TNT is a cv network. More details please refer to the MindSpore model zoo on Gitee at [research/cv/TNT](https://gitee.com/mindspore/models/blob/r1.6/research/cv/TNT/README_CN.md). 49 | 50 | All parameters in the module are trainable. 51 | 52 | ## Disclaimer 53 | 54 | MindSpore ("we") do not own any ownership or intellectual property rights of the datasets, and the trained models are provided on an "as is" and "as available" basis. We make no representations or warranties of any kind of the datasets and trained models (collectively, “materials”) and will not be liable for any loss, damage, expense or cost arising from the materials. Please ensure that you have permission to use the dataset under the appropriate license for the dataset and in accordance with the terms of the relevant license agreement. The trained models provided are only for research and education purposes. 55 | 56 | To Dataset Owners: If you do not wish to have a dataset included in MindSpore, or wish to update it in any way, we will remove or update the content at your request. Please contact us through GitHub or Gitee. Your understanding and contributions to the community are greatly appreciated. 57 | 58 | MindSpore is available under the Apache 2.0 license, please see the LICENSE file. 59 | -------------------------------------------------------------------------------- /mshub_res/assets/mindspore/1.7/3ddensenet_iseg2017.md: -------------------------------------------------------------------------------- 1 | # 3D_DenseNet 2 | 3 | --- 4 | 5 | model-name: 3D_DenseNet 6 | 7 | backbone-name: 3D_DenseNet 8 | 9 | module-type: cv 10 | 11 | fine-tunable: True 12 | 13 | model-version: 1.7 14 | 15 | train-dataset: iseg2017 16 | 17 | evaluation: acc92 18 | 19 | author: MindSpore team 20 | 21 | update-time: 2022-06-22 22 | 23 | repo-link: 24 | 25 | user-id: MindSpore 26 | 27 | used-for: inference 28 | 29 | mindspore-version: 1.7 30 | 31 | asset: 32 | 33 | - 34 | file-format: ckpt 35 | asset-link: 36 | asset-sha256: 81196ef44f3db51e3d22b69d01373c162639fa31362dd71388906fc307ccc3fe 37 | 38 | license: Apache2.0 39 | 40 | summary: 3D_DenseNet is used for cv 41 | 42 | --- 43 | 44 | ## Introduction 45 | 46 | This MindSpore Hub model uses the implementation of 3D_DenseNet from the MindSpore model zoo on Gitee at research/cv/3D_DenseNet. 47 | 48 | 3D_DenseNet is a cv network. More details please refer to the MindSpore model zoo on Gitee at [research/cv/3D_DenseNet](https://gitee.com/mindspore/models/blob/r1.7/research/cv/3D_DenseNet/README.md). 49 | 50 | All parameters in the module are trainable. 51 | 52 | ## Disclaimer 53 | 54 | MindSpore ("we") do not own any ownership or intellectual property rights of the datasets, and the trained models are provided on an "as is" and "as available" basis. We make no representations or warranties of any kind of the datasets and trained models (collectively, “materials”) and will not be liable for any loss, damage, expense or cost arising from the materials. Please ensure that you have permission to use the dataset under the appropriate license for the dataset and in accordance with the terms of the relevant license agreement. The trained models provided are only for research and education purposes. 55 | 56 | To Dataset Owners: If you do not wish to have a dataset included in MindSpore, or wish to update it in any way, we will remove or update the content at your request. Please contact us through GitHub or Gitee. Your understanding and contributions to the community are greatly appreciated. 57 | 58 | MindSpore is available under the Apache 2.0 license, please see the LICENSE file. 59 | -------------------------------------------------------------------------------- /mshub_res/assets/mindspore/1.7/avacifar_cifar10.md: -------------------------------------------------------------------------------- 1 | # AVA_cifar 2 | 3 | --- 4 | 5 | model-name: AVA_cifar 6 | 7 | backbone-name: AVA_cifar 8 | 9 | module-type: cv 10 | 11 | fine-tunable: True 12 | 13 | model-version: 1.7 14 | 15 | train-dataset: cifar10 16 | 17 | evaluation: top1acc90.87 18 | 19 | author: MindSpore team 20 | 21 | update-time: 2022-06-22 22 | 23 | repo-link: 24 | 25 | user-id: MindSpore 26 | 27 | used-for: inference 28 | 29 | mindspore-version: 1.7 30 | 31 | asset: 32 | 33 | - 34 | file-format: ckpt 35 | asset-link: 36 | asset-sha256: 955f6c567108af733ea82f8efb1eec0accd44cbc79a41b2d216ca83d09a646c6 37 | 38 | license: Apache2.0 39 | 40 | summary: AVA_cifar is used for cv 41 | 42 | --- 43 | 44 | ## Introduction 45 | 46 | This MindSpore Hub model uses the implementation of AVA_cifar from the MindSpore model zoo on Gitee at research/cv/AVA_cifar. 47 | 48 | AVA_cifar is a cv network. More details please refer to the MindSpore model zoo on Gitee at [research/cv/AVA_cifar](https://gitee.com/mindspore/models/blob/r1.7/research/cv/AVA_cifar/README.md). 49 | 50 | All parameters in the module are trainable. 51 | 52 | ## Disclaimer 53 | 54 | MindSpore ("we") do not own any ownership or intellectual property rights of the datasets, and the trained models are provided on an "as is" and "as available" basis. We make no representations or warranties of any kind of the datasets and trained models (collectively, “materials”) and will not be liable for any loss, damage, expense or cost arising from the materials. Please ensure that you have permission to use the dataset under the appropriate license for the dataset and in accordance with the terms of the relevant license agreement. The trained models provided are only for research and education purposes. 55 | 56 | To Dataset Owners: If you do not wish to have a dataset included in MindSpore, or wish to update it in any way, we will remove or update the content at your request. Please contact us through GitHub or Gitee. Your understanding and contributions to the community are greatly appreciated. 57 | 58 | MindSpore is available under the Apache 2.0 license, please see the LICENSE file. 59 | -------------------------------------------------------------------------------- /mshub_res/assets/mindspore/1.7/avahpa_hpa.md: -------------------------------------------------------------------------------- 1 | # AVA_hpa 2 | 3 | --- 4 | 5 | model-name: AVA_hpa 6 | 7 | backbone-name: AVA_hpa 8 | 9 | module-type: cv 10 | 11 | fine-tunable: True 12 | 13 | model-version: 1.7 14 | 15 | train-dataset: hpa 16 | 17 | evaluation: macroF1acc70.05 | microF1acc77.97 | auc94.23 18 | 19 | author: MindSpore team 20 | 21 | update-time: 2022-06-22 22 | 23 | repo-link: 24 | 25 | user-id: MindSpore 26 | 27 | used-for: inference 28 | 29 | mindspore-version: 1.7 30 | 31 | asset: 32 | 33 | - 34 | file-format: ckpt 35 | asset-link: 36 | asset-sha256: 50d6c1d61a750a3524076c1088bda669d492713a6b8be9d71dd9c6b5e2656102 37 | 38 | license: Apache2.0 39 | 40 | summary: AVA_hpa is used for cv 41 | 42 | --- 43 | 44 | ## Introduction 45 | 46 | This MindSpore Hub model uses the implementation of AVA_hpa from the MindSpore model zoo on Gitee at research/cv/AVA_hpa. 47 | 48 | AVA_hpa is a cv network. More details please refer to the MindSpore model zoo on Gitee at [research/cv/AVA_hpa](https://gitee.com/mindspore/models/blob/r1.7/research/cv/AVA_hpa/README.md). 49 | 50 | All parameters in the module are trainable. 51 | 52 | ## Disclaimer 53 | 54 | MindSpore ("we") do not own any ownership or intellectual property rights of the datasets, and the trained models are provided on an "as is" and "as available" basis. We make no representations or warranties of any kind of the datasets and trained models (collectively, “materials”) and will not be liable for any loss, damage, expense or cost arising from the materials. Please ensure that you have permission to use the dataset under the appropriate license for the dataset and in accordance with the terms of the relevant license agreement. The trained models provided are only for research and education purposes. 55 | 56 | To Dataset Owners: If you do not wish to have a dataset included in MindSpore, or wish to update it in any way, we will remove or update the content at your request. Please contact us through GitHub or Gitee. Your understanding and contributions to the community are greatly appreciated. 57 | 58 | MindSpore is available under the Apache 2.0 license, please see the LICENSE file. 59 | -------------------------------------------------------------------------------- /mshub_res/assets/mindspore/1.7/c3d_hmdb51.md: -------------------------------------------------------------------------------- 1 | # c3d 2 | 3 | --- 4 | 5 | model-name: c3d 6 | 7 | backbone-name: c3d 8 | 9 | module-type: cv 10 | 11 | fine-tunable: True 12 | 13 | model-version: 1.7 14 | 15 | train-dataset: hmdb51 16 | 17 | evaluation: top1acc49.67 18 | 19 | author: MindSpore team 20 | 21 | update-time: 2022-06-22 22 | 23 | repo-link: 24 | 25 | user-id: MindSpore 26 | 27 | used-for: inference 28 | 29 | mindspore-version: 1.7 30 | 31 | asset: 32 | 33 | - 34 | file-format: ckpt 35 | asset-link: 36 | asset-sha256: 76ca14dbd7c499255af90cee53f9df858632989a6deef1be0a56f63d319b9789 37 | 38 | license: Apache2.0 39 | 40 | summary: c3d is used for cv 41 | 42 | --- 43 | 44 | ## Introduction 45 | 46 | This MindSpore Hub model uses the implementation of c3d from the MindSpore model zoo on Gitee at official/cv/c3d. 47 | 48 | c3d is a cv network. More details please refer to the MindSpore model zoo on Gitee at [official/cv/c3d](https://gitee.com/mindspore/models/blob/r1.7/official/cv/c3d/README.md). 49 | 50 | All parameters in the module are trainable. 51 | 52 | ## Citation 53 | 54 | Learning Spatiotemporal Features with 3D Convolutional Networks 55 | 56 | ## Disclaimer 57 | 58 | MindSpore ("we") do not own any ownership or intellectual property rights of the datasets, and the trained models are provided on an "as is" and "as available" basis. We make no representations or warranties of any kind of the datasets and trained models (collectively, “materials”) and will not be liable for any loss, damage, expense or cost arising from the materials. Please ensure that you have permission to use the dataset under the appropriate license for the dataset and in accordance with the terms of the relevant license agreement. The trained models provided are only for research and education purposes. 59 | 60 | To Dataset Owners: If you do not wish to have a dataset included in MindSpore, or wish to update it in any way, we will remove or update the content at your request. Please contact us through GitHub or Gitee. Your understanding and contributions to the community are greatly appreciated. 61 | 62 | MindSpore is available under the Apache 2.0 license, please see the LICENSE file. 63 | -------------------------------------------------------------------------------- /mshub_res/assets/mindspore/1.7/c3d_ucf101.md: -------------------------------------------------------------------------------- 1 | # c3d 2 | 3 | --- 4 | 5 | model-name: c3d 6 | 7 | backbone-name: c3d 8 | 9 | module-type: cv 10 | 11 | fine-tunable: True 12 | 13 | model-version: 1.7 14 | 15 | train-dataset: ucf101 16 | 17 | evaluation: top1acc79.51 18 | 19 | author: MindSpore team 20 | 21 | update-time: 2022-06-22 22 | 23 | repo-link: 24 | 25 | user-id: MindSpore 26 | 27 | used-for: inference 28 | 29 | mindspore-version: 1.7 30 | 31 | asset: 32 | 33 | - 34 | file-format: ckpt 35 | asset-link: 36 | asset-sha256: 44e707636ce4ada89ee1f97ce1fecbe1a0373b7dfe21a6d711c1db827676002f 37 | 38 | license: Apache2.0 39 | 40 | summary: c3d is used for cv 41 | 42 | --- 43 | 44 | ## Introduction 45 | 46 | This MindSpore Hub model uses the implementation of c3d from the MindSpore model zoo on Gitee at official/cv/c3d. 47 | 48 | c3d is a cv network. More details please refer to the MindSpore model zoo on Gitee at [official/cv/c3d](https://gitee.com/mindspore/models/blob/r1.7/official/cv/c3d/README.md). 49 | 50 | All parameters in the module are trainable. 51 | 52 | ## Citation 53 | 54 | Learning Spatiotemporal Features with 3D Convolutional Networks 55 | 56 | ## Disclaimer 57 | 58 | MindSpore ("we") do not own any ownership or intellectual property rights of the datasets, and the trained models are provided on an "as is" and "as available" basis. We make no representations or warranties of any kind of the datasets and trained models (collectively, “materials”) and will not be liable for any loss, damage, expense or cost arising from the materials. Please ensure that you have permission to use the dataset under the appropriate license for the dataset and in accordance with the terms of the relevant license agreement. The trained models provided are only for research and education purposes. 59 | 60 | To Dataset Owners: If you do not wish to have a dataset included in MindSpore, or wish to update it in any way, we will remove or update the content at your request. Please contact us through GitHub or Gitee. Your understanding and contributions to the community are greatly appreciated. 61 | 62 | MindSpore is available under the Apache 2.0 license, please see the LICENSE file. 63 | -------------------------------------------------------------------------------- /mshub_res/assets/mindspore/1.7/cct_imagenet2012.md: -------------------------------------------------------------------------------- 1 | # cct 2 | 3 | --- 4 | 5 | model-name: cct 6 | 7 | backbone-name: cct 8 | 9 | module-type: cv 10 | 11 | fine-tunable: True 12 | 13 | model-version: 1.7 14 | 15 | train-dataset: imagenet2012 16 | 17 | evaluation: top1acc80.24 | top5acc94.93 18 | 19 | author: MindSpore team 20 | 21 | update-time: 2022-06-22 22 | 23 | repo-link: 24 | 25 | user-id: MindSpore 26 | 27 | used-for: inference 28 | 29 | mindspore-version: 1.7 30 | 31 | asset: 32 | 33 | - 34 | file-format: ckpt 35 | asset-link: 36 | asset-sha256: 7df9c4c1e9d190f289aadfe79fdbef5c330228b1694230eeedf552dc15188261 37 | 38 | license: Apache2.0 39 | 40 | summary: cct is used for cv 41 | 42 | --- 43 | 44 | ## Introduction 45 | 46 | This MindSpore Hub model uses the implementation of cct from the MindSpore model zoo on Gitee at research/cv/cct. 47 | 48 | cct is a cv network. More details please refer to the MindSpore model zoo on Gitee at [research/cv/cct](https://gitee.com/mindspore/models/blob/r1.7/research/cv/cct/README_CN.md). 49 | 50 | All parameters in the module are trainable. 51 | 52 | ## Disclaimer 53 | 54 | MindSpore ("we") do not own any ownership or intellectual property rights of the datasets, and the trained models are provided on an "as is" and "as available" basis. We make no representations or warranties of any kind of the datasets and trained models (collectively, “materials”) and will not be liable for any loss, damage, expense or cost arising from the materials. Please ensure that you have permission to use the dataset under the appropriate license for the dataset and in accordance with the terms of the relevant license agreement. The trained models provided are only for research and education purposes. 55 | 56 | To Dataset Owners: If you do not wish to have a dataset included in MindSpore, or wish to update it in any way, we will remove or update the content at your request. Please contact us through GitHub or Gitee. Your understanding and contributions to the community are greatly appreciated. 57 | 58 | MindSpore is available under the Apache 2.0 license, please see the LICENSE file. 59 | -------------------------------------------------------------------------------- /mshub_res/assets/mindspore/1.7/cgan_G_mnist.md: -------------------------------------------------------------------------------- 1 | # CGAN 2 | 3 | --- 4 | 5 | model-name: CGAN 6 | 7 | backbone-name: CGAN 8 | 9 | module-type: cv 10 | 11 | fine-tunable: True 12 | 13 | model-version: 1.7 14 | 15 | train-dataset: mnist 16 | 17 | evaluation: - 18 | 19 | author: MindSpore team 20 | 21 | update-time: 2022-06-22 22 | 23 | repo-link: 24 | 25 | user-id: MindSpore 26 | 27 | used-for: inference 28 | 29 | mindspore-version: 1.7 30 | 31 | asset: 32 | 33 | - 34 | file-format: ckpt 35 | asset-link: 36 | asset-sha256: 360a455b190fc1a56cfb487ed25278794e86ea615458cf3066ca06e3b8ad3d41 37 | 38 | license: Apache2.0 39 | 40 | summary: CGAN is used for cv 41 | 42 | --- 43 | 44 | ## Introduction 45 | 46 | This MindSpore Hub model uses the implementation of CGAN from the MindSpore model zoo on Gitee at research/cv/CGAN. 47 | 48 | CGAN is a cv network. More details please refer to the MindSpore model zoo on Gitee at [research/cv/CGAN](https://gitee.com/mindspore/models/blob/r1.7/research/cv/CGAN/README.md). 49 | 50 | All parameters in the module are trainable. 51 | 52 | ## Citation 53 | 54 | Conditional Generative Adversarial Nets. 55 | 56 | ## Disclaimer 57 | 58 | MindSpore ("we") do not own any ownership or intellectual property rights of the datasets, and the trained models are provided on an "as is" and "as available" basis. We make no representations or warranties of any kind of the datasets and trained models (collectively, “materials”) and will not be liable for any loss, damage, expense or cost arising from the materials. Please ensure that you have permission to use the dataset under the appropriate license for the dataset and in accordance with the terms of the relevant license agreement. The trained models provided are only for research and education purposes. 59 | 60 | To Dataset Owners: If you do not wish to have a dataset included in MindSpore, or wish to update it in any way, we will remove or update the content at your request. Please contact us through GitHub or Gitee. Your understanding and contributions to the community are greatly appreciated. 61 | 62 | MindSpore is available under the Apache 2.0 license, please see the LICENSE file. 63 | -------------------------------------------------------------------------------- /mshub_res/assets/mindspore/1.7/dbpn_div2k.md: -------------------------------------------------------------------------------- 1 | # DBPN 2 | 3 | --- 4 | 5 | model-name: DBPN 6 | 7 | backbone-name: DBPN 8 | 9 | module-type: cv 10 | 11 | fine-tunable: True 12 | 13 | model-version: 1.7 14 | 15 | train-dataset: div2k 16 | 17 | evaluation: PSNR31.93 18 | 19 | author: MindSpore team 20 | 21 | update-time: 2022-06-22 22 | 23 | repo-link: 24 | 25 | user-id: MindSpore 26 | 27 | used-for: inference 28 | 29 | mindspore-version: 1.7 30 | 31 | asset: 32 | 33 | - 34 | file-format: ckpt 35 | asset-link: 36 | asset-sha256: 75d74c46e4efc3a4f2e81df53a3ab8817618d8193a6032a971fe02a5e307ef77 37 | 38 | license: Apache2.0 39 | 40 | summary: DBPN is used for cv 41 | 42 | --- 43 | 44 | ## Introduction 45 | 46 | This MindSpore Hub model uses the implementation of DBPN from the MindSpore model zoo on Gitee at research/cv/DBPN. 47 | 48 | DBPN is a cv network. More details please refer to the MindSpore model zoo on Gitee at [research/cv/DBPN](https://gitee.com/mindspore/models/blob/r1.7/research/cv/DBPN/README.md). 49 | 50 | All parameters in the module are trainable. 51 | 52 | ## Citation 53 | 54 | Muhammad Haris, Greg Shakhnarovich, Norimichi Ukita 55 | 56 | ## Disclaimer 57 | 58 | MindSpore ("we") do not own any ownership or intellectual property rights of the datasets, and the trained models are provided on an "as is" and "as available" basis. We make no representations or warranties of any kind of the datasets and trained models (collectively, “materials”) and will not be liable for any loss, damage, expense or cost arising from the materials. Please ensure that you have permission to use the dataset under the appropriate license for the dataset and in accordance with the terms of the relevant license agreement. The trained models provided are only for research and education purposes. 59 | 60 | To Dataset Owners: If you do not wish to have a dataset included in MindSpore, or wish to update it in any way, we will remove or update the content at your request. Please contact us through GitHub or Gitee. Your understanding and contributions to the community are greatly appreciated. 61 | 62 | MindSpore is available under the Apache 2.0 license, please see the LICENSE file. 63 | -------------------------------------------------------------------------------- /mshub_res/assets/mindspore/1.7/dbpngan_G_div2k.md: -------------------------------------------------------------------------------- 1 | # DBPN 2 | 3 | --- 4 | 5 | model-name: DBPN 6 | 7 | backbone-name: DBPN 8 | 9 | module-type: cv 10 | 11 | fine-tunable: True 12 | 13 | model-version: 1.7 14 | 15 | train-dataset: div2k 16 | 17 | evaluation: PSNR29.23 18 | 19 | author: MindSpore team 20 | 21 | update-time: 2022-06-22 22 | 23 | repo-link: 24 | 25 | user-id: MindSpore 26 | 27 | used-for: inference 28 | 29 | mindspore-version: 1.7 30 | 31 | asset: 32 | 33 | - 34 | file-format: ckpt 35 | asset-link: 36 | asset-sha256: 00ad7584fde0e5a926596ed91bebd5c13aa04086c22ac6def15a9d14dcd7eec1 37 | 38 | license: Apache2.0 39 | 40 | summary: DBPN is used for cv 41 | 42 | --- 43 | 44 | ## Introduction 45 | 46 | This MindSpore Hub model uses the implementation of DBPN from the MindSpore model zoo on Gitee at research/cv/DBPN. 47 | 48 | DBPN is a cv network. More details please refer to the MindSpore model zoo on Gitee at [research/cv/DBPN](https://gitee.com/mindspore/models/blob/r1.7/research/cv/DBPN/README.md). 49 | 50 | All parameters in the module are trainable. 51 | 52 | ## Citation 53 | 54 | Muhammad Haris, Greg Shakhnarovich, Norimichi Ukita 55 | 56 | ## Disclaimer 57 | 58 | MindSpore ("we") do not own any ownership or intellectual property rights of the datasets, and the trained models are provided on an "as is" and "as available" basis. We make no representations or warranties of any kind of the datasets and trained models (collectively, “materials”) and will not be liable for any loss, damage, expense or cost arising from the materials. Please ensure that you have permission to use the dataset under the appropriate license for the dataset and in accordance with the terms of the relevant license agreement. The trained models provided are only for research and education purposes. 59 | 60 | To Dataset Owners: If you do not wish to have a dataset included in MindSpore, or wish to update it in any way, we will remove or update the content at your request. Please contact us through GitHub or Gitee. Your understanding and contributions to the community are greatly appreciated. 61 | 62 | MindSpore is available under the Apache 2.0 license, please see the LICENSE file. 63 | -------------------------------------------------------------------------------- /mshub_res/assets/mindspore/1.7/ddrnet_imagenet2012.md: -------------------------------------------------------------------------------- 1 | # DDRNet 2 | 3 | --- 4 | 5 | model-name: DDRNet 6 | 7 | backbone-name: DDRNet 8 | 9 | module-type: cv 10 | 11 | fine-tunable: True 12 | 13 | model-version: 1.7 14 | 15 | train-dataset: imagenet2012 16 | 17 | evaluation: top1acc76.36 | top5acc93.24 18 | 19 | author: MindSpore team 20 | 21 | update-time: 2022-06-22 22 | 23 | repo-link: 24 | 25 | user-id: MindSpore 26 | 27 | used-for: inference 28 | 29 | mindspore-version: 1.7 30 | 31 | asset: 32 | 33 | - 34 | file-format: ckpt 35 | asset-link: 36 | asset-sha256: 6acbc32e242e674a61b812adfb1a7972348e013f0c8bd0beccaeca0446305997 37 | 38 | license: Apache2.0 39 | 40 | summary: DDRNet is used for cv 41 | 42 | --- 43 | 44 | ## Introduction 45 | 46 | This MindSpore Hub model uses the implementation of DDRNet from the MindSpore model zoo on Gitee at research/cv/DDRNet. 47 | 48 | DDRNet is a cv network. More details please refer to the MindSpore model zoo on Gitee at [research/cv/DDRNet](https://gitee.com/mindspore/models/blob/r1.7/research/cv/DDRNet/README_CN.md). 49 | 50 | All parameters in the module are trainable. 51 | 52 | ## Disclaimer 53 | 54 | MindSpore ("we") do not own any ownership or intellectual property rights of the datasets, and the trained models are provided on an "as is" and "as available" basis. We make no representations or warranties of any kind of the datasets and trained models (collectively, “materials”) and will not be liable for any loss, damage, expense or cost arising from the materials. Please ensure that you have permission to use the dataset under the appropriate license for the dataset and in accordance with the terms of the relevant license agreement. The trained models provided are only for research and education purposes. 55 | 56 | To Dataset Owners: If you do not wish to have a dataset included in MindSpore, or wish to update it in any way, we will remove or update the content at your request. Please contact us through GitHub or Gitee. Your understanding and contributions to the community are greatly appreciated. 57 | 58 | MindSpore is available under the Apache 2.0 license, please see the LICENSE file. 59 | -------------------------------------------------------------------------------- /mshub_res/assets/mindspore/1.7/dgu_udc.md: -------------------------------------------------------------------------------- 1 | # dgu 2 | 3 | --- 4 | 5 | model-name: dgu 6 | 7 | backbone-name: dgu 8 | 9 | module-type: nlp 10 | 11 | fine-tunable: True 12 | 13 | model-version: 1.7 14 | 15 | train-dataset: udc 16 | 17 | evaluation: R1acc82.14 | R2acc90.45 | R5acc97.75 18 | 19 | author: MindSpore team 20 | 21 | update-time: 2022-06-22 22 | 23 | repo-link: 24 | 25 | user-id: MindSpore 26 | 27 | used-for: inference 28 | 29 | mindspore-version: 1.7 30 | 31 | asset: 32 | 33 | - 34 | file-format: ckpt 35 | asset-link: 36 | asset-sha256: ef323d57071720e70df35c28d0d8cffcfafdefdfe694ca4c39b21d013f2b8542 37 | 38 | license: Apache2.0 39 | 40 | summary: dgu is used for nlp 41 | 42 | --- 43 | 44 | ## Introduction 45 | 46 | This MindSpore Hub model uses the implementation of dgu from the MindSpore model zoo on Gitee at official/nlp/dgu. 47 | 48 | dgu is a nlp network. More details please refer to the MindSpore model zoo on Gitee at [official/nlp/dgu](https://gitee.com/mindspore/models/blob/r1.7/official/nlp/dgu/README_CN.md). 49 | 50 | All parameters in the module are trainable. 51 | 52 | ## Disclaimer 53 | 54 | MindSpore ("we") do not own any ownership or intellectual property rights of the datasets, and the trained models are provided on an "as is" and "as available" basis. We make no representations or warranties of any kind of the datasets and trained models (collectively, “materials”) and will not be liable for any loss, damage, expense or cost arising from the materials. Please ensure that you have permission to use the dataset under the appropriate license for the dataset and in accordance with the terms of the relevant license agreement. The trained models provided are only for research and education purposes. 55 | 56 | To Dataset Owners: If you do not wish to have a dataset included in MindSpore, or wish to update it in any way, we will remove or update the content at your request. Please contact us through GitHub or Gitee. Your understanding and contributions to the community are greatly appreciated. 57 | 58 | MindSpore is available under the Apache 2.0 license, please see the LICENSE file. 59 | -------------------------------------------------------------------------------- /mshub_res/assets/mindspore/1.7/emotect_baidu.md: -------------------------------------------------------------------------------- 1 | # emotect 2 | 3 | --- 4 | 5 | model-name: emotect 6 | 7 | backbone-name: emotect 8 | 9 | module-type: nlp 10 | 11 | fine-tunable: True 12 | 13 | model-version: 1.7 14 | 15 | train-dataset: baidu 16 | 17 | evaluation: acc90.44 18 | 19 | author: MindSpore team 20 | 21 | update-time: 2022-06-22 22 | 23 | repo-link: 24 | 25 | user-id: MindSpore 26 | 27 | used-for: inference 28 | 29 | mindspore-version: 1.7 30 | 31 | asset: 32 | 33 | - 34 | file-format: ckpt 35 | asset-link: 36 | asset-sha256: 5b0ddb04f17f27d706007353d15faf93b627dcaf0ce7fcfcf1b61d4f2ddc228b 37 | 38 | license: Apache2.0 39 | 40 | summary: emotect is used for nlp 41 | 42 | --- 43 | 44 | ## Introduction 45 | 46 | This MindSpore Hub model uses the implementation of emotect from the MindSpore model zoo on Gitee at official/nlp/emotect. 47 | 48 | emotect is a nlp network. More details please refer to the MindSpore model zoo on Gitee at [official/nlp/emotect](https://gitee.com/mindspore/models/blob/r1.7/official/nlp/emotect/README_CN.md). 49 | 50 | All parameters in the module are trainable. 51 | 52 | ## Disclaimer 53 | 54 | MindSpore ("we") do not own any ownership or intellectual property rights of the datasets, and the trained models are provided on an "as is" and "as available" basis. We make no representations or warranties of any kind of the datasets and trained models (collectively, “materials”) and will not be liable for any loss, damage, expense or cost arising from the materials. Please ensure that you have permission to use the dataset under the appropriate license for the dataset and in accordance with the terms of the relevant license agreement. The trained models provided are only for research and education purposes. 55 | 56 | To Dataset Owners: If you do not wish to have a dataset included in MindSpore, or wish to update it in any way, we will remove or update the content at your request. Please contact us through GitHub or Gitee. Your understanding and contributions to the community are greatly appreciated. 57 | 58 | MindSpore is available under the Apache 2.0 license, please see the LICENSE file. 59 | -------------------------------------------------------------------------------- /mshub_res/assets/mindspore/1.7/nfnet_imagenet2012.md: -------------------------------------------------------------------------------- 1 | # NFNet 2 | 3 | --- 4 | 5 | model-name: NFNet 6 | 7 | backbone-name: NFNet 8 | 9 | module-type: cv 10 | 11 | fine-tunable: True 12 | 13 | model-version: 1.7 14 | 15 | train-dataset: imagenet2012 16 | 17 | evaluation: top1acc83.26 18 | 19 | author: MindSpore team 20 | 21 | update-time: 2022-06-22 22 | 23 | repo-link: 24 | 25 | user-id: MindSpore 26 | 27 | used-for: inference 28 | 29 | mindspore-version: 1.7 30 | 31 | asset: 32 | 33 | - 34 | file-format: ckpt 35 | asset-link: 36 | asset-sha256: 773b0098a124e13beaee3a14b3dfc09b73139cc185f11fc43d9d2124d6905c26 37 | 38 | license: Apache2.0 39 | 40 | summary: NFNet is used for cv 41 | 42 | --- 43 | 44 | ## Introduction 45 | 46 | This MindSpore Hub model uses the implementation of NFNet from the MindSpore model zoo on Gitee at research/cv/NFNet. 47 | 48 | NFNet is a cv network. More details please refer to the MindSpore model zoo on Gitee at [research/cv/NFNet](https://gitee.com/mindspore/models/blob/r1.7/research/cv/NFNet/README_CN.md). 49 | 50 | All parameters in the module are trainable. 51 | 52 | ## Disclaimer 53 | 54 | MindSpore ("we") do not own any ownership or intellectual property rights of the datasets, and the trained models are provided on an "as is" and "as available" basis. We make no representations or warranties of any kind of the datasets and trained models (collectively, “materials”) and will not be liable for any loss, damage, expense or cost arising from the materials. Please ensure that you have permission to use the dataset under the appropriate license for the dataset and in accordance with the terms of the relevant license agreement. The trained models provided are only for research and education purposes. 55 | 56 | To Dataset Owners: If you do not wish to have a dataset included in MindSpore, or wish to update it in any way, we will remove or update the content at your request. Please contact us through GitHub or Gitee. Your understanding and contributions to the community are greatly appreciated. 57 | 58 | MindSpore is available under the Apache 2.0 license, please see the LICENSE file. 59 | -------------------------------------------------------------------------------- /mshub_res/assets/mindspore/1.7/rdn_div2k.md: -------------------------------------------------------------------------------- 1 | # RDN 2 | 3 | --- 4 | 5 | model-name: RDN 6 | 7 | backbone-name: RDN 8 | 9 | module-type: cv 10 | 11 | fine-tunable: True 12 | 13 | model-version: 1.7 14 | 15 | train-dataset: div2k 16 | 17 | evaluation: psnr38.3 | ssim0.961 18 | 19 | author: MindSpore team 20 | 21 | update-time: 2022-06-22 22 | 23 | repo-link: 24 | 25 | user-id: MindSpore 26 | 27 | used-for: inference 28 | 29 | mindspore-version: 1.7 30 | 31 | asset: 32 | 33 | - 34 | file-format: ckpt 35 | asset-link: 36 | asset-sha256: 36cbce25c04256f1b0652c3f25ba704faf135dc2ad395020de3b8db7e05e23ef 37 | 38 | license: Apache2.0 39 | 40 | summary: RDN is used for cv 41 | 42 | --- 43 | 44 | ## Introduction 45 | 46 | This MindSpore Hub model uses the implementation of RDN from the MindSpore model zoo on Gitee at official/cv/RDN. 47 | 48 | RDN is a cv network. More details please refer to the MindSpore model zoo on Gitee at [official/cv/RDN](https://gitee.com/mindspore/models/blob/r1.7/official/cv/RDN/README.md). 49 | 50 | All parameters in the module are trainable. 51 | 52 | ## Disclaimer 53 | 54 | MindSpore ("we") do not own any ownership or intellectual property rights of the datasets, and the trained models are provided on an "as is" and "as available" basis. We make no representations or warranties of any kind of the datasets and trained models (collectively, “materials”) and will not be liable for any loss, damage, expense or cost arising from the materials. Please ensure that you have permission to use the dataset under the appropriate license for the dataset and in accordance with the terms of the relevant license agreement. The trained models provided are only for research and education purposes. 55 | 56 | To Dataset Owners: If you do not wish to have a dataset included in MindSpore, or wish to update it in any way, we will remove or update the content at your request. Please contact us through GitHub or Gitee. Your understanding and contributions to the community are greatly appreciated. 57 | 58 | MindSpore is available under the Apache 2.0 license, please see the LICENSE file. 59 | -------------------------------------------------------------------------------- /mshub_res/assets/mindspore/1.7/tnt_imagenet2012.md: -------------------------------------------------------------------------------- 1 | # TNT 2 | 3 | --- 4 | 5 | model-name: TNT 6 | 7 | backbone-name: TNT 8 | 9 | module-type: cv 10 | 11 | fine-tunable: True 12 | 13 | model-version: 1.7 14 | 15 | train-dataset: imagenet2012 16 | 17 | evaluation: top1acc81.03 | top5acc95.41 18 | 19 | author: MindSpore team 20 | 21 | update-time: 2022-06-22 22 | 23 | repo-link: 24 | 25 | user-id: MindSpore 26 | 27 | used-for: inference 28 | 29 | mindspore-version: 1.7 30 | 31 | asset: 32 | 33 | - 34 | file-format: ckpt 35 | asset-link: 36 | asset-sha256: 94dd6f5b772d6b87c3cb017beffe9c5784a237d7e08217355828084690eb90c5 37 | 38 | license: Apache2.0 39 | 40 | summary: TNT is used for cv 41 | 42 | --- 43 | 44 | ## Introduction 45 | 46 | This MindSpore Hub model uses the implementation of TNT from the MindSpore model zoo on Gitee at research/cv/TNT. 47 | 48 | TNT is a cv network. More details please refer to the MindSpore model zoo on Gitee at [research/cv/TNT](https://gitee.com/mindspore/models/blob/r1.7/research/cv/TNT/README_CN.md). 49 | 50 | All parameters in the module are trainable. 51 | 52 | ## Disclaimer 53 | 54 | MindSpore ("we") do not own any ownership or intellectual property rights of the datasets, and the trained models are provided on an "as is" and "as available" basis. We make no representations or warranties of any kind of the datasets and trained models (collectively, “materials”) and will not be liable for any loss, damage, expense or cost arising from the materials. Please ensure that you have permission to use the dataset under the appropriate license for the dataset and in accordance with the terms of the relevant license agreement. The trained models provided are only for research and education purposes. 55 | 56 | To Dataset Owners: If you do not wish to have a dataset included in MindSpore, or wish to update it in any way, we will remove or update the content at your request. Please contact us through GitHub or Gitee. Your understanding and contributions to the community are greatly appreciated. 57 | 58 | MindSpore is available under the Apache 2.0 license, please see the LICENSE file. 59 | -------------------------------------------------------------------------------- /mshub_res/assets/mindspore/1.7/yolox_coco2017.md: -------------------------------------------------------------------------------- 1 | # yolox 2 | 3 | --- 4 | 5 | model-name: yolox 6 | 7 | backbone-name: yolox 8 | 9 | module-type: cv 10 | 11 | fine-tunable: True 12 | 13 | model-version: 1.7 14 | 15 | train-dataset: coco2017 16 | 17 | evaluation: map47.3 18 | 19 | author: MindSpore team 20 | 21 | update-time: 2022-06-22 22 | 23 | repo-link: 24 | 25 | user-id: MindSpore 26 | 27 | used-for: inference 28 | 29 | mindspore-version: 1.7 30 | 31 | asset: 32 | 33 | - 34 | file-format: ckpt 35 | asset-link: 36 | asset-sha256: 31ee25297c89aa6632de562c891a8fb8f85cfb52d86a2145de303452b591cdb6 37 | 38 | license: Apache2.0 39 | 40 | summary: yolox is used for cv 41 | 42 | --- 43 | 44 | ## Introduction 45 | 46 | This MindSpore Hub model uses the implementation of yolox from the MindSpore model zoo on Gitee at research/cv/yolox. 47 | 48 | yolox is a cv network. More details please refer to the MindSpore model zoo on Gitee at [research/cv/yolox](https://gitee.com/mindspore/models/blob/r1.7/research/cv/yolox/README_CN.md). 49 | 50 | All parameters in the module are trainable. 51 | 52 | ## Citation 53 | 54 | YOLOX: Exceeding YOLO Series in 2021 55 | 56 | ## Disclaimer 57 | 58 | MindSpore ("we") do not own any ownership or intellectual property rights of the datasets, and the trained models are provided on an "as is" and "as available" basis. We make no representations or warranties of any kind of the datasets and trained models (collectively, “materials”) and will not be liable for any loss, damage, expense or cost arising from the materials. Please ensure that you have permission to use the dataset under the appropriate license for the dataset and in accordance with the terms of the relevant license agreement. The trained models provided are only for research and education purposes. 59 | 60 | To Dataset Owners: If you do not wish to have a dataset included in MindSpore, or wish to update it in any way, we will remove or update the content at your request. Please contact us through GitHub or Gitee. Your understanding and contributions to the community are greatly appreciated. 61 | 62 | MindSpore is available under the Apache 2.0 license, please see the LICENSE file. 63 | -------------------------------------------------------------------------------- /mshub_res/assets/mindspore/1.8/3ddensenet_iseg2017.md: -------------------------------------------------------------------------------- 1 | # 3D_DenseNet 2 | 3 | --- 4 | 5 | model-name: 3D_DenseNet 6 | 7 | backbone-name: 3D_DenseNet 8 | 9 | module-type: cv 10 | 11 | fine-tunable: True 12 | 13 | model-version: 1.8 14 | 15 | train-dataset: iseg2017 16 | 17 | evaluation: acc92 18 | 19 | author: MindSpore team 20 | 21 | update-time: 2022-07-19 22 | 23 | repo-link: 24 | 25 | user-id: MindSpore 26 | 27 | used-for: inference 28 | 29 | mindspore-version: 1.8 30 | 31 | asset: 32 | 33 | - 34 | file-format: ckpt 35 | asset-link: 36 | asset-sha256: 81196ef44f3db51e3d22b69d01373c162639fa31362dd71388906fc307ccc3fe 37 | 38 | license: Apache2.0 39 | 40 | summary: 3D_DenseNet is used for cv 41 | 42 | --- 43 | 44 | ## Introduction 45 | 46 | This MindSpore Hub model uses the implementation of 3D_DenseNet from the MindSpore model zoo on Gitee at research/cv/3D_DenseNet. 47 | 48 | 3D_DenseNet is a cv network. More details please refer to the MindSpore model zoo on Gitee at [research/cv/3D_DenseNet](https://gitee.com/mindspore/models/blob/r1.8/research/cv/3D_DenseNet/README.md). 49 | 50 | All parameters in the module are trainable. 51 | 52 | ## Disclaimer 53 | 54 | MindSpore ("we") do not own any ownership or intellectual property rights of the datasets, and the trained models are provided on an "as is" and "as available" basis. We make no representations or warranties of any kind of the datasets and trained models (collectively, “materials”) and will not be liable for any loss, damage, expense or cost arising from the materials. Please ensure that you have permission to use the dataset under the appropriate license for the dataset and in accordance with the terms of the relevant license agreement. The trained models provided are only for research and education purposes. 55 | 56 | To Dataset Owners: If you do not wish to have a dataset included in MindSpore, or wish to update it in any way, we will remove or update the content at your request. Please contact us through GitHub or Gitee. Your understanding and contributions to the community are greatly appreciated. 57 | 58 | MindSpore is available under the Apache 2.0 license, please see the LICENSE file. 59 | -------------------------------------------------------------------------------- /mshub_res/assets/mindspore/1.8/avacifar_cifar10.md: -------------------------------------------------------------------------------- 1 | # AVA_cifar 2 | 3 | --- 4 | 5 | model-name: AVA_cifar 6 | 7 | backbone-name: AVA_cifar 8 | 9 | module-type: cv 10 | 11 | fine-tunable: True 12 | 13 | model-version: 1.8 14 | 15 | train-dataset: cifar10 16 | 17 | evaluation: top1acc90.87 18 | 19 | author: MindSpore team 20 | 21 | update-time: 2022-07-19 22 | 23 | repo-link: 24 | 25 | user-id: MindSpore 26 | 27 | used-for: inference 28 | 29 | mindspore-version: 1.8 30 | 31 | asset: 32 | 33 | - 34 | file-format: ckpt 35 | asset-link: 36 | asset-sha256: 955f6c567108af733ea82f8efb1eec0accd44cbc79a41b2d216ca83d09a646c6 37 | 38 | license: Apache2.0 39 | 40 | summary: AVA_cifar is used for cv 41 | 42 | --- 43 | 44 | ## Introduction 45 | 46 | This MindSpore Hub model uses the implementation of AVA_cifar from the MindSpore model zoo on Gitee at research/cv/AVA_cifar. 47 | 48 | AVA_cifar is a cv network. More details please refer to the MindSpore model zoo on Gitee at [research/cv/AVA_cifar](https://gitee.com/mindspore/models/blob/r1.8/research/cv/AVA_cifar/README.md). 49 | 50 | All parameters in the module are trainable. 51 | 52 | ## Disclaimer 53 | 54 | MindSpore ("we") do not own any ownership or intellectual property rights of the datasets, and the trained models are provided on an "as is" and "as available" basis. We make no representations or warranties of any kind of the datasets and trained models (collectively, “materials”) and will not be liable for any loss, damage, expense or cost arising from the materials. Please ensure that you have permission to use the dataset under the appropriate license for the dataset and in accordance with the terms of the relevant license agreement. The trained models provided are only for research and education purposes. 55 | 56 | To Dataset Owners: If you do not wish to have a dataset included in MindSpore, or wish to update it in any way, we will remove or update the content at your request. Please contact us through GitHub or Gitee. Your understanding and contributions to the community are greatly appreciated. 57 | 58 | MindSpore is available under the Apache 2.0 license, please see the LICENSE file. 59 | -------------------------------------------------------------------------------- /mshub_res/assets/mindspore/1.8/avahpa_hpa.md: -------------------------------------------------------------------------------- 1 | # AVA_hpa 2 | 3 | --- 4 | 5 | model-name: AVA_hpa 6 | 7 | backbone-name: AVA_hpa 8 | 9 | module-type: cv 10 | 11 | fine-tunable: True 12 | 13 | model-version: 1.8 14 | 15 | train-dataset: hpa 16 | 17 | evaluation: macroF1acc70.05 | microF1acc77.97 | auc94.23 18 | 19 | author: MindSpore team 20 | 21 | update-time: 2022-07-19 22 | 23 | repo-link: 24 | 25 | user-id: MindSpore 26 | 27 | used-for: inference 28 | 29 | mindspore-version: 1.8 30 | 31 | asset: 32 | 33 | - 34 | file-format: ckpt 35 | asset-link: 36 | asset-sha256: 50d6c1d61a750a3524076c1088bda669d492713a6b8be9d71dd9c6b5e2656102 37 | 38 | license: Apache2.0 39 | 40 | summary: AVA_hpa is used for cv 41 | 42 | --- 43 | 44 | ## Introduction 45 | 46 | This MindSpore Hub model uses the implementation of AVA_hpa from the MindSpore model zoo on Gitee at research/cv/AVA_hpa. 47 | 48 | AVA_hpa is a cv network. More details please refer to the MindSpore model zoo on Gitee at [research/cv/AVA_hpa](https://gitee.com/mindspore/models/blob/r1.8/research/cv/AVA_hpa/README.md). 49 | 50 | All parameters in the module are trainable. 51 | 52 | ## Disclaimer 53 | 54 | MindSpore ("we") do not own any ownership or intellectual property rights of the datasets, and the trained models are provided on an "as is" and "as available" basis. We make no representations or warranties of any kind of the datasets and trained models (collectively, “materials”) and will not be liable for any loss, damage, expense or cost arising from the materials. Please ensure that you have permission to use the dataset under the appropriate license for the dataset and in accordance with the terms of the relevant license agreement. The trained models provided are only for research and education purposes. 55 | 56 | To Dataset Owners: If you do not wish to have a dataset included in MindSpore, or wish to update it in any way, we will remove or update the content at your request. Please contact us through GitHub or Gitee. Your understanding and contributions to the community are greatly appreciated. 57 | 58 | MindSpore is available under the Apache 2.0 license, please see the LICENSE file. 59 | -------------------------------------------------------------------------------- /mshub_res/assets/mindspore/1.8/c3d_hmdb51.md: -------------------------------------------------------------------------------- 1 | # c3d 2 | 3 | --- 4 | 5 | model-name: c3d 6 | 7 | backbone-name: c3d 8 | 9 | module-type: cv 10 | 11 | fine-tunable: True 12 | 13 | model-version: 1.8 14 | 15 | train-dataset: hmdb51 16 | 17 | evaluation: top1acc49.67 18 | 19 | author: MindSpore team 20 | 21 | update-time: 2022-07-19 22 | 23 | repo-link: 24 | 25 | user-id: MindSpore 26 | 27 | used-for: inference 28 | 29 | mindspore-version: 1.8 30 | 31 | asset: 32 | 33 | - 34 | file-format: ckpt 35 | asset-link: 36 | asset-sha256: 76ca14dbd7c499255af90cee53f9df858632989a6deef1be0a56f63d319b9789 37 | 38 | license: Apache2.0 39 | 40 | summary: c3d is used for cv 41 | 42 | --- 43 | 44 | ## Introduction 45 | 46 | This MindSpore Hub model uses the implementation of c3d from the MindSpore model zoo on Gitee at official/cv/c3d. 47 | 48 | c3d is a cv network. More details please refer to the MindSpore model zoo on Gitee at [official/cv/c3d](https://gitee.com/mindspore/models/blob/r1.8/official/cv/c3d/README.md). 49 | 50 | All parameters in the module are trainable. 51 | 52 | ## Citation 53 | 54 | Learning Spatiotemporal Features with 3D Convolutional Networks 55 | 56 | ## Disclaimer 57 | 58 | MindSpore ("we") do not own any ownership or intellectual property rights of the datasets, and the trained models are provided on an "as is" and "as available" basis. We make no representations or warranties of any kind of the datasets and trained models (collectively, “materials”) and will not be liable for any loss, damage, expense or cost arising from the materials. Please ensure that you have permission to use the dataset under the appropriate license for the dataset and in accordance with the terms of the relevant license agreement. The trained models provided are only for research and education purposes. 59 | 60 | To Dataset Owners: If you do not wish to have a dataset included in MindSpore, or wish to update it in any way, we will remove or update the content at your request. Please contact us through GitHub or Gitee. Your understanding and contributions to the community are greatly appreciated. 61 | 62 | MindSpore is available under the Apache 2.0 license, please see the LICENSE file. 63 | -------------------------------------------------------------------------------- /mshub_res/assets/mindspore/1.8/c3d_ucf101.md: -------------------------------------------------------------------------------- 1 | # c3d 2 | 3 | --- 4 | 5 | model-name: c3d 6 | 7 | backbone-name: c3d 8 | 9 | module-type: cv 10 | 11 | fine-tunable: True 12 | 13 | model-version: 1.8 14 | 15 | train-dataset: ucf101 16 | 17 | evaluation: top1acc79.51 18 | 19 | author: MindSpore team 20 | 21 | update-time: 2022-07-19 22 | 23 | repo-link: 24 | 25 | user-id: MindSpore 26 | 27 | used-for: inference 28 | 29 | mindspore-version: 1.8 30 | 31 | asset: 32 | 33 | - 34 | file-format: ckpt 35 | asset-link: 36 | asset-sha256: 44e707636ce4ada89ee1f97ce1fecbe1a0373b7dfe21a6d711c1db827676002f 37 | 38 | license: Apache2.0 39 | 40 | summary: c3d is used for cv 41 | 42 | --- 43 | 44 | ## Introduction 45 | 46 | This MindSpore Hub model uses the implementation of c3d from the MindSpore model zoo on Gitee at official/cv/c3d. 47 | 48 | c3d is a cv network. More details please refer to the MindSpore model zoo on Gitee at [official/cv/c3d](https://gitee.com/mindspore/models/blob/r1.8/official/cv/c3d/README.md). 49 | 50 | All parameters in the module are trainable. 51 | 52 | ## Citation 53 | 54 | Learning Spatiotemporal Features with 3D Convolutional Networks 55 | 56 | ## Disclaimer 57 | 58 | MindSpore ("we") do not own any ownership or intellectual property rights of the datasets, and the trained models are provided on an "as is" and "as available" basis. We make no representations or warranties of any kind of the datasets and trained models (collectively, “materials”) and will not be liable for any loss, damage, expense or cost arising from the materials. Please ensure that you have permission to use the dataset under the appropriate license for the dataset and in accordance with the terms of the relevant license agreement. The trained models provided are only for research and education purposes. 59 | 60 | To Dataset Owners: If you do not wish to have a dataset included in MindSpore, or wish to update it in any way, we will remove or update the content at your request. Please contact us through GitHub or Gitee. Your understanding and contributions to the community are greatly appreciated. 61 | 62 | MindSpore is available under the Apache 2.0 license, please see the LICENSE file. 63 | -------------------------------------------------------------------------------- /mshub_res/assets/mindspore/1.8/cct_imagenet2012.md: -------------------------------------------------------------------------------- 1 | # cct 2 | 3 | --- 4 | 5 | model-name: cct 6 | 7 | backbone-name: cct 8 | 9 | module-type: cv 10 | 11 | fine-tunable: True 12 | 13 | model-version: 1.8 14 | 15 | train-dataset: imagenet2012 16 | 17 | evaluation: top1acc80.24 | top5acc94.93 18 | 19 | author: MindSpore team 20 | 21 | update-time: 2022-07-19 22 | 23 | repo-link: 24 | 25 | user-id: MindSpore 26 | 27 | used-for: inference 28 | 29 | mindspore-version: 1.8 30 | 31 | asset: 32 | 33 | - 34 | file-format: ckpt 35 | asset-link: 36 | asset-sha256: 7df9c4c1e9d190f289aadfe79fdbef5c330228b1694230eeedf552dc15188261 37 | 38 | license: Apache2.0 39 | 40 | summary: cct is used for cv 41 | 42 | --- 43 | 44 | ## Introduction 45 | 46 | This MindSpore Hub model uses the implementation of cct from the MindSpore model zoo on Gitee at research/cv/cct. 47 | 48 | cct is a cv network. More details please refer to the MindSpore model zoo on Gitee at [research/cv/cct](https://gitee.com/mindspore/models/blob/r1.8/research/cv/cct/README_CN.md). 49 | 50 | All parameters in the module are trainable. 51 | 52 | ## Disclaimer 53 | 54 | MindSpore ("we") do not own any ownership or intellectual property rights of the datasets, and the trained models are provided on an "as is" and "as available" basis. We make no representations or warranties of any kind of the datasets and trained models (collectively, “materials”) and will not be liable for any loss, damage, expense or cost arising from the materials. Please ensure that you have permission to use the dataset under the appropriate license for the dataset and in accordance with the terms of the relevant license agreement. The trained models provided are only for research and education purposes. 55 | 56 | To Dataset Owners: If you do not wish to have a dataset included in MindSpore, or wish to update it in any way, we will remove or update the content at your request. Please contact us through GitHub or Gitee. Your understanding and contributions to the community are greatly appreciated. 57 | 58 | MindSpore is available under the Apache 2.0 license, please see the LICENSE file. 59 | -------------------------------------------------------------------------------- /mshub_res/assets/mindspore/1.8/cgan_G_mnist.md: -------------------------------------------------------------------------------- 1 | # CGAN 2 | 3 | --- 4 | 5 | model-name: CGAN 6 | 7 | backbone-name: CGAN 8 | 9 | module-type: cv 10 | 11 | fine-tunable: True 12 | 13 | model-version: 1.8 14 | 15 | train-dataset: mnist 16 | 17 | evaluation: - 18 | 19 | author: MindSpore team 20 | 21 | update-time: 2022-07-19 22 | 23 | repo-link: 24 | 25 | user-id: MindSpore 26 | 27 | used-for: inference 28 | 29 | mindspore-version: 1.8 30 | 31 | asset: 32 | 33 | - 34 | file-format: ckpt 35 | asset-link: 36 | asset-sha256: 360a455b190fc1a56cfb487ed25278794e86ea615458cf3066ca06e3b8ad3d41 37 | 38 | license: Apache2.0 39 | 40 | summary: CGAN is used for cv 41 | 42 | --- 43 | 44 | ## Introduction 45 | 46 | This MindSpore Hub model uses the implementation of CGAN from the MindSpore model zoo on Gitee at research/cv/CGAN. 47 | 48 | CGAN is a cv network. More details please refer to the MindSpore model zoo on Gitee at [research/cv/CGAN](https://gitee.com/mindspore/models/blob/r1.8/research/cv/CGAN/README.md). 49 | 50 | All parameters in the module are trainable. 51 | 52 | ## Citation 53 | 54 | Conditional Generative Adversarial Nets. 55 | 56 | ## Disclaimer 57 | 58 | MindSpore ("we") do not own any ownership or intellectual property rights of the datasets, and the trained models are provided on an "as is" and "as available" basis. We make no representations or warranties of any kind of the datasets and trained models (collectively, “materials”) and will not be liable for any loss, damage, expense or cost arising from the materials. Please ensure that you have permission to use the dataset under the appropriate license for the dataset and in accordance with the terms of the relevant license agreement. The trained models provided are only for research and education purposes. 59 | 60 | To Dataset Owners: If you do not wish to have a dataset included in MindSpore, or wish to update it in any way, we will remove or update the content at your request. Please contact us through GitHub or Gitee. Your understanding and contributions to the community are greatly appreciated. 61 | 62 | MindSpore is available under the Apache 2.0 license, please see the LICENSE file. 63 | -------------------------------------------------------------------------------- /mshub_res/assets/mindspore/1.8/dbpn_div2k.md: -------------------------------------------------------------------------------- 1 | # DBPN 2 | 3 | --- 4 | 5 | model-name: DBPN 6 | 7 | backbone-name: DBPN 8 | 9 | module-type: cv-super_resolution 10 | 11 | fine-tunable: True 12 | 13 | model-version: 1.8 14 | 15 | train-dataset: DIV2K 16 | 17 | evaluation: psnr31.62 18 | 19 | author: MindSpore team 20 | 21 | update-time: 2022-08-08 22 | 23 | repo-link: 24 | 25 | user-id: MindSpore 26 | 27 | used-for: inference 28 | 29 | mindspore-version: 1.8 30 | 31 | asset: 32 | 33 | - 34 | file-format: ckpt 35 | asset-link: 36 | asset-sha256: e29dd3bade3d6e25e6790c02add96a865cdfdb66c9f76063d440ea87eea2b217 37 | 38 | license: Apache2.0 39 | 40 | summary: DBPN is used for cv 41 | 42 | --- 43 | 44 | ## Introduction 45 | 46 | This MindSpore Hub model uses the implementation of DBPN from the MindSpore model zoo on Gitee at research/cv/DBPN. 47 | 48 | DBPN is a cv network. More details please refer to the MindSpore model zoo on Gitee at [research/cv/DBPN](https://gitee.com/mindspore/models/blob/r1.8/research/cv/DBPN/README.md). 49 | 50 | All parameters in the module are trainable. 51 | 52 | ## Citation 53 | 54 | Muhammad Haris, Greg Shakhnarovich, Norimichi Ukita 55 | 56 | ## Disclaimer 57 | 58 | MindSpore ("we") do not own any ownership or intellectual property rights of the datasets, and the trained models are provided on an "as is" and "as available" basis. We make no representations or warranties of any kind of the datasets and trained models (collectively, “materials”) and will not be liable for any loss, damage, expense or cost arising from the materials. Please ensure that you have permission to use the dataset under the appropriate license for the dataset and in accordance with the terms of the relevant license agreement. The trained models provided are only for research and education purposes. 59 | 60 | To Dataset Owners: If you do not wish to have a dataset included in MindSpore, or wish to update it in any way, we will remove or update the content at your request. Please contact us through GitHub or Gitee. Your understanding and contributions to the community are greatly appreciated. 61 | 62 | MindSpore is available under the Apache 2.0 license, please see the LICENSE file. 63 | -------------------------------------------------------------------------------- /mshub_res/assets/mindspore/1.8/dbpngan_G_div2k.md: -------------------------------------------------------------------------------- 1 | # DBPN 2 | 3 | --- 4 | 5 | model-name: DBPN 6 | 7 | backbone-name: DBPN 8 | 9 | module-type: cv 10 | 11 | fine-tunable: True 12 | 13 | model-version: 1.8 14 | 15 | train-dataset: div2k 16 | 17 | evaluation: PSNR29.23 18 | 19 | author: MindSpore team 20 | 21 | update-time: 2022-07-19 22 | 23 | repo-link: 24 | 25 | user-id: MindSpore 26 | 27 | used-for: inference 28 | 29 | mindspore-version: 1.8 30 | 31 | asset: 32 | 33 | - 34 | file-format: ckpt 35 | asset-link: 36 | asset-sha256: 00ad7584fde0e5a926596ed91bebd5c13aa04086c22ac6def15a9d14dcd7eec1 37 | 38 | license: Apache2.0 39 | 40 | summary: DBPN is used for cv 41 | 42 | --- 43 | 44 | ## Introduction 45 | 46 | This MindSpore Hub model uses the implementation of DBPN from the MindSpore model zoo on Gitee at research/cv/DBPN. 47 | 48 | DBPN is a cv network. More details please refer to the MindSpore model zoo on Gitee at [research/cv/DBPN](https://gitee.com/mindspore/models/blob/r1.8/research/cv/DBPN/README.md). 49 | 50 | All parameters in the module are trainable. 51 | 52 | ## Citation 53 | 54 | Muhammad Haris, Greg Shakhnarovich, Norimichi Ukita 55 | 56 | ## Disclaimer 57 | 58 | MindSpore ("we") do not own any ownership or intellectual property rights of the datasets, and the trained models are provided on an "as is" and "as available" basis. We make no representations or warranties of any kind of the datasets and trained models (collectively, “materials”) and will not be liable for any loss, damage, expense or cost arising from the materials. Please ensure that you have permission to use the dataset under the appropriate license for the dataset and in accordance with the terms of the relevant license agreement. The trained models provided are only for research and education purposes. 59 | 60 | To Dataset Owners: If you do not wish to have a dataset included in MindSpore, or wish to update it in any way, we will remove or update the content at your request. Please contact us through GitHub or Gitee. Your understanding and contributions to the community are greatly appreciated. 61 | 62 | MindSpore is available under the Apache 2.0 license, please see the LICENSE file. 63 | -------------------------------------------------------------------------------- /mshub_res/assets/mindspore/1.8/ddrnet_imagenet2012.md: -------------------------------------------------------------------------------- 1 | # DDRNet 2 | 3 | --- 4 | 5 | model-name: DDRNet 6 | 7 | backbone-name: DDRNet 8 | 9 | module-type: cv 10 | 11 | fine-tunable: True 12 | 13 | model-version: 1.8 14 | 15 | train-dataset: imagenet2012 16 | 17 | evaluation: top1acc76.36 | top5acc93.24 18 | 19 | author: MindSpore team 20 | 21 | update-time: 2022-07-19 22 | 23 | repo-link: 24 | 25 | user-id: MindSpore 26 | 27 | used-for: inference 28 | 29 | mindspore-version: 1.8 30 | 31 | asset: 32 | 33 | - 34 | file-format: ckpt 35 | asset-link: 36 | asset-sha256: 6acbc32e242e674a61b812adfb1a7972348e013f0c8bd0beccaeca0446305997 37 | 38 | license: Apache2.0 39 | 40 | summary: DDRNet is used for cv 41 | 42 | --- 43 | 44 | ## Introduction 45 | 46 | This MindSpore Hub model uses the implementation of DDRNet from the MindSpore model zoo on Gitee at research/cv/DDRNet. 47 | 48 | DDRNet is a cv network. More details please refer to the MindSpore model zoo on Gitee at [research/cv/DDRNet](https://gitee.com/mindspore/models/blob/r1.8/research/cv/DDRNet/README_CN.md). 49 | 50 | All parameters in the module are trainable. 51 | 52 | ## Disclaimer 53 | 54 | MindSpore ("we") do not own any ownership or intellectual property rights of the datasets, and the trained models are provided on an "as is" and "as available" basis. We make no representations or warranties of any kind of the datasets and trained models (collectively, “materials”) and will not be liable for any loss, damage, expense or cost arising from the materials. Please ensure that you have permission to use the dataset under the appropriate license for the dataset and in accordance with the terms of the relevant license agreement. The trained models provided are only for research and education purposes. 55 | 56 | To Dataset Owners: If you do not wish to have a dataset included in MindSpore, or wish to update it in any way, we will remove or update the content at your request. Please contact us through GitHub or Gitee. Your understanding and contributions to the community are greatly appreciated. 57 | 58 | MindSpore is available under the Apache 2.0 license, please see the LICENSE file. 59 | -------------------------------------------------------------------------------- /mshub_res/assets/mindspore/1.8/dgu_udc.md: -------------------------------------------------------------------------------- 1 | # dgu 2 | 3 | --- 4 | 5 | model-name: dgu 6 | 7 | backbone-name: dgu 8 | 9 | module-type: nlp 10 | 11 | fine-tunable: True 12 | 13 | model-version: 1.8 14 | 15 | train-dataset: udc 16 | 17 | evaluation: R1acc82.14 | R2acc90.45 | R5acc97.75 18 | 19 | author: MindSpore team 20 | 21 | update-time: 2022-07-19 22 | 23 | repo-link: 24 | 25 | user-id: MindSpore 26 | 27 | used-for: inference 28 | 29 | mindspore-version: 1.8 30 | 31 | asset: 32 | 33 | - 34 | file-format: ckpt 35 | asset-link: 36 | asset-sha256: ef323d57071720e70df35c28d0d8cffcfafdefdfe694ca4c39b21d013f2b8542 37 | 38 | license: Apache2.0 39 | 40 | summary: dgu is used for nlp 41 | 42 | --- 43 | 44 | ## Introduction 45 | 46 | This MindSpore Hub model uses the implementation of dgu from the MindSpore model zoo on Gitee at official/nlp/dgu. 47 | 48 | dgu is a nlp network. More details please refer to the MindSpore model zoo on Gitee at [official/nlp/dgu](https://gitee.com/mindspore/models/blob/r1.8/official/nlp/dgu/README_CN.md). 49 | 50 | All parameters in the module are trainable. 51 | 52 | ## Disclaimer 53 | 54 | MindSpore ("we") do not own any ownership or intellectual property rights of the datasets, and the trained models are provided on an "as is" and "as available" basis. We make no representations or warranties of any kind of the datasets and trained models (collectively, “materials”) and will not be liable for any loss, damage, expense or cost arising from the materials. Please ensure that you have permission to use the dataset under the appropriate license for the dataset and in accordance with the terms of the relevant license agreement. The trained models provided are only for research and education purposes. 55 | 56 | To Dataset Owners: If you do not wish to have a dataset included in MindSpore, or wish to update it in any way, we will remove or update the content at your request. Please contact us through GitHub or Gitee. Your understanding and contributions to the community are greatly appreciated. 57 | 58 | MindSpore is available under the Apache 2.0 license, please see the LICENSE file. 59 | -------------------------------------------------------------------------------- /mshub_res/assets/mindspore/1.8/emotect_baidu.md: -------------------------------------------------------------------------------- 1 | # emotect 2 | 3 | --- 4 | 5 | model-name: emotect 6 | 7 | backbone-name: emotect 8 | 9 | module-type: nlp 10 | 11 | fine-tunable: True 12 | 13 | model-version: 1.8 14 | 15 | train-dataset: baidu 16 | 17 | evaluation: acc90.44 18 | 19 | author: MindSpore team 20 | 21 | update-time: 2022-07-19 22 | 23 | repo-link: 24 | 25 | user-id: MindSpore 26 | 27 | used-for: inference 28 | 29 | mindspore-version: 1.8 30 | 31 | asset: 32 | 33 | - 34 | file-format: ckpt 35 | asset-link: 36 | asset-sha256: 5b0ddb04f17f27d706007353d15faf93b627dcaf0ce7fcfcf1b61d4f2ddc228b 37 | 38 | license: Apache2.0 39 | 40 | summary: emotect is used for nlp 41 | 42 | --- 43 | 44 | ## Introduction 45 | 46 | This MindSpore Hub model uses the implementation of emotect from the MindSpore model zoo on Gitee at official/nlp/emotect. 47 | 48 | emotect is a nlp network. More details please refer to the MindSpore model zoo on Gitee at [official/nlp/emotect](https://gitee.com/mindspore/models/blob/r1.8/official/nlp/emotect/README_CN.md). 49 | 50 | All parameters in the module are trainable. 51 | 52 | ## Disclaimer 53 | 54 | MindSpore ("we") do not own any ownership or intellectual property rights of the datasets, and the trained models are provided on an "as is" and "as available" basis. We make no representations or warranties of any kind of the datasets and trained models (collectively, “materials”) and will not be liable for any loss, damage, expense or cost arising from the materials. Please ensure that you have permission to use the dataset under the appropriate license for the dataset and in accordance with the terms of the relevant license agreement. The trained models provided are only for research and education purposes. 55 | 56 | To Dataset Owners: If you do not wish to have a dataset included in MindSpore, or wish to update it in any way, we will remove or update the content at your request. Please contact us through GitHub or Gitee. Your understanding and contributions to the community are greatly appreciated. 57 | 58 | MindSpore is available under the Apache 2.0 license, please see the LICENSE file. 59 | -------------------------------------------------------------------------------- /mshub_res/assets/mindspore/1.8/nfnet_imagenet2012.md: -------------------------------------------------------------------------------- 1 | # NFNet 2 | 3 | --- 4 | 5 | model-name: NFNet 6 | 7 | backbone-name: NFNet 8 | 9 | module-type: cv 10 | 11 | fine-tunable: True 12 | 13 | model-version: 1.8 14 | 15 | train-dataset: imagenet2012 16 | 17 | evaluation: top1acc83.26 18 | 19 | author: MindSpore team 20 | 21 | update-time: 2022-07-19 22 | 23 | repo-link: 24 | 25 | user-id: MindSpore 26 | 27 | used-for: inference 28 | 29 | mindspore-version: 1.8 30 | 31 | asset: 32 | 33 | - 34 | file-format: ckpt 35 | asset-link: 36 | asset-sha256: 773b0098a124e13beaee3a14b3dfc09b73139cc185f11fc43d9d2124d6905c26 37 | 38 | license: Apache2.0 39 | 40 | summary: NFNet is used for cv 41 | 42 | --- 43 | 44 | ## Introduction 45 | 46 | This MindSpore Hub model uses the implementation of NFNet from the MindSpore model zoo on Gitee at research/cv/NFNet. 47 | 48 | NFNet is a cv network. More details please refer to the MindSpore model zoo on Gitee at [research/cv/NFNet](https://gitee.com/mindspore/models/blob/r1.8/research/cv/NFNet/README_CN.md). 49 | 50 | All parameters in the module are trainable. 51 | 52 | ## Disclaimer 53 | 54 | MindSpore ("we") do not own any ownership or intellectual property rights of the datasets, and the trained models are provided on an "as is" and "as available" basis. We make no representations or warranties of any kind of the datasets and trained models (collectively, “materials”) and will not be liable for any loss, damage, expense or cost arising from the materials. Please ensure that you have permission to use the dataset under the appropriate license for the dataset and in accordance with the terms of the relevant license agreement. The trained models provided are only for research and education purposes. 55 | 56 | To Dataset Owners: If you do not wish to have a dataset included in MindSpore, or wish to update it in any way, we will remove or update the content at your request. Please contact us through GitHub or Gitee. Your understanding and contributions to the community are greatly appreciated. 57 | 58 | MindSpore is available under the Apache 2.0 license, please see the LICENSE file. 59 | -------------------------------------------------------------------------------- /mshub_res/assets/mindspore/1.8/rcan_div2k.md: -------------------------------------------------------------------------------- 1 | # RCAN 2 | 3 | --- 4 | 5 | model-name: RCAN 6 | 7 | backbone-name: RCAN 8 | 9 | module-type: cv-super_resolution 10 | 11 | fine-tunable: True 12 | 13 | model-version: 1.8 14 | 15 | train-dataset: DIV2K 16 | 17 | evaluation: PSNR38.26 | SSIM0.9614 18 | 19 | author: MindSpore team 20 | 21 | update-time: 2022-08-08 22 | 23 | repo-link: 24 | 25 | user-id: MindSpore 26 | 27 | used-for: inference 28 | 29 | mindspore-version: 1.8 30 | 31 | asset: 32 | 33 | - 34 | file-format: ckpt 35 | asset-link: 36 | asset-sha256: f75418c61a5daa58c3e3499ad0551033bf3a1cce217054c7e01ab42615977286 37 | 38 | license: Apache2.0 39 | 40 | summary: RCAN is used for cv 41 | 42 | --- 43 | 44 | ## Introduction 45 | 46 | This MindSpore Hub model uses the implementation of RCAN from the MindSpore model zoo on Gitee at research/cv/RCAN. 47 | 48 | RCAN is a cv network. More details please refer to the MindSpore model zoo on Gitee at [research/cv/RCAN](https://gitee.com/mindspore/models/blob/r1.8/research/cv/RCAN/README.md). 49 | 50 | All parameters in the module are trainable. 51 | 52 | ## Disclaimer 53 | 54 | MindSpore ("we") do not own any ownership or intellectual property rights of the datasets, and the trained models are provided on an "as is" and "as available" basis. We make no representations or warranties of any kind of the datasets and trained models (collectively, “materials”) and will not be liable for any loss, damage, expense or cost arising from the materials. Please ensure that you have permission to use the dataset under the appropriate license for the dataset and in accordance with the terms of the relevant license agreement. The trained models provided are only for research and education purposes. 55 | 56 | To Dataset Owners: If you do not wish to have a dataset included in MindSpore, or wish to update it in any way, we will remove or update the content at your request. Please contact us through GitHub or Gitee. Your understanding and contributions to the community are greatly appreciated. 57 | 58 | MindSpore is available under the Apache 2.0 license, please see the LICENSE file. 59 | -------------------------------------------------------------------------------- /mshub_res/assets/mindspore/1.8/rdn_div2k.md: -------------------------------------------------------------------------------- 1 | # RDN 2 | 3 | --- 4 | 5 | model-name: RDN 6 | 7 | backbone-name: RDN 8 | 9 | module-type: cv 10 | 11 | fine-tunable: True 12 | 13 | model-version: 1.8 14 | 15 | train-dataset: div2k 16 | 17 | evaluation: psnr38.3 | ssim0.961 18 | 19 | author: MindSpore team 20 | 21 | update-time: 2022-07-19 22 | 23 | repo-link: 24 | 25 | user-id: MindSpore 26 | 27 | used-for: inference 28 | 29 | mindspore-version: 1.8 30 | 31 | asset: 32 | 33 | - 34 | file-format: ckpt 35 | asset-link: 36 | asset-sha256: 36cbce25c04256f1b0652c3f25ba704faf135dc2ad395020de3b8db7e05e23ef 37 | 38 | license: Apache2.0 39 | 40 | summary: RDN is used for cv 41 | 42 | --- 43 | 44 | ## Introduction 45 | 46 | This MindSpore Hub model uses the implementation of RDN from the MindSpore model zoo on Gitee at official/cv/RDN. 47 | 48 | RDN is a cv network. More details please refer to the MindSpore model zoo on Gitee at [official/cv/RDN](https://gitee.com/mindspore/models/blob/r1.8/official/cv/RDN/README.md). 49 | 50 | All parameters in the module are trainable. 51 | 52 | ## Disclaimer 53 | 54 | MindSpore ("we") do not own any ownership or intellectual property rights of the datasets, and the trained models are provided on an "as is" and "as available" basis. We make no representations or warranties of any kind of the datasets and trained models (collectively, “materials”) and will not be liable for any loss, damage, expense or cost arising from the materials. Please ensure that you have permission to use the dataset under the appropriate license for the dataset and in accordance with the terms of the relevant license agreement. The trained models provided are only for research and education purposes. 55 | 56 | To Dataset Owners: If you do not wish to have a dataset included in MindSpore, or wish to update it in any way, we will remove or update the content at your request. Please contact us through GitHub or Gitee. Your understanding and contributions to the community are greatly appreciated. 57 | 58 | MindSpore is available under the Apache 2.0 license, please see the LICENSE file. 59 | -------------------------------------------------------------------------------- /mshub_res/assets/mindspore/1.8/repvgg_imagenet2012.md: -------------------------------------------------------------------------------- 1 | # repvgg 2 | 3 | --- 4 | 5 | model-name: repvgg 6 | 7 | backbone-name: repvgg 8 | 9 | module-type: cv 10 | 11 | fine-tunable: True 12 | 13 | model-version: 1.8 14 | 15 | train-dataset: imagenet2012 16 | 17 | evaluation: top1acc72.60 | top5acc90.61 18 | 19 | author: MindSpore team 20 | 21 | update-time: 2022-07-19 22 | 23 | repo-link: 24 | 25 | user-id: MindSpore 26 | 27 | used-for: inference 28 | 29 | mindspore-version: 1.8 30 | 31 | asset: 32 | 33 | - 34 | file-format: ckpt 35 | asset-link: 36 | asset-sha256: dd09785c5b00c794cc150fee1409cc379b5cf166e869330fed0cfa9ac7d42953 37 | 38 | license: Apache2.0 39 | 40 | summary: repvgg is used for cv 41 | 42 | --- 43 | 44 | ## Introduction 45 | 46 | This MindSpore Hub model uses the implementation of repvgg from the MindSpore model zoo on Gitee at research/cv/repvgg. 47 | 48 | repvgg is a cv network. More details please refer to the MindSpore model zoo on Gitee at [research/cv/repvgg](https://gitee.com/mindspore/models/blob/r1.8/research/cv/repvgg/README_CN.md). 49 | 50 | All parameters in the module are trainable. 51 | 52 | ## Disclaimer 53 | 54 | MindSpore ("we") do not own any ownership or intellectual property rights of the datasets, and the trained models are provided on an "as is" and "as available" basis. We make no representations or warranties of any kind of the datasets and trained models (collectively, “materials”) and will not be liable for any loss, damage, expense or cost arising from the materials. Please ensure that you have permission to use the dataset under the appropriate license for the dataset and in accordance with the terms of the relevant license agreement. The trained models provided are only for research and education purposes. 55 | 56 | To Dataset Owners: If you do not wish to have a dataset included in MindSpore, or wish to update it in any way, we will remove or update the content at your request. Please contact us through GitHub or Gitee. Your understanding and contributions to the community are greatly appreciated. 57 | 58 | MindSpore is available under the Apache 2.0 license, please see the LICENSE file. 59 | -------------------------------------------------------------------------------- /mshub_res/assets/mindspore/1.8/tnt_imagenet2012.md: -------------------------------------------------------------------------------- 1 | # TNT 2 | 3 | --- 4 | 5 | model-name: TNT 6 | 7 | backbone-name: TNT 8 | 9 | module-type: cv 10 | 11 | fine-tunable: True 12 | 13 | model-version: 1.8 14 | 15 | train-dataset: imagenet2012 16 | 17 | evaluation: top1acc81.03 | top5acc95.41 18 | 19 | author: MindSpore team 20 | 21 | update-time: 2022-07-19 22 | 23 | repo-link: 24 | 25 | user-id: MindSpore 26 | 27 | used-for: inference 28 | 29 | mindspore-version: 1.8 30 | 31 | asset: 32 | 33 | - 34 | file-format: ckpt 35 | asset-link: 36 | asset-sha256: 94dd6f5b772d6b87c3cb017beffe9c5784a237d7e08217355828084690eb90c5 37 | 38 | license: Apache2.0 39 | 40 | summary: TNT is used for cv 41 | 42 | --- 43 | 44 | ## Introduction 45 | 46 | This MindSpore Hub model uses the implementation of TNT from the MindSpore model zoo on Gitee at research/cv/TNT. 47 | 48 | TNT is a cv network. More details please refer to the MindSpore model zoo on Gitee at [research/cv/TNT](https://gitee.com/mindspore/models/blob/r1.8/research/cv/TNT/README_CN.md). 49 | 50 | All parameters in the module are trainable. 51 | 52 | ## Disclaimer 53 | 54 | MindSpore ("we") do not own any ownership or intellectual property rights of the datasets, and the trained models are provided on an "as is" and "as available" basis. We make no representations or warranties of any kind of the datasets and trained models (collectively, “materials”) and will not be liable for any loss, damage, expense or cost arising from the materials. Please ensure that you have permission to use the dataset under the appropriate license for the dataset and in accordance with the terms of the relevant license agreement. The trained models provided are only for research and education purposes. 55 | 56 | To Dataset Owners: If you do not wish to have a dataset included in MindSpore, or wish to update it in any way, we will remove or update the content at your request. Please contact us through GitHub or Gitee. Your understanding and contributions to the community are greatly appreciated. 57 | 58 | MindSpore is available under the Apache 2.0 license, please see the LICENSE file. 59 | -------------------------------------------------------------------------------- /mshub_res/assets/mindspore/1.8/yolox_coco2017.md: -------------------------------------------------------------------------------- 1 | # yolox 2 | 3 | --- 4 | 5 | model-name: yolox 6 | 7 | backbone-name: yolox 8 | 9 | module-type: cv 10 | 11 | fine-tunable: True 12 | 13 | model-version: 1.8 14 | 15 | train-dataset: coco2017 16 | 17 | evaluation: map47.3 18 | 19 | author: MindSpore team 20 | 21 | update-time: 2022-07-19 22 | 23 | repo-link: 24 | 25 | user-id: MindSpore 26 | 27 | used-for: inference 28 | 29 | mindspore-version: 1.8 30 | 31 | asset: 32 | 33 | - 34 | file-format: ckpt 35 | asset-link: 36 | asset-sha256: 31ee25297c89aa6632de562c891a8fb8f85cfb52d86a2145de303452b591cdb6 37 | 38 | license: Apache2.0 39 | 40 | summary: yolox is used for cv 41 | 42 | --- 43 | 44 | ## Introduction 45 | 46 | This MindSpore Hub model uses the implementation of yolox from the MindSpore model zoo on Gitee at research/cv/yolox. 47 | 48 | yolox is a cv network. More details please refer to the MindSpore model zoo on Gitee at [research/cv/yolox](https://gitee.com/mindspore/models/blob/r1.8/research/cv/yolox/README_CN.md). 49 | 50 | All parameters in the module are trainable. 51 | 52 | ## Citation 53 | 54 | YOLOX: Exceeding YOLO Series in 2021 55 | 56 | ## Disclaimer 57 | 58 | MindSpore ("we") do not own any ownership or intellectual property rights of the datasets, and the trained models are provided on an "as is" and "as available" basis. We make no representations or warranties of any kind of the datasets and trained models (collectively, “materials”) and will not be liable for any loss, damage, expense or cost arising from the materials. Please ensure that you have permission to use the dataset under the appropriate license for the dataset and in accordance with the terms of the relevant license agreement. The trained models provided are only for research and education purposes. 59 | 60 | To Dataset Owners: If you do not wish to have a dataset included in MindSpore, or wish to update it in any way, we will remove or update the content at your request. Please contact us through GitHub or Gitee. Your understanding and contributions to the community are greatly appreciated. 61 | 62 | MindSpore is available under the Apache 2.0 license, please see the LICENSE file. 63 | -------------------------------------------------------------------------------- /mshub_res/assets/mindspore/1.9/avacifar_cifar10.md: -------------------------------------------------------------------------------- 1 | # AVA_cifar 2 | 3 | --- 4 | 5 | model-name: AVA_cifar 6 | 7 | backbone-name: AVA_cifar 8 | 9 | module-type: cv 10 | 11 | fine-tunable: True 12 | 13 | model-version: 1.9 14 | 15 | train-dataset: cifar10 16 | 17 | evaluation: top1acc90.87 18 | 19 | author: MindSpore team 20 | 21 | update-time: 2022-10-11 22 | 23 | repo-link: 24 | 25 | user-id: MindSpore 26 | 27 | used-for: inference 28 | 29 | mindspore-version: 1.9 30 | 31 | asset: 32 | 33 | - 34 | file-format: ckpt 35 | asset-link: 36 | asset-sha256: 955f6c567108af733ea82f8efb1eec0accd44cbc79a41b2d216ca83d09a646c6 37 | 38 | license: Apache2.0 39 | 40 | summary: AVA_cifar is used for cv 41 | 42 | --- 43 | 44 | ## Introduction 45 | 46 | This MindSpore Hub model uses the implementation of AVA_cifar from the MindSpore model zoo on Gitee at research/cv/AVA_cifar. 47 | 48 | AVA_cifar is a cv network. More details please refer to the MindSpore model zoo on Gitee at [research/cv/AVA_cifar](https://gitee.com/mindspore/models/blob/r1.9/research/cv/AVA_cifar/README.md). 49 | 50 | All parameters in the module are trainable. 51 | 52 | ## Disclaimer 53 | 54 | MindSpore ("we") do not own any ownership or intellectual property rights of the datasets, and the trained models are provided on an "as is" and "as available" basis. We make no representations or warranties of any kind of the datasets and trained models (collectively, “materials”) and will not be liable for any loss, damage, expense or cost arising from the materials. Please ensure that you have permission to use the dataset under the appropriate license for the dataset and in accordance with the terms of the relevant license agreement. The trained models provided are only for research and education purposes. 55 | 56 | To Dataset Owners: If you do not wish to have a dataset included in MindSpore, or wish to update it in any way, we will remove or update the content at your request. Please contact us through GitHub or Gitee. Your understanding and contributions to the community are greatly appreciated. 57 | 58 | MindSpore is available under the Apache 2.0 license, please see the LICENSE file. 59 | -------------------------------------------------------------------------------- /mshub_res/assets/mindspore/1.9/avahpa_hpa.md: -------------------------------------------------------------------------------- 1 | # AVA_hpa 2 | 3 | --- 4 | 5 | model-name: AVA_hpa 6 | 7 | backbone-name: AVA_hpa 8 | 9 | module-type: cv 10 | 11 | fine-tunable: True 12 | 13 | model-version: 1.9 14 | 15 | train-dataset: hpa 16 | 17 | evaluation: macroF1acc70.05 | microF1acc77.97 | auc94.23 18 | 19 | author: MindSpore team 20 | 21 | update-time: 2022-10-11 22 | 23 | repo-link: 24 | 25 | user-id: MindSpore 26 | 27 | used-for: inference 28 | 29 | mindspore-version: 1.9 30 | 31 | asset: 32 | 33 | - 34 | file-format: ckpt 35 | asset-link: 36 | asset-sha256: 50d6c1d61a750a3524076c1088bda669d492713a6b8be9d71dd9c6b5e2656102 37 | 38 | license: Apache2.0 39 | 40 | summary: AVA_hpa is used for cv 41 | 42 | --- 43 | 44 | ## Introduction 45 | 46 | This MindSpore Hub model uses the implementation of AVA_hpa from the MindSpore model zoo on Gitee at research/cv/AVA_hpa. 47 | 48 | AVA_hpa is a cv network. More details please refer to the MindSpore model zoo on Gitee at [research/cv/AVA_hpa](https://gitee.com/mindspore/models/blob/r1.9/research/cv/AVA_hpa/README.md). 49 | 50 | All parameters in the module are trainable. 51 | 52 | ## Disclaimer 53 | 54 | MindSpore ("we") do not own any ownership or intellectual property rights of the datasets, and the trained models are provided on an "as is" and "as available" basis. We make no representations or warranties of any kind of the datasets and trained models (collectively, “materials”) and will not be liable for any loss, damage, expense or cost arising from the materials. Please ensure that you have permission to use the dataset under the appropriate license for the dataset and in accordance with the terms of the relevant license agreement. The trained models provided are only for research and education purposes. 55 | 56 | To Dataset Owners: If you do not wish to have a dataset included in MindSpore, or wish to update it in any way, we will remove or update the content at your request. Please contact us through GitHub or Gitee. Your understanding and contributions to the community are greatly appreciated. 57 | 58 | MindSpore is available under the Apache 2.0 license, please see the LICENSE file. 59 | -------------------------------------------------------------------------------- /mshub_res/assets/mindspore/1.9/cct_imagenet2012.md: -------------------------------------------------------------------------------- 1 | # cct 2 | 3 | --- 4 | 5 | model-name: cct 6 | 7 | backbone-name: cct 8 | 9 | module-type: cv 10 | 11 | fine-tunable: True 12 | 13 | model-version: 1.9 14 | 15 | train-dataset: imagenet2012 16 | 17 | evaluation: top1acc80.24 | top5acc94.93 18 | 19 | author: MindSpore team 20 | 21 | update-time: 2022-10-11 22 | 23 | repo-link: 24 | 25 | user-id: MindSpore 26 | 27 | used-for: inference 28 | 29 | mindspore-version: 1.9 30 | 31 | asset: 32 | 33 | - 34 | file-format: ckpt 35 | asset-link: 36 | asset-sha256: 7df9c4c1e9d190f289aadfe79fdbef5c330228b1694230eeedf552dc15188261 37 | 38 | license: Apache2.0 39 | 40 | summary: cct is used for cv 41 | 42 | --- 43 | 44 | ## Introduction 45 | 46 | This MindSpore Hub model uses the implementation of cct from the MindSpore model zoo on Gitee at research/cv/cct. 47 | 48 | cct is a cv network. More details please refer to the MindSpore model zoo on Gitee at [research/cv/cct](https://gitee.com/mindspore/models/blob/r1.9/research/cv/cct/README_CN.md). 49 | 50 | All parameters in the module are trainable. 51 | 52 | ## Disclaimer 53 | 54 | MindSpore ("we") do not own any ownership or intellectual property rights of the datasets, and the trained models are provided on an "as is" and "as available" basis. We make no representations or warranties of any kind of the datasets and trained models (collectively, “materials”) and will not be liable for any loss, damage, expense or cost arising from the materials. Please ensure that you have permission to use the dataset under the appropriate license for the dataset and in accordance with the terms of the relevant license agreement. The trained models provided are only for research and education purposes. 55 | 56 | To Dataset Owners: If you do not wish to have a dataset included in MindSpore, or wish to update it in any way, we will remove or update the content at your request. Please contact us through GitHub or Gitee. Your understanding and contributions to the community are greatly appreciated. 57 | 58 | MindSpore is available under the Apache 2.0 license, please see the LICENSE file. 59 | -------------------------------------------------------------------------------- /mshub_res/assets/mindspore/1.9/cgan_G_mnist.md: -------------------------------------------------------------------------------- 1 | # CGAN 2 | 3 | --- 4 | 5 | model-name: CGAN 6 | 7 | backbone-name: CGAN 8 | 9 | module-type: cv 10 | 11 | fine-tunable: True 12 | 13 | model-version: 1.9 14 | 15 | train-dataset: mnist 16 | 17 | evaluation: - 18 | 19 | author: MindSpore team 20 | 21 | update-time: 2022-10-11 22 | 23 | repo-link: 24 | 25 | user-id: MindSpore 26 | 27 | used-for: inference 28 | 29 | mindspore-version: 1.9 30 | 31 | asset: 32 | 33 | - 34 | file-format: ckpt 35 | asset-link: 36 | asset-sha256: 360a455b190fc1a56cfb487ed25278794e86ea615458cf3066ca06e3b8ad3d41 37 | 38 | license: Apache2.0 39 | 40 | summary: CGAN is used for cv 41 | 42 | --- 43 | 44 | ## Introduction 45 | 46 | This MindSpore Hub model uses the implementation of CGAN from the MindSpore model zoo on Gitee at research/cv/CGAN. 47 | 48 | CGAN is a cv network. More details please refer to the MindSpore model zoo on Gitee at [research/cv/CGAN](https://gitee.com/mindspore/models/blob/r1.9/research/cv/CGAN/README.md). 49 | 50 | All parameters in the module are trainable. 51 | 52 | ## Citation 53 | 54 | [Conditional Generative Adversarial Nets](https://arxiv.org/pdf/1411.1784.pdf) 55 | 56 | ## Disclaimer 57 | 58 | MindSpore ("we") do not own any ownership or intellectual property rights of the datasets, and the trained models are provided on an "as is" and "as available" basis. We make no representations or warranties of any kind of the datasets and trained models (collectively, “materials”) and will not be liable for any loss, damage, expense or cost arising from the materials. Please ensure that you have permission to use the dataset under the appropriate license for the dataset and in accordance with the terms of the relevant license agreement. The trained models provided are only for research and education purposes. 59 | 60 | To Dataset Owners: If you do not wish to have a dataset included in MindSpore, or wish to update it in any way, we will remove or update the content at your request. Please contact us through GitHub or Gitee. Your understanding and contributions to the community are greatly appreciated. 61 | 62 | MindSpore is available under the Apache 2.0 license, please see the LICENSE file. 63 | -------------------------------------------------------------------------------- /mshub_res/assets/mindspore/1.9/dgu_udc.md: -------------------------------------------------------------------------------- 1 | # dgu 2 | 3 | --- 4 | 5 | model-name: dgu 6 | 7 | backbone-name: dgu 8 | 9 | module-type: nlp 10 | 11 | fine-tunable: True 12 | 13 | model-version: 1.9 14 | 15 | train-dataset: udc 16 | 17 | evaluation: R1acc82.14 | R2acc90.45 | R5acc97.75 18 | 19 | author: MindSpore team 20 | 21 | update-time: 2022-10-11 22 | 23 | repo-link: 24 | 25 | user-id: MindSpore 26 | 27 | used-for: inference 28 | 29 | mindspore-version: 1.9 30 | 31 | asset: 32 | 33 | - 34 | file-format: ckpt 35 | asset-link: 36 | asset-sha256: ef323d57071720e70df35c28d0d8cffcfafdefdfe694ca4c39b21d013f2b8542 37 | 38 | license: Apache2.0 39 | 40 | summary: dgu is used for nlp 41 | 42 | --- 43 | 44 | ## Introduction 45 | 46 | This MindSpore Hub model uses the implementation of dgu from the MindSpore model zoo on Gitee at official/nlp/dgu. 47 | 48 | dgu is a nlp network. More details please refer to the MindSpore model zoo on Gitee at [official/nlp/dgu](https://gitee.com/mindspore/models/blob/r1.9/official/nlp/dgu/README_CN.md). 49 | 50 | All parameters in the module are trainable. 51 | 52 | ## Disclaimer 53 | 54 | MindSpore ("we") do not own any ownership or intellectual property rights of the datasets, and the trained models are provided on an "as is" and "as available" basis. We make no representations or warranties of any kind of the datasets and trained models (collectively, “materials”) and will not be liable for any loss, damage, expense or cost arising from the materials. Please ensure that you have permission to use the dataset under the appropriate license for the dataset and in accordance with the terms of the relevant license agreement. The trained models provided are only for research and education purposes. 55 | 56 | To Dataset Owners: If you do not wish to have a dataset included in MindSpore, or wish to update it in any way, we will remove or update the content at your request. Please contact us through GitHub or Gitee. Your understanding and contributions to the community are greatly appreciated. 57 | 58 | MindSpore is available under the Apache 2.0 license, please see the LICENSE file. 59 | -------------------------------------------------------------------------------- /mshub_res/assets/mindspore/1.9/emotect_baidu.md: -------------------------------------------------------------------------------- 1 | # emotect 2 | 3 | --- 4 | 5 | model-name: emotect 6 | 7 | backbone-name: emotect 8 | 9 | module-type: nlp 10 | 11 | fine-tunable: True 12 | 13 | model-version: 1.9 14 | 15 | train-dataset: baidu 16 | 17 | evaluation: acc90.44 18 | 19 | author: MindSpore team 20 | 21 | update-time: 2022-10-11 22 | 23 | repo-link: 24 | 25 | user-id: MindSpore 26 | 27 | used-for: inference 28 | 29 | mindspore-version: 1.9 30 | 31 | asset: 32 | 33 | - 34 | file-format: ckpt 35 | asset-link: 36 | asset-sha256: 5b0ddb04f17f27d706007353d15faf93b627dcaf0ce7fcfcf1b61d4f2ddc228b 37 | 38 | license: Apache2.0 39 | 40 | summary: emotect is used for nlp 41 | 42 | --- 43 | 44 | ## Introduction 45 | 46 | This MindSpore Hub model uses the implementation of emotect from the MindSpore model zoo on Gitee at official/nlp/emotect. 47 | 48 | emotect is a nlp network. More details please refer to the MindSpore model zoo on Gitee at [official/nlp/emotect](https://gitee.com/mindspore/models/blob/r1.9/official/nlp/emotect/README_CN.md). 49 | 50 | All parameters in the module are trainable. 51 | 52 | ## Disclaimer 53 | 54 | MindSpore ("we") do not own any ownership or intellectual property rights of the datasets, and the trained models are provided on an "as is" and "as available" basis. We make no representations or warranties of any kind of the datasets and trained models (collectively, “materials”) and will not be liable for any loss, damage, expense or cost arising from the materials. Please ensure that you have permission to use the dataset under the appropriate license for the dataset and in accordance with the terms of the relevant license agreement. The trained models provided are only for research and education purposes. 55 | 56 | To Dataset Owners: If you do not wish to have a dataset included in MindSpore, or wish to update it in any way, we will remove or update the content at your request. Please contact us through GitHub or Gitee. Your understanding and contributions to the community are greatly appreciated. 57 | 58 | MindSpore is available under the Apache 2.0 license, please see the LICENSE file. 59 | -------------------------------------------------------------------------------- /mshub_res/assets/mindspore/1.9/hed_bsds500.md: -------------------------------------------------------------------------------- 1 | # hed 2 | 3 | --- 4 | 5 | model-name: hed 6 | 7 | backbone-name: hed 8 | 9 | module-type: cv 10 | 11 | fine-tunable: True 12 | 13 | model-version: 1.9 14 | 15 | train-dataset: bsds500 16 | 17 | evaluation: ODS77.2 18 | 19 | author: MindSpore team 20 | 21 | update-time: 2022-10-11 22 | 23 | repo-link: 24 | 25 | user-id: MindSpore 26 | 27 | used-for: inference 28 | 29 | mindspore-version: 1.9 30 | 31 | asset: 32 | 33 | - 34 | file-format: ckpt 35 | asset-link: 36 | asset-sha256: fad52b107d0e42f50b4331a36da3c733410a17840f31892a0498616da1dde8e9 37 | 38 | license: Apache2.0 39 | 40 | summary: hed is used for cv 41 | 42 | --- 43 | 44 | ## Introduction 45 | 46 | This MindSpore Hub model uses the implementation of hed from the MindSpore model zoo on Gitee at research/cv/hed. 47 | 48 | hed is a cv network. More details please refer to the MindSpore model zoo on Gitee at [research/cv/hed](https://gitee.com/mindspore/models/blob/r1.9/research/cv/hed/README.md). 49 | 50 | All parameters in the module are trainable. 51 | 52 | ## Citation 53 | 54 | [Holistically-Nested Edge Detection](https://arxiv.org/pdf/1504.06375.pdf) 55 | 56 | ## Disclaimer 57 | 58 | MindSpore ("we") do not own any ownership or intellectual property rights of the datasets, and the trained models are provided on an "as is" and "as available" basis. We make no representations or warranties of any kind of the datasets and trained models (collectively, “materials”) and will not be liable for any loss, damage, expense or cost arising from the materials. Please ensure that you have permission to use the dataset under the appropriate license for the dataset and in accordance with the terms of the relevant license agreement. The trained models provided are only for research and education purposes. 59 | 60 | To Dataset Owners: If you do not wish to have a dataset included in MindSpore, or wish to update it in any way, we will remove or update the content at your request. Please contact us through GitHub or Gitee. Your understanding and contributions to the community are greatly appreciated. 61 | 62 | MindSpore is available under the Apache 2.0 license, please see the LICENSE file. 63 | -------------------------------------------------------------------------------- /mshub_res/assets/mindspore/1.9/ipt_x2_set5.md: -------------------------------------------------------------------------------- 1 | # IPT 2 | 3 | --- 4 | 5 | model-name: IPT 6 | 7 | backbone-name: IPT 8 | 9 | module-type: cv 10 | 11 | fine-tunable: True 12 | 13 | model-version: 1.9 14 | 15 | train-dataset: set5 16 | 17 | evaluation: acc38.3 18 | 19 | author: MindSpore team 20 | 21 | update-time: 2022-10-11 22 | 23 | repo-link: 24 | 25 | user-id: MindSpore 26 | 27 | used-for: inference 28 | 29 | mindspore-version: 1.9 30 | 31 | asset: 32 | 33 | - 34 | file-format: ckpt 35 | asset-link: 36 | asset-sha256: b0faf955e2ed0a02a7f90dafac1af9f9eab2b657c4f373b5355a280e5c1f4403 37 | 38 | license: Apache2.0 39 | 40 | summary: IPT is used for cv 41 | 42 | --- 43 | 44 | ## Introduction 45 | 46 | This MindSpore Hub model uses the implementation of IPT from the MindSpore model zoo on Gitee at research/cv/IPT. 47 | 48 | IPT is a cv network. More details please refer to the MindSpore model zoo on Gitee at [research/cv/IPT](https://gitee.com/mindspore/models/blob/r1.9/research/cv/IPT/README.md). 49 | 50 | All parameters in the module are trainable. 51 | 52 | ## Citation 53 | 54 | [Pre-Trained Image Processing Transformer](https://arxiv.org/pdf/2012.00364.pdf) 55 | 56 | ## Disclaimer 57 | 58 | MindSpore ("we") do not own any ownership or intellectual property rights of the datasets, and the trained models are provided on an "as is" and "as available" basis. We make no representations or warranties of any kind of the datasets and trained models (collectively, “materials”) and will not be liable for any loss, damage, expense or cost arising from the materials. Please ensure that you have permission to use the dataset under the appropriate license for the dataset and in accordance with the terms of the relevant license agreement. The trained models provided are only for research and education purposes. 59 | 60 | To Dataset Owners: If you do not wish to have a dataset included in MindSpore, or wish to update it in any way, we will remove or update the content at your request. Please contact us through GitHub or Gitee. Your understanding and contributions to the community are greatly appreciated. 61 | 62 | MindSpore is available under the Apache 2.0 license, please see the LICENSE file. 63 | -------------------------------------------------------------------------------- /mshub_res/assets/mindspore/1.9/rcan_div2k.md: -------------------------------------------------------------------------------- 1 | # RCAN 2 | 3 | --- 4 | 5 | model-name: RCAN 6 | 7 | backbone-name: RCAN 8 | 9 | module-type: cv 10 | 11 | fine-tunable: True 12 | 13 | model-version: 1.9 14 | 15 | train-dataset: DIV2K 16 | 17 | evaluation: PSNR38.26 | SSIM0.9614 18 | 19 | author: MindSpore team 20 | 21 | update-time: 2022-10-12 22 | 23 | repo-link: 24 | 25 | user-id: MindSpore 26 | 27 | used-for: inference 28 | 29 | mindspore-version: 1.9 30 | 31 | asset: 32 | 33 | - 34 | file-format: ckpt 35 | asset-link: 36 | asset-sha256: f75418c61a5daa58c3e3499ad0551033bf3a1cce217054c7e01ab42615977286 37 | 38 | license: Apache2.0 39 | 40 | summary: RCAN is used for cv 41 | 42 | --- 43 | 44 | ## Introduction 45 | 46 | This MindSpore Hub model uses the implementation of RCAN from the MindSpore model zoo on Gitee at research/cv/RCAN. 47 | 48 | RCAN is a cv network. More details please refer to the MindSpore model zoo on Gitee at [research/cv/RCAN](https://gitee.com/mindspore/models/blob/r1.9/research/cv/RCAN/README.md). 49 | 50 | All parameters in the module are trainable. 51 | 52 | ## Disclaimer 53 | 54 | MindSpore ("we") do not own any ownership or intellectual property rights of the datasets, and the trained models are provided on an "as is" and "as available" basis. We make no representations or warranties of any kind of the datasets and trained models (collectively, “materials”) and will not be liable for any loss, damage, expense or cost arising from the materials. Please ensure that you have permission to use the dataset under the appropriate license for the dataset and in accordance with the terms of the relevant license agreement. The trained models provided are only for research and education purposes. 55 | 56 | To Dataset Owners: If you do not wish to have a dataset included in MindSpore, or wish to update it in any way, we will remove or update the content at your request. Please contact us through GitHub or Gitee. Your understanding and contributions to the community are greatly appreciated. 57 | 58 | MindSpore is available under the Apache 2.0 license, please see the LICENSE file. 59 | -------------------------------------------------------------------------------- /mshub_res/assets/noah-cvlab/static/images/ghostnet_static_img.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mindspore-ai/hub/7351873cc950650acd2c67c5d705c93f04667387/mshub_res/assets/noah-cvlab/static/images/ghostnet_static_img.png -------------------------------------------------------------------------------- /mshub_res/tools/get_sha256.py: -------------------------------------------------------------------------------- 1 | # Copyright 2020 Huawei Technologies Co., Ltd 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | # ============================================================================ 15 | """Tool for generate sha256 for given file path.""" 16 | 17 | import argparse 18 | import hashlib 19 | import os 20 | 21 | 22 | def parse_args(): 23 | args_parser = argparse.ArgumentParser("Get Sha256 from specific file") 24 | args_parser.add_argument("--file", type=str, required=True, help="which file to calculate the sha256") 25 | return args_parser.parse_args() 26 | 27 | 28 | def gen_sha256(path): 29 | if not os.path.exists(path): 30 | print(f"File {path} not exists") 31 | return 32 | 33 | with open(path, 'rb') as fr: 34 | content = fr.read() 35 | m = hashlib.sha256() 36 | m.update(content) 37 | print(f"sha256: {m.hexdigest()}") 38 | 39 | 40 | if __name__ == "__main__": 41 | args = parse_args() 42 | gen_sha256(args.file) 43 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | PyYAML>=5.3 2 | mistune==0.8.4 3 | mindspore>=0.7.0 4 | -------------------------------------------------------------------------------- /tests/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright 2020 Huawei Technologies Co., Ltd 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | # ============================================================================ 15 | -------------------------------------------------------------------------------- /tests/st/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright 2020 Huawei Technologies Co., Ltd 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | # ============================================================================ 15 | -------------------------------------------------------------------------------- /tests/ut/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright 2020 Huawei Technologies Co., Ltd 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | # ============================================================================ 15 | -------------------------------------------------------------------------------- /tests/ut/runtest.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | # Copyright 2020 Huawei Technologies Co., Ltd. 3 | # 4 | # Licensed under the Apache License, Version 2.0 (the "License"); 5 | # you may not use this file except in compliance with the License. 6 | # You may obtain a copy of the License at 7 | # 8 | # http://www.apache.org/licenses/LICENSE-2.0 9 | # 10 | # Unless required by applicable law or agreed to in writing, software 11 | # distributed under the License is distributed on an "AS IS" BASIS, 12 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 13 | # See the License for the specific language governing permissions and 14 | # limitations under the License. 15 | 16 | set -e 17 | 18 | SCRIPT_BASEDIR=$(realpath "$(dirname "$0")") 19 | 20 | PROJECT_DIR=$(realpath "$SCRIPT_BASEDIR/../../") 21 | UT_PATH="$PROJECT_DIR/tests/ut" 22 | 23 | run_test() { 24 | echo "Start to run test." 25 | cd "$PROJECT_DIR" || exit 26 | 27 | pytest "$UT_PATH" 28 | 29 | echo "Test all use cases success." 30 | } 31 | 32 | run_test 33 | -------------------------------------------------------------------------------- /tests/ut/test_list.py: -------------------------------------------------------------------------------- 1 | # Copyright 2020 Huawei Technologies Co., Ltd 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | # ============================================================================ 15 | """test hub_list""" 16 | 17 | from mindspore_hub import hub_list 18 | 19 | 20 | class TestHubList: 21 | """Test hub_list function.""" 22 | 23 | def test_hub_list(self): 24 | """ 25 | Feature: Test normal cases. 26 | Description: mindspore_hub.hub_list function 27 | Expectation: success. 28 | """ 29 | versions = (None, 'master', 'r1.8') 30 | force_reloads = (True, False) 31 | for version in versions: 32 | for force_reload in force_reloads: 33 | hub_list(version, force_reload) 34 | -------------------------------------------------------------------------------- /tests/ut/test_validator_md.py: -------------------------------------------------------------------------------- 1 | # Copyright 2020 Huawei Technologies Co., Ltd 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | # ============================================================================ 15 | """test validate md files.""" 16 | 17 | import os 18 | import random 19 | from mindspore_hub.info import CellInfo 20 | 21 | 22 | def _find_md(path, dst_depth, depth=1): 23 | """Find network md files.""" 24 | mds = [] 25 | files = os.listdir(path) 26 | if dst_depth < depth: 27 | return [] 28 | 29 | if dst_depth == depth: 30 | for f in files: 31 | tmp = os.path.join(path, f) 32 | if os.path.isfile(tmp) and tmp.endswith('.md'): 33 | mds.append(tmp) 34 | else: 35 | for f in files: 36 | tmp = os.path.join(path, f) 37 | if os.path.isdir(tmp): 38 | mds += _find_md(tmp, dst_depth, depth + 1) 39 | return mds 40 | 41 | 42 | def test_validate_md(): 43 | """Test validate md files in assets folder.""" 44 | current_dir = os.path.dirname(os.path.abspath(__file__)) 45 | asset_dir = os.path.join(current_dir, '../../mshub_res/assets') 46 | mds = _find_md(os.path.abspath(asset_dir), 3) 47 | for md in random.choices(mds, k=10): 48 | CellInfo(md) 49 | --------------------------------------------------------------------------------