├── .gitignore ├── CONTRIBUTING.md ├── Efficient-ConvBN-Blocks-for-Transfer-Learning-and-Beyond └── tune_mode_convbn.py ├── LICENSE ├── NOTICE ├── OWNERS ├── README.md ├── README_CN.md ├── RELEASE.md ├── SECURITY.md ├── application ├── A-Causal-U-net-based-Neural-Beamforming-Network │ └── cunet.py ├── ACGNet │ ├── README.md │ ├── loss.py │ ├── main.py │ └── model.py ├── AccSGD-Parallelizing-Stochastic-Gradient-Descent │ └── AccSGD.py ├── Adaptive-Nesterov-Momentum-Algorithm │ └── adan.py ├── Adversarial-Laser-Spot │ └── laser_simulation.py ├── An-Exploration-of-Conditioning-Methods │ ├── README.md │ ├── layers │ │ ├── __init__.py │ │ └── conditional.py │ └── main.py ├── Analysis-of-Black-Hole-Solutions-in-Parabolic-Class-Using-Neural-Networks │ └── parabolic_eq_solver.py ├── AoA │ ├── aoa_mindspore │ │ ├── __init__.py │ │ ├── aoa.py │ │ ├── demo1.py │ │ └── demo2.py │ └── saoa.png ├── Black-Box-Attacks-against │ ├── gradmax.py │ └── readme.md ├── CLIP-It │ ├── .gitignore │ ├── LICENSE │ ├── README.md │ ├── assets │ │ └── overview.png │ ├── clip_it.py │ ├── docker │ │ └── Dockerfile │ └── test.py ├── Castling-ViT │ ├── README.py │ └── attention.py ├── Class-balanced-loss-pytorch-master │ └── class_balanced_loss_ms.py ├── Conv2Former_Simple_Transformer │ ├── mindspore │ │ ├── __init__.py │ │ └── conv2former_mindspore.py │ ├── train_mindspore.py │ └── validate_mindspore.py ├── D-Unet_Mindspore │ ├── DUnet.py │ ├── DUnet_parts.py │ ├── loss.py │ └── workspace_test.py ├── DCTransformer │ ├── README.md │ ├── dctransformer.ipynb │ └── dctransformer.py ├── DNN-with-SimulatedAnnealing │ ├── README.md │ ├── main.py │ ├── neural_network.py │ └── simulated_annealing.py ├── Distributional-Reinforcement-Learning-with-Quantile-Regression │ └── logger.py ├── DoWG-Unleashed │ └── dowg.py ├── DropMax-Adaptive-Variational-Softmax │ └── dropmax.py ├── Duration-Informed-Attention-Network │ ├── deepmind_version.py │ ├── qr-dqn-solution-cool.py │ └── rl_utils.py ├── ECG-arrhythmia-classification │ ├── heartnet.py │ └── readme.md ├── EWC-Overcoming-catastrophic-forgetting │ ├── demo.ipynb │ └── elastic_weight_consolidation.py ├── Efficient ConvBN Blocks for Transfer Learning and Beyond │ └── tune_mode_convbn.py ├── FFperceptron │ ├── FFperceptron_MNIST.ipynb │ ├── FFperceptron_MNIST.py │ └── README.md ├── Fixup-Initialization │ └── model_build.py ├── Frank-Wolfe-Algorithm │ └── fgsm.py ├── GAN-master │ └── gan_ms.py ├── GTE │ ├── README.md │ ├── main.py │ ├── parser.py │ └── utils.py ├── Generalized-End-to-End-Loss-for-Speaker-Verification │ └── ge2e.py ├── HTM-mindspore │ ├── LICENSE │ ├── README.md │ ├── htm.png │ ├── htm_mindspore │ │ ├── __init__.py │ │ ├── htm_mindspore.py │ │ └── test_mindspore.py │ └── setup.py ├── HeuristicDropout │ ├── HeuristicDropout.py │ ├── README.MD │ └── TestModel.py ├── HyperDreamBooth │ └── model.py ├── Inverting-Visual-Representations-with-Convolutional-Networks │ └── inversion.py ├── Investigating │ ├── data │ │ ├── generated_showerthoughts_4_gpt2.txt │ │ ├── generated_showerthoughts_4_neo.txt │ │ ├── generated_showerthoughts_ChatGPT_df_1.pkl │ │ ├── generated_showerthoughts_ChatGPT_df_2.pkl │ │ ├── generated_showerthoughts_ChatGPT_df_3.pkl │ │ ├── roberta_test_data_GPT2.ndjson │ │ ├── roberta_test_data_chatGPT.ndjson │ │ ├── roberta_test_data_mixed.ndjson │ │ ├── roberta_train_data_GPT2.ndjson │ │ ├── roberta_train_data_chatGPT.ndjson │ │ └── roberta_train_data_mixed.ndjson │ └── showerthought_dataset.py ├── Jeffreys-divergence-based │ ├── README.md │ └── main.py ├── KPC-cF-main │ └── dual_filtering_temp_ver_ms.py ├── Kronecker-Attention-Networks │ ├── README.md │ └── kronecker_attention.py ├── LGL-INR-master │ └── LGL-INR-master │ │ └── LGLINR_ms.py ├── Large-Batch-Optimization-for-Deep-Learning │ └── lamb.py ├── Large-Margin-Deep-Networks-for-Classification │ ├── large_margin.py │ └── mnist.ipynb ├── Learning-to-Upsample │ ├── LICENSE │ ├── README.md │ ├── complexity.jpg │ └── dysample.py ├── Learning-with-Noisy-Labels │ └── NCOD.py ├── MCA │ ├── MCA.py │ └── README.md ├── NBC-Softmax │ ├── NBC-Softmax.py │ └── README.md ├── NeBLa │ ├── README.py │ ├── image_encoder.py │ ├── mlp.py │ └── point_embedder.py ├── Neural-Network-based-Speech-Enhancement │ └── network.py ├── Neural-Side-by-Side │ ├── LICENSE │ ├── README.md │ └── model.py ├── OTCE │ ├── README.md │ └── otce.py ├── On-the-Convergence-of-AdaBound-and-its-Connection-to-SGD │ └── csgd.py ├── Projected-Distribution │ ├── README.md │ └── pdl.py ├── PyDSN │ ├── demo.py │ └── loss_mindspore.py ├── RAF-ms-main │ └── raf_ms.py ├── RITA │ └── compute_fitness.py ├── RawNet │ ├── README.md │ └── model.py ├── ReSeg │ ├── README.md │ ├── ReNet.py │ └── train_model.py ├── Real-NVP │ ├── loss_mindspore.py │ ├── network_mindspore.py │ ├── output_figures │ │ ├── Figure_1.png │ │ ├── Figure_2.png │ │ ├── Figure_3.png │ │ ├── Figure_4.png │ │ ├── Figure_5.png │ │ ├── Figure_6.png │ │ ├── Figure_7.png │ │ ├── Figure_8.png │ │ └── Figure_9.png │ └── train_mindspore.py ├── S2DNet-Minimal │ ├── README.md │ ├── adap_layers.py │ ├── main.py │ ├── s2dnet.py │ └── vgg16.py ├── SGDR │ ├── CosineAnnealingWithRestartsLR.py │ └── readme.md ├── Scalable-Sharpness-Aware-Minimization │ └── looksam.py ├── SciNet │ ├── Analysis.ipynb │ ├── Generate_Trainingdata.ipynb │ ├── README.md │ ├── Training.ipynb │ ├── models.py │ └── utils.py ├── SeesawLoss-master │ └── seesaw_loss_ms.py ├── Spatial and Temporal Mutual Promotion for Video-based Person Re-identification │ └── ruu.py ├── Spatial-Information-Considered-Network │ └── SIC_NET.py ├── Sub2Full-OCT-Denoising │ ├── output_ms.png │ ├── output_torch.png │ ├── pytorch2mindspore.py │ ├── target.png │ ├── test.png │ ├── test.py │ └── unet.py ├── TRSTMI │ ├── README.md │ ├── TrustRegion.py │ ├── requirements.txt │ └── trstmi.py ├── TSFF │ └── paper_model.py ├── TSGD │ ├── README.md │ └── tsgd │ │ ├── __init__.py │ │ ├── main.py │ │ └── tsgd.py ├── The_RefinedWeb_Dataset_for_Falcon │ ├── data │ │ ├── expert_factor.csv │ │ ├── news_factor.csv │ │ └── wiki_factor.csv │ ├── eval_factuality_mindspore.py │ └── requirements.txt ├── Three-Dimensional-Lip-Motion-Network-for-Text-Independent-Speaker-Recognition-master │ └── 3LMNet_ms.py ├── Tom │ └── tom.py ├── Two-Layer-ReLU-Network-Analytically │ └── anmin.py ├── WaveCRN-master │ └── model_ms.py ├── WinoGrande │ ├── README.md │ ├── data │ │ ├── easy │ │ │ ├── gpt_generations.txt │ │ │ ├── pool_200.tsv │ │ │ ├── pool_400.tsv │ │ │ └── test_100.tsv │ │ ├── hard │ │ │ ├── gpt_generations.txt │ │ │ ├── pool_400.tsv │ │ │ └── test_100.tsv │ │ └── medium │ │ │ ├── gpt_generations.txt │ │ │ └── test_100.tsv │ └── generate_gpt3_explanations.py ├── __init__.py ├── a-self-attentive-model-for-knowledge-tracing │ ├── README.md │ └── model.py ├── adam-aio │ ├── AdamAIO.py │ └── README.md ├── alibi │ ├── README.md │ ├── alibi │ │ ├── __init__.py │ │ ├── attention.py │ │ ├── config.py │ │ ├── layers.py │ │ └── model.py │ └── main.py ├── auxiliary-tasks-in-multi-task-learning │ └── test.py ├── aves │ ├── README.md │ ├── aves-base-bio.json │ └── aves.py ├── circle-loss │ ├── README.md │ └── circle_loss.py ├── comparing-the-efficacy │ ├── README.md │ └── sampler.py ├── coneheads-hierarchy-aware-attention │ ├── README.md │ ├── penumbral.py │ └── umbral.py ├── contextual-learning │ ├── FCT.py │ └── README.md ├── corss-dataset-training │ ├── ms_cross_focal_loss.py │ └── readme.md ├── cross-transformers-spatially-aware-few-shot-transfer │ ├── README.md │ └── crosstransformers.py ├── delving-deeper-into-convolutional │ ├── Conv-GRU.py │ └── README.md ├── dice-loss-for-data-imbalanced-nlp-tasks │ ├── README.md │ ├── loss.py │ └── test_loss.py ├── drop-an-octave-reducing-spatial │ ├── octconv.py │ └── readme.md ├── dropblock-a-regularization-method │ ├── README.md │ └── layer.py ├── dynamic-relu │ ├── README.md │ └── dynamic_relu.py ├── effnet-an-efficient-structure │ ├── README.md │ └── effnet.py ├── entropy-law │ ├── README.md │ ├── ZIP.py │ ├── formatter.py │ ├── motivation.png │ └── script.sh ├── factorized-attention │ ├── README.md │ └── efficient_attention.py ├── fake-new-dectection │ └── GCNN_main.py ├── falformer │ ├── README.md │ ├── feature_based_clustering.py │ └── kmeans.py ├── fastformer-additive │ ├── Fastformer.py │ └── README.md ├── fed-ensemble-main-ms │ ├── fedEnsemble_ms.py │ ├── main_ms.py │ ├── models_ms.py │ └── resnet50.py ├── fisheradversarial-mindspore │ ├── README.md │ ├── fisherform.py │ ├── fishertrace.py │ ├── setup.py │ └── usage.py ├── fixed-classifier │ ├── README.md │ └── fixed_proj.py ├── from-softmax-to-sparsemax-a-sparse │ ├── README.md │ ├── sparsemax.py │ └── test.py ├── gaitpart-temporal-part-based │ ├── README.md │ └── layers.py ├── gated-word-character-recurrent-language-model │ ├── model.py │ └── readme.md ├── gaussian_adaptive_attention │ ├── README.md │ └── gaussian_adaptive_attention │ │ ├── GaussianBlock.py │ │ ├── __init__.py │ │ └── main.py ├── gdn │ ├── README.md │ ├── gdn.py │ └── main.py ├── generalizing-and-decoupling-neural-collapse │ ├── HUG_loss.py │ └── README.md ├── group-normalization │ ├── README.md │ └── group_norm.py ├── hyperspherical-consistency-regularization │ ├── README.md │ └── hcr.py ├── identifying-untrustworthy-prediction │ ├── CSM.py │ └── main.py ├── isr │ ├── README.md │ ├── isr.py │ └── swin_transformer │ │ ├── features.py │ │ ├── helper.py │ │ ├── layers.py │ │ ├── model.py │ │ ├── registry.py │ │ └── utils │ │ ├── download.py │ │ └── path.py ├── iterative-a-de-blending │ └── iadb.py ├── jko_wass_grad │ ├── jko_gradient_mindspore.py │ ├── jko_wass_grad_mindspore.py │ └── pth2pkl.py ├── layer-normalization │ ├── GRU_layernorm_cell.py │ └── README.md ├── lcn-master-ms │ └── local_context_norm_ms.py ├── learning-an-explicit-hyperparameter │ ├── README.md │ └── protonet_Tanh.py ├── light-head │ ├── README.md │ └── light_head.py ├── local-relation-networks-for-image-recognition │ ├── README.md │ └── layer.py ├── locally-consistent-deformable-convolution │ ├── README.md │ └── def_conv.py ├── localquantumannealing │ ├── README.md │ ├── localquantumannealing │ │ ├── __init__.py │ │ ├── lqa.py │ │ └── lqa_basic.py │ └── test.py ├── lru │ ├── README.md │ └── lru.py ├── lsuv-init │ ├── README.md │ └── lsuv_init.py ├── matthews-correlation │ ├── Example.ipynb │ ├── README.md │ └── loss.py ├── mgan-mindspore │ ├── LICENSE │ ├── README.md │ ├── crop_images.py │ ├── images │ │ ├── faces-sample.png │ │ └── mgan.png │ ├── init_data.sh │ ├── models.py │ └── train.py ├── misinfo-reaction-frames │ └── demo.py ├── model-conversion-via │ ├── README.md │ └── utils.py ├── multi-adversarial-domain-adaptation │ ├── README.md │ └── mada.py ├── multi-task-learning-using-uncertainty-to │ ├── README.md │ └── code.py ├── multimodal-trajectory │ ├── README.md │ └── mtp_loss.py ├── multirate-training-of-neural-networks │ ├── Optimizer_multirate.py │ └── README.md ├── mvn2vec │ ├── README.md │ └── mvn2vec-reg.py ├── neural-audio-synthesis-wavenet │ ├── README.md │ ├── WaveNetEncodeTests.ipynb │ └── WaveNetEncoder.py ├── nystrom-attention │ ├── nystrom_attention │ │ ├── _init_.py │ │ └── nystrom_attention.py │ ├── setup.py │ ├── test_attn.py │ └── test_model.py ├── open-domain-dialogue │ ├── README.md │ ├── config.py │ ├── functions.py │ └── testdata.csv ├── openfashionclip-vision-and-language │ ├── README.md │ ├── maxi_dress.jpg │ └── quick_start.py ├── optimize-coordconv │ ├── README.md │ └── modules.py ├── perceptual-score │ ├── README.md │ ├── first_option.py │ └── second_option.py ├── polyloss │ ├── APLLoss.py │ └── README.md ├── privacy-enhancement │ ├── README.md │ └── noise.py ├── pro │ ├── README.md │ ├── demo.py │ └── pro.py ├── pruning-artificial-neural-networks │ ├── how_to_use-ms.ipynb │ └── src │ │ └── PSPentropy.py ├── random_quantize │ ├── README.md │ └── randomized_quantization.py ├── recurrent-highway-network │ ├── README.md │ ├── recurrent_highway_network.py │ └── test_rhn.py ├── refining-activation │ ├── readme.md │ └── softpool.py ├── res_mlp_ms │ └── res_mlp_ms.py ├── riemann-noise │ └── riemann_noise_ms.py ├── robustness_depth_lang │ ├── VPD │ │ └── models_ms.py │ ├── data │ │ └── readme.txt │ ├── model │ │ └── readme.txt │ └── sample_scripts │ │ ├── create_depth_embeddings_ms.py │ │ └── depth_file_list.txt ├── root-mean-square-layer-normalization │ ├── README.md │ └── norm.py ├── sa-mlp-distilling-graph │ ├── README.md │ └── model.py ├── segmentation-dataset │ ├── README.md │ └── dataloader.py ├── shallow-rnns │ ├── LICENSE │ ├── README.md │ └── model │ │ ├── __init__.py │ │ └── sharnn.py ├── similarity-of-neural-network │ ├── CKA.py │ └── README.md ├── smu │ ├── README.md │ └── smu.py ├── squeeze-and-attention-networks-for-semantic │ ├── README.md │ └── SABlock.py ├── ssFPN │ ├── README.md │ └── ssFPN.py ├── stable-sam │ ├── README.md │ ├── sam.py │ └── usam.py ├── stochastic-attention-head-removal-a-simple │ ├── README.md │ ├── attention.py │ └── head_removal.py ├── swish-t │ ├── README.md │ └── swisht.py ├── temporal-and-aspectual-entailment │ ├── Readme.md │ └── mindspore_models.py ├── the-Soft-Nearest-Neighbor-Loss │ └── snnl.py ├── translating-math-formula-images │ ├── README.md │ ├── positionalembedding2d.py │ └── visualization.ipynb ├── vat │ ├── README.md │ └── vat.py ├── weakly-supervised │ ├── README.md │ └── boneloss.py └── wide-minima-density-hypothesis │ ├── README.md │ ├── knee_lr_schedule.py │ ├── minima_width_compute.py │ ├── plots │ ├── 0explore │ │ ├── 0explore_acc-1.png │ │ └── 0explore_sharpness-1.png │ ├── 100explore │ │ ├── 100explore_acc-1.png │ │ └── 100explore_sharpness-1.png │ ├── 30explore │ │ ├── 30explore_acc-1.png │ │ └── 30explore_sharpness-1.png │ └── 60explore │ │ ├── 60explore_acc-1.png │ │ └── 60explore_sharpness-1.png │ └── tables │ ├── epoch_savings.PNG │ ├── full_budget.PNG │ ├── short-budget.PNG │ └── sota_iwslt.PNG ├── dataset ├── A_Curb_Dataset │ ├── README.md │ └── vis_data.py └── TransNAS-Bench-101 │ ├── README.md │ ├── api │ ├── __init__.py │ └── api_mini.py │ ├── example.py │ └── models │ ├── net_infer │ ├── __init__.py │ ├── cell_micro.py │ └── net_macro.py │ ├── net_ops │ ├── __init__.py │ ├── cell_ops.py │ ├── he_normal.py │ ├── meters.py │ ├── norm.py │ ├── resnet.py │ └── spectral_norm.py │ ├── task_models │ ├── decoder.py │ ├── discriminator.py │ ├── encoder.py │ ├── feedforward.py │ ├── gan.py │ ├── segmentation.py │ └── siamese.py │ └── utils │ └── utils.py ├── intern ├── A-Note-on-the-Inception-Score │ ├── README.md │ └── inception_score.py ├── Adaptive-Weighted-Discriminator-for-Training-Generative-Adversarial-Networks │ ├── README.md │ └── aw_loss.py ├── Automatic-Feature-Interaction │ ├── models.py │ └── readme.md ├── Block_Model │ ├── README.md │ └── block_model.py ├── Case_TranslationInvariantAttn │ ├── README.md │ └── model │ │ ├── tisa_ms.py │ │ └── tisa_pytorch.py ├── Corella │ ├── Corella.py │ └── README.md ├── D-Unet_Mindspore │ ├── DUnet.py │ ├── DUnet_parts.py │ ├── loss.py │ └── workspace_test.py ├── Datasets-Models-main │ ├── Models.py │ └── README.md ├── Deep-Edge-Aware-Interactive │ ├── example │ │ ├── enh_inputs.png │ │ ├── gt.png │ │ └── org_inputs.png │ └── ms_cdr.py ├── Deep-Gradient-Compression │ ├── README.md │ └── ms_dgc.py ├── Deep_Feature_Factorization_For_Concept_Discovery │ ├── demo.py │ ├── nmf.py │ └── utils.py ├── Deformable_Patch_Representation │ ├── README.md │ └── deformable_patch_representation.py ├── Fourier-Features-Let-Networks-Learn-High-Frequency │ ├── data.py │ ├── model.py │ ├── night.jpg │ └── train.py ├── GR-HSTU │ └── hstu.py ├── HTM-mindspore │ ├── LICENSE │ ├── README.md │ ├── htm.png │ ├── htm_mindspore │ │ ├── __init__.py │ │ ├── htm_mindspore.py │ │ └── test_mindspore.py │ └── setup.py ├── Hyena-A-Convolutional-Neural-Network-for-Modelling-Sentences │ └── hyena.py ├── IEBBMP │ ├── Iterpretable-mindspore-submit.py │ └── README ├── INR-Implicit-Neural-Representations-with-Periodic-Activation-Functions │ ├── INR_images.ipynb │ ├── data_processor.py │ ├── network.py │ └── samples │ │ ├── bert.jpg │ │ └── ernie.jpg ├── Integrated-Gradient │ ├── README.md │ ├── assets │ │ ├── heatmap.jpg │ │ └── n01669191_46.JPEG │ ├── ig.py │ ├── result.png │ └── show.py ├── Knowing-When-to-Look-Adaptive-Attention │ └── adaptive_attention.py ├── Last-Query-Transformer-RNN │ └── last_query_model.py ├── Least_Generative │ ├── Least Generative_mindspore.ipynb │ └── README.md ├── Luminance-Guided-Chrominance-Enhancement-for-HEVC-Intra-Coding │ ├── README.md │ ├── network.py │ ├── submodule.py │ └── test.py ├── MicAugment │ ├── README.md │ ├── mic_augment_mindspore.py │ └── test_mic_augment.py ├── NBNet-Noise-Basis-Learning-for-Image-Denoising-with-Subspace-Projection │ ├── NBNet.py │ └── README.md ├── Occupational-Biases-in-Norwegian-and-Multilingual-Language-Models │ ├── README.md │ ├── codes │ │ └── compute_scores.py │ ├── gold_data │ │ └── gender_equality_NSB.ods │ └── templates │ │ ├── templates_er.txt │ │ └── templates_jobber_som.txt ├── PCGrad-mindspore-example │ └── pcgrad-example._ms.py ├── PR_Product │ ├── PR.py │ └── README.md ├── Patient2Vec-A-Personalized-Interpretable │ ├── Patient2Vec.png │ ├── Patient2Vec.py │ └── README.rst ├── PolyLoss │ ├── README.md │ ├── polyloss_mindspore.py │ └── test_polyloss.py ├── Profiling-Pareto-Front │ ├── README.md │ ├── ms_moosvgd.py │ ├── ms_run_zdt_moosvgd.py │ └── ms_zdt_functions.py ├── QuantExplainNLP_AlgoAnalysis │ ├── README.md │ ├── data_preprocessing.py │ ├── explainability.py │ ├── models.py │ ├── notebook │ │ ├── ADMISSIONS.csv │ │ ├── DIAGNOSES_ICD.csv │ │ ├── NOTEEVENTS.csv │ │ └── PROCEDURES_ICD.csv │ └── train.py ├── Quiz_gen │ ├── Quiz_Gen_mindspore_main.ipynb │ └── README.md ├── README.md ├── RGNN │ ├── README.md │ ├── model_simple.py │ └── test.py ├── S2Wrapper │ ├── readme.md │ └── s2wrapper.py ├── SING │ ├── SING.py │ ├── main.py │ └── readme.md ├── SMOTE │ ├── README.md │ └── smote_mindspore.py ├── STCrowd │ ├── STCrowd_convert.py │ └── read.md ├── SV-X-Softmax │ ├── readme.md │ └── sv-x-softmax.py ├── SVGD │ ├── 1D_Gaussian_mixture.py │ ├── SVGD.py │ └── multivariate_normal.py ├── ShortFuse-Biomedical-Time-Series │ └── Hybrid_CNN.py ├── Simba │ ├── README.md │ ├── main.py │ └── simba.py ├── Sketch2art │ ├── README.md │ ├── example.png │ └── module.py ├── SmeLU-master │ ├── example_ms.py │ ├── smelu │ │ ├── __init__.py │ │ └── smelu_ms.py │ └── speed_test_ms.py ├── Super-Resolution-for-Root-Imaging │ ├── model_FSRCNN_mindspore.py │ └── model_srgan_mindspore.py ├── TSA_mindspore │ └── TSA_crossentropy_loss.py ├── Translation-Invariant │ └── model │ │ └── tisa.py ├── UE-Unified-Embedding-Battle-Tested-Feature │ ├── test.py │ └── ue.py ├── Uncertainty_Calibration_Object_Detection │ ├── metrics.py │ └── utils_.py ├── VAE-Creative-Discovery-using-QD-Search │ └── VAE2.py ├── ZoneoutRNN │ ├── README.md │ ├── ZoneoutRNN.py │ └── test.py ├── bi-tempered-loss-pytorch │ ├── README.md │ ├── bi_tempered_loss.py │ ├── fusion_result.json │ └── test_loss.py ├── cyclic-cosine-decay-master │ ├── example.ipynb │ └── scheduler.py ├── dora │ ├── README.md │ └── dora.py ├── fisheradversarial-mindspore │ ├── README.md │ ├── fisherform.py │ ├── fishertrace.py │ ├── setup.py │ └── usage.py ├── hyperparameters-part1 │ ├── README.md │ ├── main.py │ ├── onecyclec_mindspore.py │ └── sched.png ├── iwae │ ├── README.md │ └── iwae.py ├── med-flamingo │ ├── scripts │ │ ├── convert_weights_ms.py │ │ ├── demo.py │ │ └── demo_utils.py │ └── src │ │ ├── __init__.py │ │ ├── ms_wrapper.py │ │ └── utils.py ├── mgan-mindspore │ ├── LICENSE │ ├── README.md │ ├── crop_images.py │ ├── images │ │ ├── faces-sample.png │ │ └── mgan.png │ ├── init_data.sh │ ├── models.py │ └── train.py ├── nalu.ms │ ├── images │ │ └── 1.png │ ├── mlp_ms.py │ ├── nalu_ms.py │ └── train_ms.ipynb ├── nystrom-attention │ ├── nystrom_attention │ │ ├── _init_.py │ │ └── nystrom_attention.py │ ├── setup.py │ ├── test_attn.py │ └── test_model.py ├── pixelshuffle1d │ ├── README.md │ ├── demo.py │ └── pixelshuffle1d.py ├── pytorch_convgru │ ├── README.md │ ├── demo.ipynb │ ├── demo_convgru.ipynb │ ├── mindspore_convgru.py │ ├── mindspore_convgru_model.py │ └── requirements.txt ├── rand_conv-master │ ├── images │ │ ├── glasses.jpg │ │ └── robot.jpg │ └── rand_conv_ms.py ├── shallow-rnns │ ├── LICENSE │ ├── README.md │ └── model │ │ ├── __init__.py │ │ └── sharnn.py ├── signSGD │ ├── README │ └── gradient-compression-mindspore.py ├── temporal-normalizing-flows │ ├── affine_flow.py │ └── readme.md ├── text-to-music │ ├── inference_mindspore.py │ ├── input_text.txt │ ├── output_tunes │ │ └── Mon_11_Nov_2024_21_15_04.abc │ └── readme.md └── token_learner │ ├── README.md │ └── tokenlearner.py ├── papers ├── AECRNet │ ├── README.md │ ├── data.py │ ├── images │ │ ├── model.png │ │ ├── results.png │ │ ├── trade-off.png │ │ └── visual.png │ ├── losses │ │ ├── __init__.py │ │ ├── contras_loss.py │ │ └── loss.py │ ├── main.py │ ├── models │ │ ├── DCN.py │ │ ├── config.py │ │ ├── model.py │ │ └── vgg_model.py │ ├── option.py │ ├── test.py │ ├── train.py │ ├── train_ori0.py │ └── utils │ │ ├── __init__.py │ │ └── var_init.py ├── AGW │ ├── README.md │ ├── agw_config.yaml │ ├── eval.py │ ├── src │ │ ├── agw.py │ │ ├── data │ │ │ ├── __init__.py │ │ │ ├── dataset.py │ │ │ ├── datasets_define.py │ │ │ └── transforms.py │ │ ├── metrics │ │ │ ├── distance.py │ │ │ └── rank.py │ │ ├── resnet.py │ │ └── utils │ │ │ ├── __init__.py │ │ │ ├── config.py │ │ │ ├── local_adapter.py │ │ │ ├── loss.py │ │ │ └── lr_generator.py │ └── train.py ├── AVA_cifar │ ├── README.md │ ├── eval.py │ ├── scripts │ │ ├── run_eval.sh │ │ ├── run_eval_gpu.sh │ │ ├── run_train.sh │ │ └── run_train_gpu.sh │ ├── src │ │ ├── RandAugment │ │ │ ├── __init__.py │ │ │ └── augmentations.py │ │ ├── autoaugment.py │ │ ├── callbacks.py │ │ ├── cifar_resnet.py │ │ ├── config.py │ │ ├── datasets.py │ │ ├── knn_eval.py │ │ ├── loss.py │ │ ├── lr_schedule.py │ │ ├── network_define.py │ │ └── optimizer.py │ └── train.py ├── AVA_hpa │ ├── .idea │ │ ├── AVA_hpa.iml │ │ ├── inspectionProfiles │ │ │ └── profiles_settings.xml │ │ ├── misc.xml │ │ ├── modules.xml │ │ ├── vcs.xml │ │ └── workspace.xml │ ├── README.md │ ├── enhanced.csv │ ├── eval.py │ ├── pretrain.py │ ├── scripts │ │ ├── run_eval.sh │ │ ├── run_eval_gpu.sh │ │ ├── run_pretrain.sh │ │ ├── run_pretrain_gpu.sh │ │ ├── run_train.sh │ │ └── run_train_gpu.sh │ ├── src │ │ ├── RandAugment │ │ │ ├── __init__.py │ │ │ └── augmentations.py │ │ ├── callbacks.py │ │ ├── config.py │ │ ├── datasets.py │ │ ├── eval_metrics.py │ │ ├── loss.py │ │ ├── lr_schedule.py │ │ ├── network_define_eval.py │ │ ├── network_define_pretrain.py │ │ ├── network_define_train.py │ │ └── resnet.py │ └── train.py ├── Alexnet-ABeffect │ ├── AB.png │ ├── ABM.png │ ├── ABM1.png │ ├── README.md │ ├── alexnet_feature_extraction.py │ ├── category.png │ ├── feature_predict_abm.py │ ├── features_extracted_0616 │ │ ├── features_animal.pickle │ │ └── features_object.pickle │ ├── lrdata_0801 │ │ ├── lrmodel_cnn1.pickle │ │ ├── lrmodel_cnn2.pickle │ │ ├── lrmodel_cnn3.pickle │ │ ├── lrmodel_cnn4.pickle │ │ ├── lrmodel_cnn5.pickle │ │ ├── lrmodel_cnn6.pickle │ │ └── lrmodel_cnn7.pickle │ └── test50_feature │ │ └── test50_feature.pickle ├── BLS │ ├── BLSBasic │ │ ├── BLS.py │ │ └── __init__.py │ ├── BLSIncremental │ │ └── __init__.py │ ├── __init__.py │ ├── checkpoints │ │ └── ms_bls_basic.ckpt │ ├── train.py │ └── utils │ │ ├── BLS_load_checkpoint.py │ │ ├── BLS_save_checkpoint.py │ │ └── math.py ├── CAJ │ ├── ChannelAug.py │ ├── README.md │ ├── eval.py │ ├── pre_process_sysu.py │ ├── scripts │ │ ├── eval.sh │ │ └── run.sh │ ├── src │ │ ├── dataset.py │ │ ├── evalfunc.py │ │ ├── loss.py │ │ ├── models │ │ │ ├── embednet.py │ │ │ ├── resnet.py │ │ │ └── trainingcell.py │ │ └── utils.py │ └── train.py ├── CME │ ├── README.md │ ├── cfg │ │ ├── darknet19_448.cfg │ │ ├── darknet_dynamic.cfg │ │ ├── fewshot │ │ │ ├── metatune.data │ │ │ ├── metatune_10shot.data │ │ │ ├── metatune_10shot_split1.data │ │ │ ├── metatune_10shot_split2.data │ │ │ ├── metatune_10shot_split3.data │ │ │ ├── metatune_1shot.data │ │ │ ├── metatune_1shot_split1.data │ │ │ ├── metatune_1shot_split2.data │ │ │ ├── metatune_1shot_split3.data │ │ │ ├── metatune_2shot.data │ │ │ ├── metatune_2shot_split1.data │ │ │ ├── metatune_2shot_split2.data │ │ │ ├── metatune_2shot_split3.data │ │ │ ├── metatune_3shot.data │ │ │ ├── metatune_3shot_split1.data │ │ │ ├── metatune_3shot_split2.data │ │ │ ├── metatune_3shot_split3.data │ │ │ ├── metatune_5shot.data │ │ │ ├── metatune_5shot_split1.data │ │ │ ├── metatune_5shot_split2.data │ │ │ ├── metatune_5shot_split3.data │ │ │ ├── metatune_split1.data │ │ │ ├── metatune_split2.data │ │ │ ├── metatune_split3.data │ │ │ ├── metayolo.data │ │ │ ├── metayolo_split1.data │ │ │ ├── metayolo_split2.data │ │ │ └── metayolo_split3.data │ │ ├── reweighting_net.cfg │ │ ├── reweighting_net_decoupling.cfg │ │ ├── tiny-yolo-voc.cfg │ │ ├── voc.data │ │ ├── yolo-voc.cfg │ │ └── yolo.cfg │ ├── data │ │ ├── coco.names │ │ ├── coco_full_10shot.txt │ │ ├── coco_full_30shot.txt │ │ ├── coco_novels.txt │ │ ├── coco_traindict_full.txt │ │ ├── coco_trainvaldict_full.txt │ │ ├── voc.names │ │ ├── voc_novels.txt │ │ ├── voc_novels_split1.txt │ │ ├── voc_novels_split2.txt │ │ ├── voc_novels_split3.txt │ │ ├── voc_traindict_bbox_10shot.txt │ │ ├── voc_traindict_bbox_1shot.txt │ │ ├── voc_traindict_bbox_2shot.txt │ │ ├── voc_traindict_bbox_3shot.txt │ │ ├── voc_traindict_bbox_5shot.txt │ │ ├── voc_traindict_full.txt │ │ └── vocsplit │ │ │ ├── box_10shot_aeroplane_train.txt │ │ │ ├── box_10shot_bicycle_train.txt │ │ │ ├── box_10shot_bird_train.txt │ │ │ ├── box_10shot_boat_train.txt │ │ │ ├── box_10shot_bottle_train.txt │ │ │ ├── box_10shot_bus_train.txt │ │ │ ├── box_10shot_car_train.txt │ │ │ ├── box_10shot_cat_train.txt │ │ │ ├── box_10shot_chair_train.txt │ │ │ ├── box_10shot_cow_train.txt │ │ │ ├── box_10shot_diningtable_train.txt │ │ │ ├── box_10shot_dog_train.txt │ │ │ ├── box_10shot_horse_train.txt │ │ │ ├── box_10shot_motorbike_train.txt │ │ │ ├── box_10shot_person_train.txt │ │ │ ├── box_10shot_pottedplant_train.txt │ │ │ ├── box_10shot_sheep_train.txt │ │ │ ├── box_10shot_sofa_train.txt │ │ │ ├── box_10shot_train_train.txt │ │ │ ├── box_10shot_tvmonitor_train.txt │ │ │ ├── box_1shot_aeroplane_train.txt │ │ │ ├── box_1shot_bicycle_train.txt │ │ │ ├── box_1shot_bird_train.txt │ │ │ ├── box_1shot_boat_train.txt │ │ │ ├── box_1shot_bottle_train.txt │ │ │ ├── box_1shot_bus_train.txt │ │ │ ├── box_1shot_car_train.txt │ │ │ ├── box_1shot_cat_train.txt │ │ │ ├── box_1shot_chair_train.txt │ │ │ ├── box_1shot_cow_train.txt │ │ │ ├── box_1shot_diningtable_train.txt │ │ │ ├── box_1shot_dog_train.txt │ │ │ ├── box_1shot_horse_train.txt │ │ │ ├── box_1shot_motorbike_train.txt │ │ │ ├── box_1shot_person_train.txt │ │ │ ├── box_1shot_pottedplant_train.txt │ │ │ ├── box_1shot_sheep_train.txt │ │ │ ├── box_1shot_sofa_train.txt │ │ │ ├── box_1shot_train_train.txt │ │ │ ├── box_1shot_tvmonitor_train.txt │ │ │ ├── box_2shot_aeroplane_train.txt │ │ │ ├── box_2shot_bicycle_train.txt │ │ │ ├── box_2shot_bird_train.txt │ │ │ ├── box_2shot_boat_train.txt │ │ │ ├── box_2shot_bottle_train.txt │ │ │ ├── box_2shot_bus_train.txt │ │ │ ├── box_2shot_car_train.txt │ │ │ ├── box_2shot_cat_train.txt │ │ │ ├── box_2shot_chair_train.txt │ │ │ ├── box_2shot_cow_train.txt │ │ │ ├── box_2shot_diningtable_train.txt │ │ │ ├── box_2shot_dog_train.txt │ │ │ ├── box_2shot_horse_train.txt │ │ │ ├── box_2shot_motorbike_train.txt │ │ │ ├── box_2shot_person_train.txt │ │ │ ├── box_2shot_pottedplant_train.txt │ │ │ ├── box_2shot_sheep_train.txt │ │ │ ├── box_2shot_sofa_train.txt │ │ │ ├── box_2shot_train_train.txt │ │ │ ├── box_2shot_tvmonitor_train.txt │ │ │ ├── box_3shot_aeroplane_train.txt │ │ │ ├── box_3shot_bicycle_train.txt │ │ │ ├── box_3shot_bird_train.txt │ │ │ ├── box_3shot_boat_train.txt │ │ │ ├── box_3shot_bottle_train.txt │ │ │ ├── box_3shot_bus_train.txt │ │ │ ├── box_3shot_car_train.txt │ │ │ ├── box_3shot_cat_train.txt │ │ │ ├── box_3shot_chair_train.txt │ │ │ ├── box_3shot_cow_train.txt │ │ │ ├── box_3shot_diningtable_train.txt │ │ │ ├── box_3shot_dog_train.txt │ │ │ ├── box_3shot_horse_train.txt │ │ │ ├── box_3shot_motorbike_train.txt │ │ │ ├── box_3shot_person_train.txt │ │ │ ├── box_3shot_pottedplant_train.txt │ │ │ ├── box_3shot_sheep_train.txt │ │ │ ├── box_3shot_sofa_train.txt │ │ │ ├── box_3shot_train_train.txt │ │ │ ├── box_3shot_tvmonitor_train.txt │ │ │ ├── box_5shot_aeroplane_train.txt │ │ │ ├── box_5shot_bicycle_train.txt │ │ │ ├── box_5shot_bird_train.txt │ │ │ ├── box_5shot_boat_train.txt │ │ │ ├── box_5shot_bottle_train.txt │ │ │ ├── box_5shot_bus_train.txt │ │ │ ├── box_5shot_car_train.txt │ │ │ ├── box_5shot_cat_train.txt │ │ │ ├── box_5shot_chair_train.txt │ │ │ ├── box_5shot_cow_train.txt │ │ │ ├── box_5shot_diningtable_train.txt │ │ │ ├── box_5shot_dog_train.txt │ │ │ ├── box_5shot_horse_train.txt │ │ │ ├── box_5shot_motorbike_train.txt │ │ │ ├── box_5shot_person_train.txt │ │ │ ├── box_5shot_pottedplant_train.txt │ │ │ ├── box_5shot_sheep_train.txt │ │ │ ├── box_5shot_sofa_train.txt │ │ │ ├── box_5shot_train_train.txt │ │ │ └── box_5shot_tvmonitor_train.txt │ ├── figures │ │ ├── contradiction.png │ │ ├── feature_disturbance.png │ │ └── framework.png │ └── output │ │ ├── aeroplane_pr.pkl │ │ ├── bicycle_pr.pkl │ │ ├── bird_pr.pkl │ │ ├── boat_pr.pkl │ │ ├── bottle_pr.pkl │ │ ├── bus_pr.pkl │ │ ├── car_pr.pkl │ │ ├── cat_pr.pkl │ │ ├── chair_pr.pkl │ │ ├── cow_pr.pkl │ │ ├── diningtable_pr.pkl │ │ ├── dog_pr.pkl │ │ ├── horse_pr.pkl │ │ ├── motorbike_pr.pkl │ │ ├── person_pr.pkl │ │ ├── pottedplant_pr.pkl │ │ ├── sheep_pr.pkl │ │ ├── sofa_pr.pkl │ │ ├── train_pr.pkl │ │ └── tvmonitor_pr.pkl ├── CS-F-LTR │ ├── ICDE21-wang.pdf │ ├── README.md │ ├── main.py │ ├── scripts │ │ └── run.sh │ ├── server.py │ └── src │ │ ├── build_common.py │ │ ├── build_dics.py │ │ ├── build_sh_fed.py │ │ ├── build_sketch.py │ │ ├── build_sketch_heapq.py │ │ ├── builder.py │ │ ├── c_semi.py │ │ ├── cal_idf.py │ │ ├── convert.py │ │ ├── countminsketch.py │ │ ├── data_preprocess.py │ │ ├── decision_tree.py │ │ ├── decision_tree_semi.py │ │ ├── dictionary.py │ │ ├── draw.py │ │ ├── eval_acc.py │ │ ├── feature_generator.py │ │ ├── federation.py │ │ ├── g_semi.py │ │ ├── gc_semi.py │ │ ├── gcl_semi.py │ │ ├── global_variables.py │ │ ├── linear_classifier.py │ │ ├── linear_regression.py │ │ ├── mapper.py │ │ ├── nserver.py │ │ ├── semi.py │ │ ├── sh_server.py │ │ ├── sketch_heap.py │ │ ├── stats.py │ │ ├── transfer.py │ │ └── utils.py ├── CSD │ ├── README.md │ ├── csd_train.py │ ├── eval.py │ ├── export.py │ ├── images │ │ ├── debug.log │ │ ├── model.png │ │ ├── psnr-speed.png │ │ ├── psnr-tradeoff.png │ │ ├── table.png │ │ ├── tradeoff.png │ │ └── visual.png │ ├── src │ │ ├── args.py │ │ ├── common.py │ │ ├── config.py │ │ ├── contras_loss.py │ │ ├── data │ │ │ ├── common.py │ │ │ ├── div2k.py │ │ │ └── srdata.py │ │ ├── edsr_model.py │ │ ├── edsr_slim.py │ │ ├── metric.py │ │ ├── metrics.py │ │ ├── rcan_model.py │ │ └── vgg_model.py │ ├── train.py │ └── utils │ │ ├── __init__.py │ │ └── var_init.py ├── Cybertron │ ├── README.en.md │ ├── README.md │ ├── cybertron │ │ ├── __init__.py │ │ ├── activation.py │ │ ├── aggregator.py │ │ ├── base.py │ │ ├── block.py │ │ ├── cutoff.py │ │ ├── cybertron.py │ │ ├── dataset.py │ │ ├── decoder.py │ │ ├── filter.py │ │ ├── interaction.py │ │ ├── model.py │ │ ├── rbf.py │ │ ├── readout.py │ │ └── train.py │ ├── examples │ │ ├── dataset_ethanol_normed_trainset_1024.npz │ │ ├── dataset_ethanol_normed_validset_128.npz │ │ ├── dataset_ethanol_origin_testset_1024.npz │ │ ├── dataset_qm9_normed_testset_1024.npz │ │ ├── dataset_qm9_normed_trainset_1024.npz │ │ ├── dataset_qm9_normed_validset_128.npz │ │ ├── dataset_qm9_origin_testset_1024.npz │ │ ├── dataset_qm9_origin_trainset_1024.npz │ │ ├── dataset_qm9_origin_validset_128.npz │ │ ├── tutorial_00.py │ │ ├── tutorial_01.py │ │ ├── tutorial_02.py │ │ ├── tutorial_03.py │ │ ├── tutorial_04.py │ │ ├── tutorial_05.py │ │ ├── tutorial_06.py │ │ ├── tutorial_07.py │ │ ├── tutorial_08.py │ │ └── tutorial_09.py │ └── sponge │ │ ├── __init__.py │ │ ├── colvar │ │ ├── __init__.py │ │ ├── colvar.py │ │ └── index.py │ │ ├── data │ │ ├── __init__.py │ │ └── hyperparam.py │ │ ├── function │ │ ├── __init__.py │ │ ├── functions.py │ │ ├── operations.py │ │ └── units.py │ │ ├── partition │ │ ├── __init__.py │ │ └── fullconnect.py │ │ └── potential │ │ ├── __init__.py │ │ └── potential.py ├── CycleCol │ ├── README.md │ ├── code-CC-CNN.ipynb │ ├── code_CC_CNN.txt │ ├── model │ │ └── coloring_2-8_775.ckpt │ └── test.txt ├── DiffNet++ │ ├── DataModule.py │ ├── Metrics.py │ ├── Parserconf.py │ ├── README.md │ ├── conf │ │ ├── flickr_Diffnetplus.ini │ │ └── yelp_Diffnetplus.ini │ ├── datahelper.py │ ├── dataset.py │ ├── eval.py │ └── train.py ├── EPRNet │ ├── README.md │ ├── __init__.py │ ├── build_seg_data.py │ ├── data │ │ ├── __init__.py │ │ ├── camvid.py │ │ ├── cityscapes.py │ │ ├── segbase.py │ │ └── transform.py │ ├── eval.py │ ├── models │ │ ├── __init__.py │ │ └── eprnet.py │ ├── nn │ │ ├── __init__.py │ │ └── loss.py │ ├── requirements.txt │ ├── tools │ │ ├── __init__.py │ │ ├── lr.py │ │ ├── path.py │ │ └── utils.py │ └── train.py ├── FCENet │ ├── dataset │ │ ├── Icdar15_Text.py │ │ ├── __init__.py │ │ ├── ctw1500 │ │ │ ├── Evaluation_Protocol │ │ │ │ ├── ctw1500_eval.py │ │ │ │ └── voc_eval_polygon.py │ │ │ ├── Evaluation_sort │ │ │ │ └── detections_text0.5.txt │ │ │ ├── Readme.md │ │ │ └── annots.pkl │ │ ├── ctw1500_text.py │ │ ├── data_util.py │ │ ├── dataload.py │ │ ├── icdar15 │ │ │ ├── Evaluation_Protocol │ │ │ │ ├── readme.txt │ │ │ │ ├── rrc_evaluation_funcs.py │ │ │ │ ├── rrc_evaluation_funcs.pyc │ │ │ │ └── script.py │ │ │ ├── eval.sh │ │ │ ├── gt.zip │ │ │ ├── submit.zip │ │ │ └── submit │ │ │ │ ├── res_img_1.txt │ │ │ │ ├── res_img_10.txt │ │ │ │ ├── res_img_100.txt │ │ │ │ ├── res_img_101.txt │ │ │ │ ├── res_img_102.txt │ │ │ │ ├── res_img_103.txt │ │ │ │ ├── res_img_104.txt │ │ │ │ ├── res_img_105.txt │ │ │ │ ├── res_img_106.txt │ │ │ │ ├── res_img_107.txt │ │ │ │ ├── res_img_108.txt │ │ │ │ ├── res_img_109.txt │ │ │ │ ├── res_img_11.txt │ │ │ │ ├── res_img_110.txt │ │ │ │ ├── res_img_111.txt │ │ │ │ ├── res_img_112.txt │ │ │ │ ├── res_img_113.txt │ │ │ │ ├── res_img_114.txt │ │ │ │ ├── res_img_115.txt │ │ │ │ ├── res_img_116.txt │ │ │ │ ├── res_img_117.txt │ │ │ │ ├── res_img_118.txt │ │ │ │ ├── res_img_119.txt │ │ │ │ ├── res_img_12.txt │ │ │ │ ├── res_img_120.txt │ │ │ │ ├── res_img_121.txt │ │ │ │ ├── res_img_122.txt │ │ │ │ ├── res_img_123.txt │ │ │ │ ├── res_img_124.txt │ │ │ │ ├── res_img_125.txt │ │ │ │ ├── res_img_126.txt │ │ │ │ ├── res_img_127.txt │ │ │ │ ├── res_img_128.txt │ │ │ │ ├── res_img_129.txt │ │ │ │ ├── res_img_13.txt │ │ │ │ ├── res_img_130.txt │ │ │ │ ├── res_img_131.txt │ │ │ │ ├── res_img_132.txt │ │ │ │ ├── res_img_133.txt │ │ │ │ ├── res_img_134.txt │ │ │ │ ├── res_img_135.txt │ │ │ │ ├── res_img_136.txt │ │ │ │ ├── res_img_137.txt │ │ │ │ ├── res_img_138.txt │ │ │ │ ├── res_img_139.txt │ │ │ │ ├── res_img_14.txt │ │ │ │ ├── res_img_140.txt │ │ │ │ ├── res_img_141.txt │ │ │ │ ├── res_img_142.txt │ │ │ │ ├── res_img_143.txt │ │ │ │ ├── res_img_144.txt │ │ │ │ ├── res_img_145.txt │ │ │ │ ├── res_img_146.txt │ │ │ │ ├── res_img_147.txt │ │ │ │ ├── res_img_148.txt │ │ │ │ ├── res_img_149.txt │ │ │ │ ├── res_img_15.txt │ │ │ │ ├── res_img_150.txt │ │ │ │ ├── res_img_151.txt │ │ │ │ ├── res_img_152.txt │ │ │ │ ├── res_img_153.txt │ │ │ │ ├── res_img_154.txt │ │ │ │ ├── res_img_155.txt │ │ │ │ ├── res_img_156.txt │ │ │ │ ├── res_img_157.txt │ │ │ │ ├── res_img_158.txt │ │ │ │ ├── res_img_159.txt │ │ │ │ ├── res_img_16.txt │ │ │ │ ├── res_img_160.txt │ │ │ │ ├── res_img_161.txt │ │ │ │ ├── res_img_162.txt │ │ │ │ ├── res_img_163.txt │ │ │ │ ├── res_img_164.txt │ │ │ │ ├── res_img_165.txt │ │ │ │ ├── res_img_166.txt │ │ │ │ ├── res_img_167.txt │ │ │ │ ├── res_img_168.txt │ │ │ │ ├── res_img_169.txt │ │ │ │ ├── res_img_17.txt │ │ │ │ ├── res_img_170.txt │ │ │ │ ├── res_img_171.txt │ │ │ │ ├── res_img_172.txt │ │ │ │ ├── res_img_173.txt │ │ │ │ ├── res_img_174.txt │ │ │ │ ├── res_img_175.txt │ │ │ │ ├── res_img_176.txt │ │ │ │ ├── res_img_177.txt │ │ │ │ ├── res_img_178.txt │ │ │ │ ├── res_img_179.txt │ │ │ │ ├── res_img_18.txt │ │ │ │ ├── res_img_180.txt │ │ │ │ ├── res_img_181.txt │ │ │ │ ├── res_img_182.txt │ │ │ │ ├── res_img_183.txt │ │ │ │ ├── res_img_184.txt │ │ │ │ ├── res_img_185.txt │ │ │ │ ├── res_img_186.txt │ │ │ │ ├── res_img_187.txt │ │ │ │ ├── res_img_188.txt │ │ │ │ ├── res_img_189.txt │ │ │ │ ├── res_img_19.txt │ │ │ │ ├── res_img_190.txt │ │ │ │ ├── res_img_191.txt │ │ │ │ ├── res_img_192.txt │ │ │ │ ├── res_img_193.txt │ │ │ │ ├── res_img_194.txt │ │ │ │ ├── res_img_195.txt │ │ │ │ ├── res_img_196.txt │ │ │ │ ├── res_img_197.txt │ │ │ │ ├── res_img_198.txt │ │ │ │ ├── res_img_199.txt │ │ │ │ ├── res_img_2.txt │ │ │ │ ├── res_img_20.txt │ │ │ │ ├── res_img_200.txt │ │ │ │ ├── res_img_201.txt │ │ │ │ ├── res_img_202.txt │ │ │ │ ├── res_img_203.txt │ │ │ │ ├── res_img_204.txt │ │ │ │ ├── res_img_205.txt │ │ │ │ ├── res_img_206.txt │ │ │ │ ├── res_img_207.txt │ │ │ │ ├── res_img_208.txt │ │ │ │ ├── res_img_209.txt │ │ │ │ ├── res_img_21.txt │ │ │ │ ├── res_img_210.txt │ │ │ │ ├── res_img_211.txt │ │ │ │ ├── res_img_212.txt │ │ │ │ ├── res_img_213.txt │ │ │ │ ├── res_img_214.txt │ │ │ │ ├── res_img_215.txt │ │ │ │ ├── res_img_216.txt │ │ │ │ ├── res_img_217.txt │ │ │ │ ├── res_img_218.txt │ │ │ │ ├── res_img_219.txt │ │ │ │ ├── res_img_22.txt │ │ │ │ ├── res_img_220.txt │ │ │ │ ├── res_img_221.txt │ │ │ │ ├── res_img_222.txt │ │ │ │ ├── res_img_223.txt │ │ │ │ ├── res_img_224.txt │ │ │ │ ├── res_img_225.txt │ │ │ │ ├── res_img_226.txt │ │ │ │ ├── res_img_227.txt │ │ │ │ ├── res_img_228.txt │ │ │ │ ├── res_img_229.txt │ │ │ │ ├── res_img_23.txt │ │ │ │ ├── res_img_230.txt │ │ │ │ ├── res_img_231.txt │ │ │ │ ├── res_img_232.txt │ │ │ │ ├── res_img_233.txt │ │ │ │ ├── res_img_234.txt │ │ │ │ ├── res_img_235.txt │ │ │ │ ├── res_img_236.txt │ │ │ │ ├── res_img_237.txt │ │ │ │ ├── res_img_238.txt │ │ │ │ ├── res_img_239.txt │ │ │ │ ├── res_img_24.txt │ │ │ │ ├── res_img_240.txt │ │ │ │ ├── res_img_241.txt │ │ │ │ ├── res_img_242.txt │ │ │ │ ├── res_img_243.txt │ │ │ │ ├── res_img_244.txt │ │ │ │ ├── res_img_245.txt │ │ │ │ ├── res_img_246.txt │ │ │ │ ├── res_img_247.txt │ │ │ │ ├── res_img_248.txt │ │ │ │ ├── res_img_249.txt │ │ │ │ ├── res_img_25.txt │ │ │ │ ├── res_img_250.txt │ │ │ │ ├── res_img_251.txt │ │ │ │ ├── res_img_252.txt │ │ │ │ ├── res_img_253.txt │ │ │ │ ├── res_img_254.txt │ │ │ │ ├── res_img_255.txt │ │ │ │ ├── res_img_256.txt │ │ │ │ ├── res_img_257.txt │ │ │ │ ├── res_img_258.txt │ │ │ │ ├── res_img_259.txt │ │ │ │ ├── res_img_26.txt │ │ │ │ ├── res_img_260.txt │ │ │ │ ├── res_img_261.txt │ │ │ │ ├── res_img_262.txt │ │ │ │ ├── res_img_263.txt │ │ │ │ ├── res_img_264.txt │ │ │ │ ├── res_img_265.txt │ │ │ │ ├── res_img_266.txt │ │ │ │ ├── res_img_267.txt │ │ │ │ ├── res_img_268.txt │ │ │ │ ├── res_img_269.txt │ │ │ │ ├── res_img_27.txt │ │ │ │ ├── res_img_270.txt │ │ │ │ ├── res_img_271.txt │ │ │ │ ├── res_img_272.txt │ │ │ │ ├── res_img_273.txt │ │ │ │ ├── res_img_274.txt │ │ │ │ ├── res_img_275.txt │ │ │ │ ├── res_img_276.txt │ │ │ │ ├── res_img_277.txt │ │ │ │ ├── res_img_278.txt │ │ │ │ ├── res_img_279.txt │ │ │ │ ├── res_img_28.txt │ │ │ │ ├── res_img_280.txt │ │ │ │ ├── res_img_281.txt │ │ │ │ ├── res_img_282.txt │ │ │ │ ├── res_img_283.txt │ │ │ │ ├── res_img_284.txt │ │ │ │ ├── res_img_285.txt │ │ │ │ ├── res_img_286.txt │ │ │ │ ├── res_img_287.txt │ │ │ │ ├── res_img_288.txt │ │ │ │ ├── res_img_289.txt │ │ │ │ ├── res_img_29.txt │ │ │ │ ├── res_img_290.txt │ │ │ │ ├── res_img_291.txt │ │ │ │ ├── res_img_292.txt │ │ │ │ ├── res_img_293.txt │ │ │ │ ├── res_img_294.txt │ │ │ │ ├── res_img_295.txt │ │ │ │ ├── res_img_296.txt │ │ │ │ ├── res_img_297.txt │ │ │ │ ├── res_img_298.txt │ │ │ │ ├── res_img_299.txt │ │ │ │ ├── res_img_3.txt │ │ │ │ ├── res_img_30.txt │ │ │ │ ├── res_img_300.txt │ │ │ │ ├── res_img_301.txt │ │ │ │ ├── res_img_302.txt │ │ │ │ ├── res_img_303.txt │ │ │ │ ├── res_img_304.txt │ │ │ │ ├── res_img_305.txt │ │ │ │ ├── res_img_306.txt │ │ │ │ ├── res_img_307.txt │ │ │ │ ├── res_img_308.txt │ │ │ │ ├── res_img_309.txt │ │ │ │ ├── res_img_31.txt │ │ │ │ ├── res_img_310.txt │ │ │ │ ├── res_img_311.txt │ │ │ │ ├── res_img_312.txt │ │ │ │ ├── res_img_313.txt │ │ │ │ ├── res_img_314.txt │ │ │ │ ├── res_img_315.txt │ │ │ │ ├── res_img_316.txt │ │ │ │ ├── res_img_317.txt │ │ │ │ ├── res_img_318.txt │ │ │ │ ├── res_img_319.txt │ │ │ │ ├── res_img_32.txt │ │ │ │ ├── res_img_320.txt │ │ │ │ ├── res_img_321.txt │ │ │ │ ├── res_img_322.txt │ │ │ │ ├── res_img_323.txt │ │ │ │ ├── res_img_324.txt │ │ │ │ ├── res_img_325.txt │ │ │ │ ├── res_img_326.txt │ │ │ │ ├── res_img_327.txt │ │ │ │ ├── res_img_328.txt │ │ │ │ ├── res_img_329.txt │ │ │ │ ├── res_img_33.txt │ │ │ │ ├── res_img_330.txt │ │ │ │ ├── res_img_331.txt │ │ │ │ ├── res_img_332.txt │ │ │ │ ├── res_img_333.txt │ │ │ │ ├── res_img_334.txt │ │ │ │ ├── res_img_335.txt │ │ │ │ ├── res_img_336.txt │ │ │ │ ├── res_img_337.txt │ │ │ │ ├── res_img_338.txt │ │ │ │ ├── res_img_339.txt │ │ │ │ ├── res_img_34.txt │ │ │ │ ├── res_img_340.txt │ │ │ │ ├── res_img_341.txt │ │ │ │ ├── res_img_342.txt │ │ │ │ ├── res_img_343.txt │ │ │ │ ├── res_img_344.txt │ │ │ │ ├── res_img_345.txt │ │ │ │ ├── res_img_346.txt │ │ │ │ ├── res_img_347.txt │ │ │ │ ├── res_img_348.txt │ │ │ │ ├── res_img_349.txt │ │ │ │ ├── res_img_35.txt │ │ │ │ ├── res_img_350.txt │ │ │ │ ├── res_img_351.txt │ │ │ │ ├── res_img_352.txt │ │ │ │ ├── res_img_353.txt │ │ │ │ ├── res_img_354.txt │ │ │ │ ├── res_img_355.txt │ │ │ │ ├── res_img_356.txt │ │ │ │ ├── res_img_357.txt │ │ │ │ ├── res_img_358.txt │ │ │ │ ├── res_img_359.txt │ │ │ │ ├── res_img_36.txt │ │ │ │ ├── res_img_360.txt │ │ │ │ ├── res_img_361.txt │ │ │ │ ├── res_img_362.txt │ │ │ │ ├── res_img_363.txt │ │ │ │ ├── res_img_364.txt │ │ │ │ ├── res_img_365.txt │ │ │ │ ├── res_img_366.txt │ │ │ │ ├── res_img_367.txt │ │ │ │ ├── res_img_368.txt │ │ │ │ ├── res_img_369.txt │ │ │ │ ├── res_img_37.txt │ │ │ │ ├── res_img_370.txt │ │ │ │ ├── res_img_371.txt │ │ │ │ ├── res_img_372.txt │ │ │ │ ├── res_img_373.txt │ │ │ │ ├── res_img_374.txt │ │ │ │ ├── res_img_375.txt │ │ │ │ ├── res_img_376.txt │ │ │ │ ├── res_img_377.txt │ │ │ │ ├── res_img_378.txt │ │ │ │ ├── res_img_379.txt │ │ │ │ ├── res_img_38.txt │ │ │ │ ├── res_img_380.txt │ │ │ │ ├── res_img_381.txt │ │ │ │ ├── res_img_382.txt │ │ │ │ ├── res_img_383.txt │ │ │ │ ├── res_img_384.txt │ │ │ │ ├── res_img_385.txt │ │ │ │ ├── res_img_386.txt │ │ │ │ ├── res_img_387.txt │ │ │ │ ├── res_img_388.txt │ │ │ │ ├── res_img_389.txt │ │ │ │ ├── res_img_39.txt │ │ │ │ ├── res_img_390.txt │ │ │ │ ├── res_img_391.txt │ │ │ │ ├── res_img_392.txt │ │ │ │ ├── res_img_393.txt │ │ │ │ ├── res_img_394.txt │ │ │ │ ├── res_img_395.txt │ │ │ │ ├── res_img_396.txt │ │ │ │ ├── res_img_397.txt │ │ │ │ ├── res_img_398.txt │ │ │ │ ├── res_img_399.txt │ │ │ │ ├── res_img_4.txt │ │ │ │ ├── res_img_40.txt │ │ │ │ ├── res_img_400.txt │ │ │ │ ├── res_img_401.txt │ │ │ │ ├── res_img_402.txt │ │ │ │ ├── res_img_403.txt │ │ │ │ ├── res_img_404.txt │ │ │ │ ├── res_img_405.txt │ │ │ │ ├── res_img_406.txt │ │ │ │ ├── res_img_407.txt │ │ │ │ ├── res_img_408.txt │ │ │ │ ├── res_img_409.txt │ │ │ │ ├── res_img_41.txt │ │ │ │ ├── res_img_410.txt │ │ │ │ ├── res_img_411.txt │ │ │ │ ├── res_img_412.txt │ │ │ │ ├── res_img_413.txt │ │ │ │ ├── res_img_414.txt │ │ │ │ ├── res_img_415.txt │ │ │ │ ├── res_img_416.txt │ │ │ │ ├── res_img_417.txt │ │ │ │ ├── res_img_418.txt │ │ │ │ ├── res_img_419.txt │ │ │ │ ├── res_img_42.txt │ │ │ │ ├── res_img_420.txt │ │ │ │ ├── res_img_421.txt │ │ │ │ ├── res_img_422.txt │ │ │ │ ├── res_img_423.txt │ │ │ │ ├── res_img_424.txt │ │ │ │ ├── res_img_425.txt │ │ │ │ ├── res_img_426.txt │ │ │ │ ├── res_img_427.txt │ │ │ │ ├── res_img_428.txt │ │ │ │ ├── res_img_429.txt │ │ │ │ ├── res_img_43.txt │ │ │ │ ├── res_img_430.txt │ │ │ │ ├── res_img_431.txt │ │ │ │ ├── res_img_432.txt │ │ │ │ ├── res_img_433.txt │ │ │ │ ├── res_img_434.txt │ │ │ │ ├── res_img_435.txt │ │ │ │ ├── res_img_436.txt │ │ │ │ ├── res_img_437.txt │ │ │ │ ├── res_img_438.txt │ │ │ │ ├── res_img_439.txt │ │ │ │ ├── res_img_44.txt │ │ │ │ ├── res_img_440.txt │ │ │ │ ├── res_img_441.txt │ │ │ │ ├── res_img_442.txt │ │ │ │ ├── res_img_443.txt │ │ │ │ ├── res_img_444.txt │ │ │ │ ├── res_img_445.txt │ │ │ │ ├── res_img_446.txt │ │ │ │ ├── res_img_447.txt │ │ │ │ ├── res_img_448.txt │ │ │ │ ├── res_img_449.txt │ │ │ │ ├── res_img_45.txt │ │ │ │ ├── res_img_450.txt │ │ │ │ ├── res_img_451.txt │ │ │ │ ├── res_img_452.txt │ │ │ │ ├── res_img_453.txt │ │ │ │ ├── res_img_454.txt │ │ │ │ ├── res_img_455.txt │ │ │ │ ├── res_img_456.txt │ │ │ │ ├── res_img_457.txt │ │ │ │ ├── res_img_458.txt │ │ │ │ ├── res_img_459.txt │ │ │ │ ├── res_img_46.txt │ │ │ │ ├── res_img_460.txt │ │ │ │ ├── res_img_461.txt │ │ │ │ ├── res_img_462.txt │ │ │ │ ├── res_img_463.txt │ │ │ │ ├── res_img_464.txt │ │ │ │ ├── res_img_465.txt │ │ │ │ ├── res_img_466.txt │ │ │ │ ├── res_img_467.txt │ │ │ │ ├── res_img_468.txt │ │ │ │ ├── res_img_469.txt │ │ │ │ ├── res_img_47.txt │ │ │ │ ├── res_img_470.txt │ │ │ │ ├── res_img_471.txt │ │ │ │ ├── res_img_472.txt │ │ │ │ ├── res_img_473.txt │ │ │ │ ├── res_img_474.txt │ │ │ │ ├── res_img_475.txt │ │ │ │ ├── res_img_476.txt │ │ │ │ ├── res_img_477.txt │ │ │ │ ├── res_img_478.txt │ │ │ │ ├── res_img_479.txt │ │ │ │ ├── res_img_48.txt │ │ │ │ ├── res_img_480.txt │ │ │ │ ├── res_img_481.txt │ │ │ │ ├── res_img_482.txt │ │ │ │ ├── res_img_483.txt │ │ │ │ ├── res_img_484.txt │ │ │ │ ├── res_img_485.txt │ │ │ │ ├── res_img_486.txt │ │ │ │ ├── res_img_487.txt │ │ │ │ ├── res_img_488.txt │ │ │ │ ├── res_img_489.txt │ │ │ │ ├── res_img_49.txt │ │ │ │ ├── res_img_490.txt │ │ │ │ ├── res_img_491.txt │ │ │ │ ├── res_img_492.txt │ │ │ │ ├── res_img_493.txt │ │ │ │ ├── res_img_494.txt │ │ │ │ ├── res_img_495.txt │ │ │ │ ├── res_img_496.txt │ │ │ │ ├── res_img_497.txt │ │ │ │ ├── res_img_498.txt │ │ │ │ ├── res_img_499.txt │ │ │ │ ├── res_img_5.txt │ │ │ │ ├── res_img_50.txt │ │ │ │ ├── res_img_500.txt │ │ │ │ ├── res_img_51.txt │ │ │ │ ├── res_img_52.txt │ │ │ │ ├── res_img_53.txt │ │ │ │ ├── res_img_54.txt │ │ │ │ ├── res_img_55.txt │ │ │ │ ├── res_img_56.txt │ │ │ │ ├── res_img_57.txt │ │ │ │ ├── res_img_58.txt │ │ │ │ ├── res_img_59.txt │ │ │ │ ├── res_img_6.txt │ │ │ │ ├── res_img_60.txt │ │ │ │ ├── res_img_61.txt │ │ │ │ ├── res_img_62.txt │ │ │ │ ├── res_img_63.txt │ │ │ │ ├── res_img_64.txt │ │ │ │ ├── res_img_65.txt │ │ │ │ ├── res_img_66.txt │ │ │ │ ├── res_img_67.txt │ │ │ │ ├── res_img_68.txt │ │ │ │ ├── res_img_69.txt │ │ │ │ ├── res_img_7.txt │ │ │ │ ├── res_img_70.txt │ │ │ │ ├── res_img_71.txt │ │ │ │ ├── res_img_72.txt │ │ │ │ ├── res_img_73.txt │ │ │ │ ├── res_img_74.txt │ │ │ │ ├── res_img_75.txt │ │ │ │ ├── res_img_76.txt │ │ │ │ ├── res_img_77.txt │ │ │ │ ├── res_img_78.txt │ │ │ │ ├── res_img_79.txt │ │ │ │ ├── res_img_8.txt │ │ │ │ ├── res_img_80.txt │ │ │ │ ├── res_img_81.txt │ │ │ │ ├── res_img_82.txt │ │ │ │ ├── res_img_83.txt │ │ │ │ ├── res_img_84.txt │ │ │ │ ├── res_img_85.txt │ │ │ │ ├── res_img_86.txt │ │ │ │ ├── res_img_87.txt │ │ │ │ ├── res_img_88.txt │ │ │ │ ├── res_img_89.txt │ │ │ │ ├── res_img_9.txt │ │ │ │ ├── res_img_90.txt │ │ │ │ ├── res_img_91.txt │ │ │ │ ├── res_img_92.txt │ │ │ │ ├── res_img_93.txt │ │ │ │ ├── res_img_94.txt │ │ │ │ ├── res_img_95.txt │ │ │ │ ├── res_img_96.txt │ │ │ │ ├── res_img_97.txt │ │ │ │ ├── res_img_98.txt │ │ │ │ └── res_img_99.txt │ │ ├── total_text.py │ │ └── total_text │ │ │ ├── Evaluation_Protocol │ │ │ ├── ComputePrecisionRecall.m │ │ │ ├── Eval.m │ │ │ ├── Examples │ │ │ │ ├── Groundtruth │ │ │ │ │ ├── poly_gt_img1.mat │ │ │ │ │ ├── poly_gt_img2.mat │ │ │ │ │ ├── poly_gt_img3.mat │ │ │ │ │ ├── poly_gt_img4.mat │ │ │ │ │ └── poly_gt_img5.mat │ │ │ │ ├── Prediction │ │ │ │ │ ├── img1.mat │ │ │ │ │ ├── img2.mat │ │ │ │ │ ├── img3.mat │ │ │ │ │ ├── img4.mat │ │ │ │ │ └── img5.mat │ │ │ │ └── Result.txt │ │ │ └── Python_scripts │ │ │ │ ├── Deteval.py │ │ │ │ ├── Pascal_VOC.py │ │ │ │ ├── polygon_fast.py │ │ │ │ ├── polygon_wrapper.py │ │ │ │ └── polygon_wrapper.pyc │ │ │ └── download.sh │ ├── eval_FourierText.py │ ├── network │ │ ├── __init__.py │ │ ├── resnet.py │ │ └── textnet.py │ ├── readme.md │ ├── requirements.txt │ ├── src │ │ └── table1.jpg │ └── util │ │ ├── __init__.py │ │ ├── augmentation.py │ │ ├── config.py │ │ ├── detector.py │ │ ├── eval.py │ │ ├── io.py │ │ ├── iterable.py │ │ ├── misc.py │ │ ├── option.py │ │ ├── strs.py │ │ └── visualize.py ├── FDDE │ └── train-fine │ │ ├── dataset.py │ │ ├── infer.py │ │ ├── main.py │ │ ├── net.py │ │ ├── resnet │ │ ├── config.py │ │ └── resnet50.py │ │ └── train.py ├── FSMAFL │ ├── README.md │ ├── Temp │ │ ├── priv_data_72.npy │ │ └── total_priv_data_72.pickle │ ├── collaborate_train.py │ ├── communication_gan.py │ ├── data_utils.py │ ├── images │ │ └── Framework.png │ ├── model_initialization.py │ ├── model_utils.py │ ├── models.py │ └── option.py ├── GAF │ └── gaf_sgdm_lenet.py ├── GraphPAS │ ├── README.md │ ├── examples │ │ └── node_classification_test.py │ ├── graphpas │ │ ├── __init__.py │ │ ├── build_gnn │ │ │ ├── __init__.py │ │ │ ├── gnn_manager.py │ │ │ ├── gnn_net.py │ │ │ └── message_passing_net.py │ │ ├── estimation.py │ │ ├── graphpas_search │ │ │ ├── __init__.py │ │ │ ├── search_algorithm.py │ │ │ ├── search_manager.py │ │ │ └── utils.py │ │ └── search_space.py │ └── parallel_config.py ├── IMM │ ├── README.md │ └── ukf.py ├── LECF │ ├── README.md │ ├── data │ │ └── video10 │ │ │ └── video10.npz │ ├── dataloader.py │ ├── evaluate.py │ ├── main.py │ └── model.py ├── LIE-IQA │ ├── README.md │ ├── checkpoint │ │ └── README.md │ ├── fig │ │ ├── LIE-IQA-Framework.png │ │ ├── optimization-framework.png │ │ ├── performance-IQA-Dataset.png │ │ └── performance-LIE-IQA-Dataset.png │ ├── images │ │ └── test_file.txt │ ├── networks │ │ ├── model.py │ │ ├── var_init.py │ │ └── vgg.py │ ├── predict.py │ ├── requirements.txt │ └── utils │ │ ├── datasets.py │ │ └── tools.py ├── MTLN │ ├── .keep │ ├── MTLN.py │ └── README.md ├── MVD │ ├── .idea │ │ ├── .gitignore │ │ ├── MVD.iml │ │ ├── inspectionProfiles │ │ │ ├── Project_Default.xml │ │ │ └── profiles_settings.xml │ │ ├── misc.xml │ │ └── modules.xml │ ├── README.md │ ├── data │ │ ├── data_loader.py │ │ ├── data_manager.py │ │ ├── demo_pre_process_sysu.py │ │ └── pre_process_sysu.py │ ├── model │ │ ├── eval.py │ │ ├── lr_generator.py │ │ ├── model_main.py │ │ ├── resnet.py │ │ ├── training_cell.py │ │ └── vib.py │ ├── train.py │ └── utils │ │ ├── loss.py │ │ └── utils.py ├── PACMOO │ ├── README.md │ ├── dataloader.py │ ├── main.py │ ├── metrics.py │ ├── min_norm_solvers_numpy.py │ └── utils.py ├── RHFL │ ├── Dataset │ │ ├── cifar.py │ │ ├── init_data.py │ │ └── utils.py │ ├── Network │ │ ├── Models_Def │ │ │ ├── efficientnet.py │ │ │ ├── resnet.py │ │ │ └── shufflenet.py │ │ └── pretrain.py │ ├── README.md │ ├── RHFL │ │ └── RHFL.py │ └── loss.py ├── SNUH │ ├── README.md │ ├── main.py │ ├── model │ │ ├── SNUH.py │ │ └── base_model.py │ ├── run.sh │ └── utils │ │ ├── data.py │ │ ├── evaluation.py │ │ ├── logger.py │ │ └── mindspore_helper.py ├── UColor │ ├── .gitignore │ ├── README.md │ ├── datasets.py │ ├── loss.py │ ├── mindspore1.3.yaml │ ├── model.py │ ├── train.py │ ├── utils.py │ └── vgg.py ├── UKF │ ├── README.md │ ├── images │ │ ├── temp1.png │ │ └── temp2.png │ └── ukf.py ├── Zero-DCE++ │ ├── .gitignore │ ├── README.md │ ├── data │ │ ├── test_data │ │ │ └── .keepme │ │ └── train_data │ │ │ └── .keepme │ ├── dataset.py │ ├── loss.py │ ├── lowlight_test.py │ ├── lowlight_train.py │ ├── mindspore1.3.yaml │ ├── models.py │ ├── pretrain_model │ │ └── zero_dcepp_epoch99.ckpt │ └── utils.py ├── Zero-DCE │ ├── .gitignore │ ├── README.md │ ├── data │ │ ├── test_data │ │ │ └── .keepme │ │ └── train_data │ │ │ └── .keepme │ ├── dataset.py │ ├── loss.py │ ├── lowlight_test.py │ ├── lowlight_train.py │ ├── mindspore1.3.yaml │ ├── models.py │ ├── pretrain_model │ │ └── zero_dce_epoch99.ckpt │ └── utils.py ├── __init__.py └── informer │ ├── README.md │ ├── img │ ├── informer.png │ └── probsparse_intro.png │ ├── model │ ├── attn.py │ ├── decoder.py │ ├── embed.py │ ├── encoder.py │ └── model.py │ └── utils │ ├── __init__.py │ ├── he_normal.py │ ├── masking.py │ └── trans_tools.py └── requirements.txt /NOTICE: -------------------------------------------------------------------------------- 1 | MindSpore 2 | Copyright 2019-2021 Huawei Technologies Co., Ltd 3 | -------------------------------------------------------------------------------- /OWNERS: -------------------------------------------------------------------------------- 1 | approvers: 2 | - baochong 3 | - kingxian 4 | - sanjaychan 5 | - changzherui 6 | - zhao_ting_v 7 | - c_34 8 | - kisnwang 9 | - qujianwei 10 | - Shawny233 11 | - anzhengqi 12 | - gengdongjie 13 | - lvyufenghw 14 | - wangcong666 15 | - geniuspatrick -------------------------------------------------------------------------------- /RELEASE.md: -------------------------------------------------------------------------------- 1 | # MindSpore Contribution 0.1 2 | 3 | ## MindSpore Contribution 0.1 Release Notes 4 | 5 | 6 | -------------------------------------------------------------------------------- /application/An-Exploration-of-Conditioning-Methods/layers/__init__.py: -------------------------------------------------------------------------------- 1 | from .conditional import ConditionalLinear 2 | 3 | __all__ = ['ConditionalLinear'] 4 | -------------------------------------------------------------------------------- /application/AoA/aoa_mindspore/__init__.py: -------------------------------------------------------------------------------- 1 | from aoa_mindspore.aoa import AttentionOnAttention 2 | AoA = AttentionOnAttention 3 | -------------------------------------------------------------------------------- /application/AoA/saoa.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mindspore-ai/contrib/c6dfba7f4320f116c4656e9fc341e2252b083bc3/application/AoA/saoa.png -------------------------------------------------------------------------------- /application/CLIP-It/assets/overview.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mindspore-ai/contrib/c6dfba7f4320f116c4656e9fc341e2252b083bc3/application/CLIP-It/assets/overview.png -------------------------------------------------------------------------------- /application/Castling-ViT/README.py: -------------------------------------------------------------------------------- 1 | This code is a mindspore implementation of Castling-ViT(Compressing Self-Attention via Switching Towards Linear-Angular Attention During Vision Transformer Inference) which is avaliable at https://github.com/GATECH-EIC/Castling-ViT. -------------------------------------------------------------------------------- /application/Conv2Former_Simple_Transformer/mindspore/__init__.py: -------------------------------------------------------------------------------- 1 | from .conv2former_mindspore import * 2 | -------------------------------------------------------------------------------- /application/HTM-mindspore/htm.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mindspore-ai/contrib/c6dfba7f4320f116c4656e9fc341e2252b083bc3/application/HTM-mindspore/htm.png -------------------------------------------------------------------------------- /application/HTM-mindspore/htm_mindspore/__init__.py: -------------------------------------------------------------------------------- 1 | from htm_mindspore.htm_mindspore import HTMAttention, HTMBlock 2 | -------------------------------------------------------------------------------- /application/Investigating/data/generated_showerthoughts_ChatGPT_df_1.pkl: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mindspore-ai/contrib/c6dfba7f4320f116c4656e9fc341e2252b083bc3/application/Investigating/data/generated_showerthoughts_ChatGPT_df_1.pkl -------------------------------------------------------------------------------- /application/Investigating/data/generated_showerthoughts_ChatGPT_df_2.pkl: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mindspore-ai/contrib/c6dfba7f4320f116c4656e9fc341e2252b083bc3/application/Investigating/data/generated_showerthoughts_ChatGPT_df_2.pkl -------------------------------------------------------------------------------- /application/Investigating/data/generated_showerthoughts_ChatGPT_df_3.pkl: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mindspore-ai/contrib/c6dfba7f4320f116c4656e9fc341e2252b083bc3/application/Investigating/data/generated_showerthoughts_ChatGPT_df_3.pkl -------------------------------------------------------------------------------- /application/Learning-to-Upsample/complexity.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mindspore-ai/contrib/c6dfba7f4320f116c4656e9fc341e2252b083bc3/application/Learning-to-Upsample/complexity.jpg -------------------------------------------------------------------------------- /application/MCA/README.md: -------------------------------------------------------------------------------- 1 | This code is a mindspore implementation of MCA which is available at https://github.com/csdllab/mca -------------------------------------------------------------------------------- /application/NBC-Softmax/README.md: -------------------------------------------------------------------------------- 1 | This code is a mindspore implementation of NBC-Softmax which is avaliable at https://github.com/gayanku/nbc-softmax. -------------------------------------------------------------------------------- /application/OTCE/README.md: -------------------------------------------------------------------------------- 1 | This code is a mindspore implementation of OTCE which is available at https://github.com/tanyang1231/OTCE_Transferability_CVPR21. The paperswidthcocde link is https://paperswithcode.com/paper/otce-a-transferability-metric-for-cross. 2 | -------------------------------------------------------------------------------- /application/Real-NVP/output_figures/Figure_1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mindspore-ai/contrib/c6dfba7f4320f116c4656e9fc341e2252b083bc3/application/Real-NVP/output_figures/Figure_1.png -------------------------------------------------------------------------------- /application/Real-NVP/output_figures/Figure_2.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mindspore-ai/contrib/c6dfba7f4320f116c4656e9fc341e2252b083bc3/application/Real-NVP/output_figures/Figure_2.png -------------------------------------------------------------------------------- /application/Real-NVP/output_figures/Figure_3.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mindspore-ai/contrib/c6dfba7f4320f116c4656e9fc341e2252b083bc3/application/Real-NVP/output_figures/Figure_3.png -------------------------------------------------------------------------------- /application/Real-NVP/output_figures/Figure_4.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mindspore-ai/contrib/c6dfba7f4320f116c4656e9fc341e2252b083bc3/application/Real-NVP/output_figures/Figure_4.png -------------------------------------------------------------------------------- /application/Real-NVP/output_figures/Figure_5.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mindspore-ai/contrib/c6dfba7f4320f116c4656e9fc341e2252b083bc3/application/Real-NVP/output_figures/Figure_5.png -------------------------------------------------------------------------------- /application/Real-NVP/output_figures/Figure_6.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mindspore-ai/contrib/c6dfba7f4320f116c4656e9fc341e2252b083bc3/application/Real-NVP/output_figures/Figure_6.png -------------------------------------------------------------------------------- /application/Real-NVP/output_figures/Figure_7.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mindspore-ai/contrib/c6dfba7f4320f116c4656e9fc341e2252b083bc3/application/Real-NVP/output_figures/Figure_7.png -------------------------------------------------------------------------------- /application/Real-NVP/output_figures/Figure_8.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mindspore-ai/contrib/c6dfba7f4320f116c4656e9fc341e2252b083bc3/application/Real-NVP/output_figures/Figure_8.png -------------------------------------------------------------------------------- /application/Real-NVP/output_figures/Figure_9.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mindspore-ai/contrib/c6dfba7f4320f116c4656e9fc341e2252b083bc3/application/Real-NVP/output_figures/Figure_9.png -------------------------------------------------------------------------------- /application/S2DNet-Minimal/README.md: -------------------------------------------------------------------------------- 1 | ## 运行 2 | 1. 在本仓库中放入一张test_image.jpg图片，用于测试。 3 | 2. 运行`python main.py` 4 | -------------------------------------------------------------------------------- /application/Sub2Full-OCT-Denoising/output_ms.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mindspore-ai/contrib/c6dfba7f4320f116c4656e9fc341e2252b083bc3/application/Sub2Full-OCT-Denoising/output_ms.png -------------------------------------------------------------------------------- /application/Sub2Full-OCT-Denoising/output_torch.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mindspore-ai/contrib/c6dfba7f4320f116c4656e9fc341e2252b083bc3/application/Sub2Full-OCT-Denoising/output_torch.png -------------------------------------------------------------------------------- /application/Sub2Full-OCT-Denoising/target.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mindspore-ai/contrib/c6dfba7f4320f116c4656e9fc341e2252b083bc3/application/Sub2Full-OCT-Denoising/target.png -------------------------------------------------------------------------------- /application/Sub2Full-OCT-Denoising/test.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mindspore-ai/contrib/c6dfba7f4320f116c4656e9fc341e2252b083bc3/application/Sub2Full-OCT-Denoising/test.png -------------------------------------------------------------------------------- /application/TRSTMI/requirements.txt: -------------------------------------------------------------------------------- 1 | mindspore 2 | sys 3 | os 4 | numpy 5 | timeit 6 | -------------------------------------------------------------------------------- /application/TSGD/tsgd/__init__.py: -------------------------------------------------------------------------------- 1 | from .tsgd import * -------------------------------------------------------------------------------- /application/The_RefinedWeb_Dataset_for_Falcon/requirements.txt: -------------------------------------------------------------------------------- 1 | mindspore==2.5 2 | numpy 3 | mindnlp==0.4.1 4 | -------------------------------------------------------------------------------- /application/adam-aio/README.md: -------------------------------------------------------------------------------- 1 | This code is a mindspore implementation of All-In-One Adam Optimizer which is avaliable at https://github.com/kayuksel/pytorch-adamaio. 2 | -------------------------------------------------------------------------------- /application/alibi/alibi/__init__.py: -------------------------------------------------------------------------------- 1 | from alibi.config import ALiBiConfig 2 | from alibi.model import ALiBiTransformer 3 | -------------------------------------------------------------------------------- /application/aves/README.md: -------------------------------------------------------------------------------- 1 | This code is a mindspore implementation of AVES: Animal Vocalization Encoder based on Self-Supervision which is avaliable at https://github.com/earthspecies/aves. 2 | -------------------------------------------------------------------------------- /application/circle-loss/README.md: -------------------------------------------------------------------------------- 1 | This code is a mindspore implementation of circle-loss which is available at https://github.com/qianjinhao/circle-loss. -------------------------------------------------------------------------------- /application/comparing-the-efficacy/README.md: -------------------------------------------------------------------------------- 1 | This code is a mindspore implementation of imujoco which is available at https://github.com/mpatacchiola/imujoco.
2 | paperswidthcode link is https://paperswithcode.com/paper/comparing-the-efficacy-of-fine-tuning-and 3 | -------------------------------------------------------------------------------- /application/contextual-learning/README.md: -------------------------------------------------------------------------------- 1 | This code is a mindspore implementation of FCT which is available at https://github.com/gao-xiyuan/fct -------------------------------------------------------------------------------- /application/corss-dataset-training/readme.md: -------------------------------------------------------------------------------- 1 | This is a mindspore implementation of the paper Cross-dataset Training for Class Increasing Object Detection -------------------------------------------------------------------------------- /application/cross-transformers-spatially-aware-few-shot-transfer/README.md: -------------------------------------------------------------------------------- 1 | This code is a mindspore implementation of CrossTransformer which is available at https://github.com/lucidrains/cross-transformers-pytorch. -------------------------------------------------------------------------------- /application/delving-deeper-into-convolutional/README.md: -------------------------------------------------------------------------------- 1 | This code is a mindspore implementation of ConvGRUCell-pytorch which is available at https://github.com/bionick87/ConvGRUCell-pytorch. -------------------------------------------------------------------------------- /application/drop-an-octave-reducing-spatial/readme.md: -------------------------------------------------------------------------------- 1 | This is a mindspore implementation of the paper Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution. 2 | -------------------------------------------------------------------------------- /application/dropblock-a-regularization-method/README.md: -------------------------------------------------------------------------------- 1 | This code is a mindspore implementation of dropblock which is available at https://github.com/gan3sh500/dropblock.
2 | paperswidthcode link is https://paperswithcode.com/paper/dropblock-a-regularization-method-for 3 | -------------------------------------------------------------------------------- /application/dynamic-relu/README.md: -------------------------------------------------------------------------------- 1 | This code is a mindspore implementation of Dynamic ReLU which is avaliable at https://github.com/Islanna/DynamicReLU. 2 | -------------------------------------------------------------------------------- /application/effnet-an-efficient-structure/README.md: -------------------------------------------------------------------------------- 1 | This code is a mindspore implementation of EffNet which is available at https://github.com/andrijdavid/EffNet. 2 | paperswidthcocde link is https://paperswithcode.com/paper/effnet-an-efficient-structure-for. -------------------------------------------------------------------------------- /application/entropy-law/motivation.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mindspore-ai/contrib/c6dfba7f4320f116c4656e9fc341e2252b083bc3/application/entropy-law/motivation.png -------------------------------------------------------------------------------- /application/falformer/README.md: -------------------------------------------------------------------------------- 1 | This code is a mindspore implementation of FALFormer which is avaliable at https://github.com/caodoanh2001/FALFormer. 2 | -------------------------------------------------------------------------------- /application/fisheradversarial-mindspore/setup.py: -------------------------------------------------------------------------------- 1 | from distutils.core import setup 2 | 3 | 4 | setup(name='Fisher_Adversarial', 5 | version='0.0', 6 | author='Joerg Martin', 7 | py_modules = ['fisherform', 'fishertrace']) 8 | -------------------------------------------------------------------------------- /application/gaussian_adaptive_attention/gaussian_adaptive_attention/__init__.py: -------------------------------------------------------------------------------- 1 | from .GaussianBlock import GaussianBlock 2 | from .GaussianBlock import MultiHeadGaussianAdaptiveAttention 3 | from .GaussianBlock import GaussianAdaptiveAttention 4 | 5 | -------------------------------------------------------------------------------- /application/group-normalization/README.md: -------------------------------------------------------------------------------- 1 | This code is a mindspore implementation of group-normalization which is available at https://github.com/taokong/group_normalization. -------------------------------------------------------------------------------- /application/hyperspherical-consistency-regularization/README.md: -------------------------------------------------------------------------------- 1 | This code is a mindspore implementation of hyperspherical-consistency-regularization which is available at https://github.com/chengtan9907/Hyperspherical-Consistency-Regularization. -------------------------------------------------------------------------------- /application/isr/README.md: -------------------------------------------------------------------------------- 1 | This code is a mindspore implementation of ISR which is avaliable at https://github.com/dcp15/ISR_ICCV2023_Oral. 2 | -------------------------------------------------------------------------------- /application/jko_wass_grad/pth2pkl.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import pickle 3 | 4 | # 加载 .pth 文件 5 | data = torch.load("pushed_data.pth", map_location="cpu") 6 | 7 | # 保存为 pickle 文件 8 | with open("pushed_data.pkl", "wb") as f: 9 | pickle.dump(data, f) 10 | -------------------------------------------------------------------------------- /application/layer-normalization/README.md: -------------------------------------------------------------------------------- 1 | This code is a mindspore implementation of LayerNorm_GRU which is available at https://github.com/ElektrischesSchaf/LayerNorm_GRU -------------------------------------------------------------------------------- /application/light-head/README.md: -------------------------------------------------------------------------------- 1 | This code is a mindspore implementation of Light Head module in Light-Head R-CNN which is avaliable at https://github.com/princefr/Light-Head.pytorch. 2 | -------------------------------------------------------------------------------- /application/localquantumannealing/localquantumannealing/__init__.py: -------------------------------------------------------------------------------- 1 | from __future__ import print_function, division 2 | 3 | from .lqa import * 4 | from .lqa_basic import * 5 | 6 | 7 | 8 | 9 | 10 | -------------------------------------------------------------------------------- /application/lru/README.md: -------------------------------------------------------------------------------- 1 | This code is a mindspore implementation of LRU which is avaliable at https://github.com/Gothos/LRU-pytorch. 2 | -------------------------------------------------------------------------------- /application/lsuv-init/README.md: -------------------------------------------------------------------------------- 1 | This code is a mindspore implementation of lsuv-init which is avaliable at https://github.com/ducha-aiki/LSUV-keras. 2 | -------------------------------------------------------------------------------- /application/matthews-correlation/README.md: -------------------------------------------------------------------------------- 1 | This code is a mindspore implementation of MCC-Loss which is available at https://github.com/kakumarabhishek/MCC-Loss. 2 | paperswidthcode link is https://paperswithcode.com/paper/matthews-correlation-coefficient-loss-for. -------------------------------------------------------------------------------- /application/mgan-mindspore/images/faces-sample.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mindspore-ai/contrib/c6dfba7f4320f116c4656e9fc341e2252b083bc3/application/mgan-mindspore/images/faces-sample.png -------------------------------------------------------------------------------- /application/mgan-mindspore/images/mgan.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mindspore-ai/contrib/c6dfba7f4320f116c4656e9fc341e2252b083bc3/application/mgan-mindspore/images/mgan.png -------------------------------------------------------------------------------- /application/model-conversion-via/README.md: -------------------------------------------------------------------------------- 1 | This code is a mindspore implementation of DPDFD which is available at https://github.com/llbbcc/DPDFD. 2 | paperswidthcode link is https://paperswithcode.com/paper/model-conversion-via-differentially-private. -------------------------------------------------------------------------------- /application/mvn2vec/README.md: -------------------------------------------------------------------------------- 1 | This code is a mindspore implementation of group-normalization which is available at https://github.com/sezinata/mvn2vec-code. -------------------------------------------------------------------------------- /application/nystrom-attention/nystrom_attention/_init_.py: -------------------------------------------------------------------------------- 1 | from nystrom_attention.nystrom_attention import NystromAttention, Nystromformer 2 | Nystromer = Nystromformer 3 | 4 | -------------------------------------------------------------------------------- /application/openfashionclip-vision-and-language/maxi_dress.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mindspore-ai/contrib/c6dfba7f4320f116c4656e9fc341e2252b083bc3/application/openfashionclip-vision-and-language/maxi_dress.jpg -------------------------------------------------------------------------------- /application/polyloss/README.md: -------------------------------------------------------------------------------- 1 | This code is a mindspore implementation of lsuv-init which is avaliable at https://github.com/lumia-group/apl. 2 | -------------------------------------------------------------------------------- /application/privacy-enhancement/README.md: -------------------------------------------------------------------------------- 1 | This code is a mindspore implementation of Few-Shot-Privacy which is available at https://github.com/ArchitParnami/Few-Shot-Privacy -------------------------------------------------------------------------------- /application/pro/README.md: -------------------------------------------------------------------------------- 1 | # Unofficial MindSpore implementation of "Deep Weakly-supervised Anomaly Detection" paper 2 | 3 | [![License](https://img.shields.io/badge/License-BSD%203--Clause-blue.svg)](https://opensource.org/licenses/BSD-3-Clause) 4 | -------------------------------------------------------------------------------- /application/recurrent-highway-network/README.md: -------------------------------------------------------------------------------- 1 | This code is a mindspore implementation of Recurrent-Highway-Network which is avaliable at https://github.com/vermaMachineLearning/Pytorch-JIT-Recurrent-Highway-Network. -------------------------------------------------------------------------------- /application/robustness_depth_lang/data/readme.txt: -------------------------------------------------------------------------------- 1 | 请下载https://github.com/xiandong20/robustness_depth_lang/tree/main/data中的内容并放置在此处 -------------------------------------------------------------------------------- /application/robustness_depth_lang/model/readme.txt: -------------------------------------------------------------------------------- 1 | 下载clip-vit-large-patch14模型文件，放在这里 -------------------------------------------------------------------------------- /application/root-mean-square-layer-normalization/README.md: -------------------------------------------------------------------------------- 1 | This code is a mindspore implementation of RMSNorm which is available at https://github.com/hazdzz/RMSNorm. -------------------------------------------------------------------------------- /application/sa-mlp-distilling-graph/README.md: -------------------------------------------------------------------------------- 1 | This code is a mindspore implementation of SA-MLP which is available at https://github.com/jc-202/sa-mlp. -------------------------------------------------------------------------------- /application/segmentation-dataset/README.md: -------------------------------------------------------------------------------- 1 | This code is a mindspore implementation of BCSD which is available at https://github.com/saifkhichi96/bcsd. -------------------------------------------------------------------------------- /application/shallow-rnns/model/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mindspore-ai/contrib/c6dfba7f4320f116c4656e9fc341e2252b083bc3/application/shallow-rnns/model/__init__.py -------------------------------------------------------------------------------- /application/similarity-of-neural-network/README.md: -------------------------------------------------------------------------------- 1 | This code is a mindspore implementation of group-normalization which is available at https://github.com/jayroxis/CKA-similarity. -------------------------------------------------------------------------------- /application/smu/README.md: -------------------------------------------------------------------------------- 1 | This code is a mindspore implementation of SMU: smooth activation function for deep networks using smoothing maximum technique which is avaliable at https://github.com/iFe1er/SMU_pytorch. 2 | -------------------------------------------------------------------------------- /application/ssFPN/README.md: -------------------------------------------------------------------------------- 1 | This code is a mindspore implementation of group-normalization which is available at https://github.com/BearCooike/ssFPN-pytorch. -------------------------------------------------------------------------------- /application/stable-sam/README.md: -------------------------------------------------------------------------------- 1 | This code is a mindspore implementation of Stable SAM which is avaliable at https://github.com/cltan023/stablesam2024. 2 | -------------------------------------------------------------------------------- /application/stochastic-attention-head-removal-a-simple/README.md: -------------------------------------------------------------------------------- 1 | This code is a mindspore implementation of attention_head_removal which is available at https://github.com/s1603602/attention_head_removal. 2 | -------------------------------------------------------------------------------- /application/swish-t/README.md: -------------------------------------------------------------------------------- 1 | This code is a mindspore implementation of Swish-T which is avaliable at https://github.com/ictseoyoungmin/Swish-T-pytorch. 2 | -------------------------------------------------------------------------------- /application/temporal-and-aspectual-entailment/Readme.md: -------------------------------------------------------------------------------- 1 | This code is a mindspore implementation of iwcs2019 which is available at https://github.com/tttthomasssss/iwcs2019. 2 | 3 | paperswidthcocde link is https://paperswithcode.com/paper/temporal-and-aspectual-entailment. -------------------------------------------------------------------------------- /application/vat/README.md: -------------------------------------------------------------------------------- 1 | This code is a mindspore implementation of VAT which is avaliable at https://github.com/JohnYKiyo/VAT. 2 | -------------------------------------------------------------------------------- /application/weakly-supervised/README.md: -------------------------------------------------------------------------------- 1 | This code is a mindspore implementation of group-normalization which is available at https://github.com/Tau-J/JointBoneLoss. 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/intern/Fourier-Features-Let-Networks-Learn-High-Frequency/night.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mindspore-ai/contrib/c6dfba7f4320f116c4656e9fc341e2252b083bc3/intern/Fourier-Features-Let-Networks-Learn-High-Frequency/night.jpg -------------------------------------------------------------------------------- /intern/HTM-mindspore/htm.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mindspore-ai/contrib/c6dfba7f4320f116c4656e9fc341e2252b083bc3/intern/HTM-mindspore/htm.png -------------------------------------------------------------------------------- /intern/HTM-mindspore/htm_mindspore/__init__.py: -------------------------------------------------------------------------------- 1 | from htm_mindspore.htm_mindspore import HTMAttention, HTMBlock 2 | -------------------------------------------------------------------------------- /intern/Integrated-Gradient/assets/heatmap.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mindspore-ai/contrib/c6dfba7f4320f116c4656e9fc341e2252b083bc3/intern/Integrated-Gradient/assets/heatmap.jpg -------------------------------------------------------------------------------- /intern/Integrated-Gradient/assets/n01669191_46.JPEG: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mindspore-ai/contrib/c6dfba7f4320f116c4656e9fc341e2252b083bc3/intern/Integrated-Gradient/assets/n01669191_46.JPEG -------------------------------------------------------------------------------- /intern/Integrated-Gradient/result.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mindspore-ai/contrib/c6dfba7f4320f116c4656e9fc341e2252b083bc3/intern/Integrated-Gradient/result.png -------------------------------------------------------------------------------- /intern/Least_Generative/README.md: -------------------------------------------------------------------------------- 1 | This is a code with paper in article Least Squares Generative Adversarial Networks,it uses mindspore to run.The paper is just with a link of https://gitee.com/link?target=https%3A%2F%2Fgithub.com%2FMakeDirtyCode%2FDCGAN-celebA-pytorch 2 | -------------------------------------------------------------------------------- /intern/Patient2Vec-A-Personalized-Interpretable/Patient2Vec.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mindspore-ai/contrib/c6dfba7f4320f116c4656e9fc341e2252b083bc3/intern/Patient2Vec-A-Personalized-Interpretable/Patient2Vec.png -------------------------------------------------------------------------------- /intern/Quiz_gen/README.md: -------------------------------------------------------------------------------- 1 | This is a code with paper Automating Turkish Educational Quiz Generation Using Large Language Models,the link of paper:https://gitee.com/link?target=https%3A%2F%2Fgithub.com%2FKamyarZeinalipour%2FTurkish_Quiz_Generator. 2 | -------------------------------------------------------------------------------- /intern/Sketch2art/example.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mindspore-ai/contrib/c6dfba7f4320f116c4656e9fc341e2252b083bc3/intern/Sketch2art/example.png -------------------------------------------------------------------------------- /intern/SmeLU-master/smelu/__init__.py: -------------------------------------------------------------------------------- 1 | from .smelu import SmeLU 2 | from .smelu_ms import SmeLU 3 | -------------------------------------------------------------------------------- /intern/fisheradversarial-mindspore/setup.py: -------------------------------------------------------------------------------- 1 | from distutils.core import setup 2 | 3 | 4 | setup(name='Fisher_Adversarial', 5 | version='0.0', 6 | author='Joerg Martin', 7 | py_modules = ['fisherform', 'fishertrace']) 8 | -------------------------------------------------------------------------------- /intern/hyperparameters-part1/sched.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mindspore-ai/contrib/c6dfba7f4320f116c4656e9fc341e2252b083bc3/intern/hyperparameters-part1/sched.png -------------------------------------------------------------------------------- /intern/med-flamingo/src/__init__.py: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /intern/mgan-mindspore/images/faces-sample.png: 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/intern/nystrom-attention/nystrom_attention/_init_.py: -------------------------------------------------------------------------------- 1 | from nystrom_attention.nystrom_attention import NystromAttention, Nystromformer 2 | Nystromer = Nystromformer 3 | 4 | -------------------------------------------------------------------------------- /intern/pytorch_convgru/requirements.txt: -------------------------------------------------------------------------------- 1 | mindspore==2.3.0 2 | numpy>=1.21.6 3 | -------------------------------------------------------------------------------- /intern/rand_conv-master/images/glasses.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mindspore-ai/contrib/c6dfba7f4320f116c4656e9fc341e2252b083bc3/intern/rand_conv-master/images/glasses.jpg -------------------------------------------------------------------------------- /intern/rand_conv-master/images/robot.jpg: 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-------------------------------------------------------------------------------- 1 | paper:Exploring the Efficacy of Pre-trained Checkpoints in Text-to-Music Generation Task implemented in minspore 2 | implemented in pytorch:https://github.com/sander-wood/text-to-music -------------------------------------------------------------------------------- /papers/AECRNet/images/model.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mindspore-ai/contrib/c6dfba7f4320f116c4656e9fc341e2252b083bc3/papers/AECRNet/images/model.png -------------------------------------------------------------------------------- /papers/AECRNet/images/results.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mindspore-ai/contrib/c6dfba7f4320f116c4656e9fc341e2252b083bc3/papers/AECRNet/images/results.png 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-------------------------------------------------------------------------------- /papers/AVA_cifar/src/RandAugment/__init__.py: -------------------------------------------------------------------------------- 1 | 2 | from src.RandAugment.augmentations import RandAugment 3 | -------------------------------------------------------------------------------- /papers/AVA_hpa/.idea/inspectionProfiles/profiles_settings.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 6 | -------------------------------------------------------------------------------- /papers/AVA_hpa/.idea/misc.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 6 | -------------------------------------------------------------------------------- /papers/AVA_hpa/.idea/vcs.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 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-------------------------------------------------------------------------------- 1 | aeroplane 2 | bicycle 3 | bird 4 | boat 5 | bottle 6 | bus 7 | car 8 | cat 9 | chair 10 | cow 11 | diningtable 12 | dog 13 | horse 14 | motorbike 15 | person 16 | pottedplant 17 | sheep 18 | sofa 19 | train 20 | tvmonitor 21 | -------------------------------------------------------------------------------- /papers/CME/data/voc_novels.txt: -------------------------------------------------------------------------------- 1 | bird,bus,cow,motorbike,sofa 2 | aeroplane,bottle,cow,horse,sofa 3 | boat,cat,motorbike,sheep,sofa 4 | bicycle,bird,motorbike,train,tvmonitor 5 | aeroplane,bird,bus,cat,person 6 | car,diningtable,motorbike,pottedplant,tvmonitor -------------------------------------------------------------------------------- /papers/CME/data/voc_novels_split1.txt: -------------------------------------------------------------------------------- 1 | bird,bus,cow,motorbike,sofa 2 | aeroplane,bottle,cow,horse,sofa 3 | boat,cat,motorbike,sheep,sofa 4 | bicycle,bird,motorbike,train,tvmonitor 5 | aeroplane,bird,bus,cat,person 6 | car,diningtable,motorbike,pottedplant,tvmonitor -------------------------------------------------------------------------------- /papers/CME/data/voc_novels_split2.txt: -------------------------------------------------------------------------------- 1 | aeroplane,bottle,cow,horse,sofa 2 | bird,bus,cow,motorbike,sofa 3 | boat,cat,motorbike,sheep,sofa 4 | bicycle,bird,motorbike,train,tvmonitor 5 | aeroplane,bird,bus,cat,person 6 | car,diningtable,motorbike,pottedplant,tvmonitor -------------------------------------------------------------------------------- /papers/CME/data/voc_novels_split3.txt: -------------------------------------------------------------------------------- 1 | boat,cat,motorbike,sheep,sofa 2 | bird,bus,cow,motorbike,sofa 3 | aeroplane,bottle,cow,horse,sofa 4 | bicycle,bird,motorbike,train,tvmonitor 5 | aeroplane,bird,bus,cat,person 6 | car,diningtable,motorbike,pottedplant,tvmonitor -------------------------------------------------------------------------------- /papers/CME/data/vocsplit/box_1shot_aeroplane_train.txt: -------------------------------------------------------------------------------- 1 | /scratch/bykang/datasets/VOCdevkit/VOC2012/JPEGImages/2008_006761.jpg 2 | -------------------------------------------------------------------------------- /papers/CME/data/vocsplit/box_1shot_bicycle_train.txt: -------------------------------------------------------------------------------- 1 | /scratch/bykang/datasets/VOCdevkit/VOC2012/JPEGImages/2009_005064.jpg 2 | -------------------------------------------------------------------------------- /papers/CME/data/vocsplit/box_1shot_bird_train.txt: -------------------------------------------------------------------------------- 1 | /scratch/bykang/datasets/VOCdevkit/VOC2007/JPEGImages/003614.jpg 2 | 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-------------------------------------------------------------------------------- /papers/EPRNet/models/__init__.py: -------------------------------------------------------------------------------- 1 | # coding=utf-8 2 | 3 | from .eprnet import * 4 | 5 | _nets_map = { 6 | 'eprnet': EPRNet, 7 | } 8 | 9 | 10 | def get_model_by_name(name: str, **kwargs): 11 | return _nets_map[name.lower()](**kwargs) 12 | -------------------------------------------------------------------------------- /papers/EPRNet/nn/__init__.py: -------------------------------------------------------------------------------- 1 | # coding=utf-8 2 | 3 | from .loss import SoftmaxCrossEntropyLoss 4 | -------------------------------------------------------------------------------- /papers/EPRNet/requirements.txt: -------------------------------------------------------------------------------- 1 | mindspore-gpu 2 | mpmath==1.1.0 3 | numpy==1.17.5 4 | opencv-python==4.5.1.48 5 | packaging==20.9 6 | Pillow==8.1.0 7 | pycparser==2.20 8 | pyparsing==2.4.7 9 | scipy==1.3.3 10 | -------------------------------------------------------------------------------- /papers/EPRNet/tools/__init__.py: -------------------------------------------------------------------------------- 1 | # coding=utf-8 2 | 3 | from .path import * 4 | from .lr import * 5 | from .utils import * 6 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/__init__.py: -------------------------------------------------------------------------------- 1 | from .dataload import * 2 | from .total_text import TotalText 3 | from .ctw1500_text import Ctw1500Text 4 | from .Icdar15_Text import Icdar15Text 5 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/ctw1500/annots.pkl: -------------------------------------------------------------------------------- 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496,20,705,7,708,65,499,78 4 | 517,141,605,136,606,162,518,167 5 | 514,116,562,112,564,135,515,139 6 | 608,156,642,154,643,171,609,172 7 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_109.txt: -------------------------------------------------------------------------------- 1 | 447,268,455,209,620,229,612,288 2 | 336,357,415,354,416,380,337,383 3 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_11.txt: -------------------------------------------------------------------------------- 1 | 398,77,498,64,501,89,401,102 2 | 403,62,467,55,469,72,405,79 3 | 409,97,485,85,488,106,412,118 4 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_110.txt: -------------------------------------------------------------------------------- 1 | -5,482,74,455,89,499,9,526 2 | -5,432,92,403,106,449,7,478 3 | -4,385,67,369,77,413,5,429 4 | 72,457,168,426,181,465,84,496 5 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_111.txt: -------------------------------------------------------------------------------- 1 | 152,175,161,138,271,164,262,201 2 | 162,456,268,411,283,447,177,492 3 | 356,384,413,362,422,385,365,407 4 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_115.txt: -------------------------------------------------------------------------------- 1 | 184,341,240,329,244,344,187,357 2 | 100,360,182,342,186,360,104,379 3 | 106,385,153,371,157,386,110,400 4 | 104,338,147,329,150,345,108,354 5 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_116.txt: 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-------------------------------------------------------------------------------- 1 | 3,104,11,50,186,75,178,129 2 | 173,126,181,84,305,108,297,150 3 | 304,128,306,111,347,117,345,133 4 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_12.txt: -------------------------------------------------------------------------------- 1 | 63,180,141,175,143,211,65,217 2 | 75,244,169,238,171,271,77,277 3 | 96,221,129,218,131,238,97,241 4 | 116,426,160,426,160,442,116,442 5 | 88,428,124,428,124,444,88,444 6 | 327,146,403,140,404,156,328,162 7 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_120.txt: -------------------------------------------------------------------------------- 1 | 42,232,43,206,159,209,158,235 2 | 778,239,873,233,874,256,779,262 3 | 631,39,714,-11,732,18,649,69 4 | 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1020,471,1025,369,1250,379,1245,482 2 | 835,339,1025,328,1033,458,843,469 3 | 595,437,606,380,709,399,698,456 4 | 811,613,855,442,1177,525,1133,696 5 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_145.txt: -------------------------------------------------------------------------------- 1 | 718,201,891,76,941,145,768,270 2 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_146.txt: -------------------------------------------------------------------------------- 1 | 416,468,483,444,493,470,425,495 2 | 369,485,419,469,428,496,378,512 3 | 684,355,739,340,744,357,689,372 4 | 525,424,584,404,591,426,532,446 5 | 610,548,626,528,668,561,652,581 6 | 572,506,599,481,630,516,603,540 7 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_147.txt: -------------------------------------------------------------------------------- 1 | 844,134,850,94,944,110,938,150 2 | 780,130,787,88,850,99,843,141 3 | 945,164,948,135,1072,147,1069,176 4 | 955,142,958,110,1066,123,1063,155 5 | 1062,153,1065,123,1177,134,1174,164 6 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_148.txt: -------------------------------------------------------------------------------- 1 | 778,254,779,237,860,244,859,260 2 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_149.txt: -------------------------------------------------------------------------------- 1 | 820,365,986,362,987,390,821,393 2 | 752,440,754,424,795,431,792,447 3 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_15.txt: -------------------------------------------------------------------------------- 1 | 1155,162,1267,157,1268,187,1156,192 2 | 1149,131,1276,123,1278,153,1151,161 3 | 363,39,398,28,403,45,368,56 4 | 396,28,433,17,438,34,401,45 5 | 467,6,504,-2,509,16,472,25 6 | 435,16,469,8,474,26,439,35 7 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_150.txt: -------------------------------------------------------------------------------- 1 | 1061,159,1255,107,1272,171,1079,223 2 | 727,310,728,281,796,283,795,312 3 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_151.txt: -------------------------------------------------------------------------------- 1 | 792,311,837,311,837,349,792,349 2 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_153.txt: 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1199,249,1272,249,1272,272,1199,272 6 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_167.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mindspore-ai/contrib/c6dfba7f4320f116c4656e9fc341e2252b083bc3/papers/FCENet/dataset/icdar15/submit/res_img_167.txt -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_168.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mindspore-ai/contrib/c6dfba7f4320f116c4656e9fc341e2252b083bc3/papers/FCENet/dataset/icdar15/submit/res_img_168.txt -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_169.txt: -------------------------------------------------------------------------------- 1 | 287,451,288,422,406,427,405,457 2 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_17.txt: -------------------------------------------------------------------------------- 1 | 1070,117,1071,97,1131,99,1130,120 2 | 1053,134,1055,115,1088,119,1085,139 3 | 1093,136,1094,117,1158,121,1157,140 4 | 1136,118,1136,102,1157,102,1157,118 5 | 1164,123,1190,123,1190,140,1164,140 6 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_170.txt: -------------------------------------------------------------------------------- 1 | 40,562,352,473,373,546,60,635 2 | 135,627,244,582,258,615,149,660 3 | 183,669,236,644,246,667,193,691 4 | 238,595,304,564,315,587,249,619 5 | 137,686,190,665,198,685,146,706 6 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_171.txt: -------------------------------------------------------------------------------- 1 | 662,49,734,49,734,69,662,69 2 | 347,63,434,57,435,81,348,87 3 | 1217,73,1269,70,1270,83,1218,86 4 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_172.txt: -------------------------------------------------------------------------------- 1 | 9,30,10,3,173,12,172,39 2 | 1181,34,1185,10,1253,20,1249,44 3 | 564,346,640,335,646,381,570,392 4 | 1245,44,1247,21,1277,23,1275,46 5 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_173.txt: -------------------------------------------------------------------------------- 1 | 293,234,377,232,378,248,294,250 2 | 261,247,261,233,287,233,287,247 3 | 275,234,276,217,329,219,328,236 4 | 335,217,379,217,379,231,335,231 5 | 301,192,304,176,356,187,353,203 6 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_174.txt: -------------------------------------------------------------------------------- 1 | 974,85,1157,-5,1181,43,998,134 2 | 434,87,451,68,495,107,478,126 3 | 721,130,756,130,756,158,721,158 4 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_175.txt: -------------------------------------------------------------------------------- 1 | 262,220,325,214,328,242,264,248 2 | 626,88,706,85,707,114,627,118 3 | 264,254,311,247,315,277,269,284 4 | 302,256,331,251,334,274,306,278 5 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_176.txt: -------------------------------------------------------------------------------- 1 | 589,307,590,276,694,281,693,313 2 | 502,295,505,264,587,272,584,303 3 | 748,319,752,290,859,304,856,332 4 | 350,280,353,249,436,257,433,287 5 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_179.txt: -------------------------------------------------------------------------------- 1 | 1117,280,1254,280,1254,304,1117,304 2 | 1148,434,1149,413,1275,416,1274,437 3 | 913,639,915,206,1026,207,1024,640 4 | 1128,258,1244,258,1244,281,1128,281 5 | 1108,414,1165,414,1165,438,1108,438 6 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_18.txt: -------------------------------------------------------------------------------- 1 | 749,286,755,233,824,240,819,293 2 | 559,76,561,48,605,50,603,78 3 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_180.txt: -------------------------------------------------------------------------------- 1 | 704,152,793,140,798,174,709,186 2 | 26,211,170,190,173,215,29,235 3 | 859,109,932,107,944,510,870,512 4 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_181.txt: -------------------------------------------------------------------------------- 1 | 1055,484,1069,405,1196,427,1182,506 2 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_182.txt: -------------------------------------------------------------------------------- 1 | 229,268,407,260,409,306,231,313 2 | 11,284,225,275,227,326,13,335 3 | 938,282,1144,235,1153,275,947,322 4 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_183.txt: -------------------------------------------------------------------------------- 1 | 294,516,403,516,403,581,294,581 2 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_184.txt: -------------------------------------------------------------------------------- 1 | 819,124,954,108,957,139,822,155 2 | 906,152,1045,137,1048,164,909,178 3 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_185.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mindspore-ai/contrib/c6dfba7f4320f116c4656e9fc341e2252b083bc3/papers/FCENet/dataset/icdar15/submit/res_img_185.txt -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_186.txt: -------------------------------------------------------------------------------- 1 | 203,433,209,378,302,388,296,443 2 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_187.txt: -------------------------------------------------------------------------------- 1 | 726,428,734,390,817,407,809,445 2 | 687,419,695,386,747,398,739,431 3 | 589,298,616,292,628,346,601,351 4 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_188.txt: -------------------------------------------------------------------------------- 1 | 454,165,530,159,533,191,457,197 2 | 455,207,539,199,541,220,456,228 3 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_189.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mindspore-ai/contrib/c6dfba7f4320f116c4656e9fc341e2252b083bc3/papers/FCENet/dataset/icdar15/submit/res_img_189.txt -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_19.txt: -------------------------------------------------------------------------------- 1 | 414,254,481,241,486,269,419,281 2 | 449,223,491,214,495,238,454,246 3 | 357,260,413,251,418,281,362,290 4 | 486,240,525,237,528,264,488,267 5 | 352,236,443,215,450,244,358,265 6 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_190.txt: -------------------------------------------------------------------------------- 1 | 1015,335,1016,310,1113,313,1112,338 2 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_191.txt: -------------------------------------------------------------------------------- 1 | 1016,313,1259,255,1275,319,1031,378 2 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_192.txt: -------------------------------------------------------------------------------- 1 | 723,263,803,263,803,283,723,283 2 | 691,285,785,283,786,305,692,307 3 | 686,265,722,263,723,282,687,284 4 | 225,295,339,291,340,322,226,326 5 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_193.txt: -------------------------------------------------------------------------------- 1 | 448,202,524,202,524,268,448,268 2 | 1171,72,1235,53,1245,85,1180,104 3 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_194.txt: -------------------------------------------------------------------------------- 1 | 96,27,275,17,278,60,99,71 2 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_196.txt: -------------------------------------------------------------------------------- 1 | 952,442,955,425,996,431,994,449 2 | 921,379,983,370,986,388,923,397 3 | 917,362,977,355,979,375,920,382 4 | 901,438,904,420,960,428,957,446 5 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_197.txt: -------------------------------------------------------------------------------- 1 | 108,169,111,147,191,158,188,179 2 | 282,622,327,611,333,635,288,646 3 | 330,610,363,600,370,624,337,633 4 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_199.txt: -------------------------------------------------------------------------------- 1 | 1130,39,1206,-4,1224,25,1148,69 2 | 985,141,1130,41,1155,77,1010,177 3 | 947,186,999,152,1013,174,961,207 4 | 906,214,954,181,968,202,921,235 5 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_20.txt: -------------------------------------------------------------------------------- 1 | 512,263,535,262,538,338,515,339 2 | 483,232,510,228,524,331,497,335 3 | 464,218,484,215,499,334,479,337 4 | 506,190,529,189,532,281,509,282 5 | 677,260,677,213,690,213,690,260 6 | 1216,152,1261,144,1264,162,1220,170 7 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_200.txt: -------------------------------------------------------------------------------- 1 | 1034,236,1188,236,1188,310,1034,310 2 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_201.txt: -------------------------------------------------------------------------------- 1 | 965,420,1119,420,1119,472,965,472 2 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_205.txt: -------------------------------------------------------------------------------- 1 | 729,151,876,41,903,76,755,187 2 | 875,43,973,-16,996,20,899,81 3 | 807,446,890,438,895,485,812,493 4 | 47,31,55,10,133,41,125,62 5 | 1040,512,1041,490,1102,493,1101,514 6 | 1065,461,1119,461,1119,490,1065,490 7 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_206.txt: -------------------------------------------------------------------------------- 1 | 354,110,449,61,465,92,370,140 2 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_207.txt: -------------------------------------------------------------------------------- 1 | 214,180,228,154,296,191,281,217 2 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_208.txt: -------------------------------------------------------------------------------- 1 | 89,183,209,179,211,226,90,230 2 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_209.txt: -------------------------------------------------------------------------------- 1 | 1005,304,1146,277,1155,328,1015,355 2 | 548,228,551,208,608,217,605,238 3 | 483,220,486,200,547,209,544,229 4 | 230,192,233,169,284,176,281,199 5 | 604,235,607,217,673,226,670,244 6 | 179,187,183,162,231,169,227,194 7 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_210.txt: -------------------------------------------------------------------------------- 1 | 859,58,1166,-16,1182,46,874,121 2 | 731,106,821,77,836,123,746,152 3 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_211.txt: -------------------------------------------------------------------------------- 1 | 28,507,28,227,107,227,107,507 2 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_212.txt: -------------------------------------------------------------------------------- 1 | 500,295,547,290,549,313,503,317 2 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_213.txt: -------------------------------------------------------------------------------- 1 | 43,94,61,59,181,123,162,158 2 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_215.txt: -------------------------------------------------------------------------------- 1 | 180,69,287,52,296,104,189,121 2 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_216.txt: -------------------------------------------------------------------------------- 1 | 514,482,528,452,629,500,615,530 2 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_217.txt: -------------------------------------------------------------------------------- 1 | 1195,158,1273,145,1279,180,1201,194 2 | 1141,101,1171,95,1175,115,1145,121 3 | 89,412,186,399,189,425,92,439 4 | 82,233,123,215,132,235,91,253 5 | 45,251,92,234,98,251,51,268 6 | 941,285,1107,250,1117,299,951,334 7 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_219.txt: -------------------------------------------------------------------------------- 1 | 272,143,320,135,323,154,275,162 2 | 175,160,258,147,261,166,178,179 3 | 324,135,362,129,364,147,327,153 4 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_22.txt: -------------------------------------------------------------------------------- 1 | 407,327,523,308,528,344,412,363 2 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_220.txt: -------------------------------------------------------------------------------- 1 | 268,200,273,164,393,180,389,216 2 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_221.txt: -------------------------------------------------------------------------------- 1 | 16,324,61,296,79,323,33,352 2 | 92,385,144,343,163,366,111,408 3 | 151,348,175,331,185,344,161,361 4 | -11,538,98,508,114,569,4,599 5 | 34,356,142,265,183,315,75,405 6 | -------------------------------------------------------------------------------- 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15,161,30,95,373,170,358,237 2 | 502,186,741,173,743,209,504,222 3 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_226.txt: -------------------------------------------------------------------------------- 1 | 830,250,839,207,903,220,894,263 2 | 782,341,785,303,897,310,894,348 3 | 785,242,794,198,842,209,833,252 4 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_227.txt: -------------------------------------------------------------------------------- 1 | 461,165,467,116,605,135,599,184 2 | 79,262,150,259,151,282,80,285 3 | 320,253,396,239,401,268,325,283 4 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_228.txt: -------------------------------------------------------------------------------- 1 | 124,221,310,204,318,288,132,305 2 | 95,435,278,415,284,470,101,490 3 | -7,233,98,220,122,424,16,437 4 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_229.txt: -------------------------------------------------------------------------------- 1 | 1005,66,1065,52,1073,84,1013,98 2 | 689,157,815,124,823,154,697,187 3 | 827,117,957,85,964,114,834,145 4 | 962,87,1004,76,1010,99,968,110 5 | 1071,49,1142,39,1146,70,1076,80 6 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_23.txt: -------------------------------------------------------------------------------- 1 | 648,172,738,165,740,186,650,194 2 | 737,191,737,172,775,172,775,191 3 | 684,193,746,190,747,207,685,210 4 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_230.txt: -------------------------------------------------------------------------------- 1 | 444,426,445,374,683,377,682,429 2 | 1001,434,1003,391,1090,395,1088,438 3 | 703,424,706,381,917,397,914,440 4 | 1095,436,1099,395,1193,404,1189,445 5 | 920,434,921,400,994,402,993,436 6 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_231.txt: -------------------------------------------------------------------------------- 1 | 50,501,113,475,127,508,64,534 2 | 8,464,77,441,89,478,20,501 3 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_232.txt: -------------------------------------------------------------------------------- 1 | 167,354,179,268,407,300,395,387 2 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_233.txt: -------------------------------------------------------------------------------- 1 | 954,251,957,220,1049,228,1046,259 2 | 696,152,762,152,762,205,696,205 3 | 851,244,852,214,958,220,957,250 4 | 792,233,794,214,842,219,840,238 5 | 766,211,769,138,1034,148,1031,221 6 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_234.txt: -------------------------------------------------------------------------------- 1 | 850,88,969,76,972,107,853,119 2 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_235.txt: -------------------------------------------------------------------------------- 1 | 373,54,413,54,413,78,373,78 2 | 449,102,450,80,519,85,518,107 3 | 295,71,296,49,376,55,375,77 4 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_237.txt: -------------------------------------------------------------------------------- 1 | 853,58,957,43,962,72,857,87 2 | 960,37,1038,31,1041,66,963,73 3 | 900,88,998,76,1001,103,903,114 4 | 1077,322,1209,278,1267,454,1136,497 5 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_239.txt: -------------------------------------------------------------------------------- 1 | 570,159,571,144,610,146,609,161 2 | 608,148,635,146,636,161,609,163 3 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_24.txt: -------------------------------------------------------------------------------- 1 | 399,467,537,437,543,466,406,496 2 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_240.txt: -------------------------------------------------------------------------------- 1 | 627,219,700,217,701,238,628,240 2 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_241.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mindspore-ai/contrib/c6dfba7f4320f116c4656e9fc341e2252b083bc3/papers/FCENet/dataset/icdar15/submit/res_img_241.txt -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_242.txt: -------------------------------------------------------------------------------- 1 | 302,287,579,228,601,333,324,393 2 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_243.txt: -------------------------------------------------------------------------------- 1 | 1044,30,1139,26,1140,44,1045,48 2 | 1143,29,1227,24,1228,38,1144,43 3 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_244.txt: -------------------------------------------------------------------------------- 1 | 185,102,194,59,412,102,404,146 2 | 404,144,409,118,527,140,522,166 3 | 507,156,511,139,564,152,560,169 4 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_245.txt: -------------------------------------------------------------------------------- 1 | 668,125,853,117,856,178,671,186 2 | 858,114,1023,103,1027,168,862,179 3 | 1063,128,1216,125,1217,168,1064,171 4 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_246.txt: -------------------------------------------------------------------------------- 1 | 866,567,1031,524,1044,577,880,620 2 | 949,501,1025,479,1038,524,962,546 3 | 859,523,942,500,955,546,872,569 4 | 859,493,916,483,921,511,865,522 5 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_247.txt: -------------------------------------------------------------------------------- 1 | 352,161,398,148,403,164,356,177 2 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_249.txt: -------------------------------------------------------------------------------- 1 | 371,214,466,203,470,244,376,254 2 | 331,373,528,335,537,382,340,420 3 | 402,306,439,301,441,323,404,328 4 | 358,255,469,247,472,284,361,292 5 | 434,304,461,294,467,312,440,321 6 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_25.txt: -------------------------------------------------------------------------------- 1 | 523,63,669,58,670,93,524,98 2 | 668,59,783,46,787,79,672,93 3 | 1117,88,1167,88,1167,101,1117,101 4 | 954,148,994,143,995,155,955,160 5 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_250.txt: -------------------------------------------------------------------------------- 1 | 323,253,376,251,377,271,324,273 2 | 377,271,377,251,414,251,414,271 3 | 578,246,614,242,616,260,580,264 4 | 361,202,374,165,429,186,416,222 5 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_253.txt: -------------------------------------------------------------------------------- 1 | 650,63,749,12,781,76,683,126 2 | 949,116,991,104,999,133,958,145 3 | 2,110,4,39,52,40,50,111 4 | 721,37,801,0,828,58,748,95 5 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_257.txt: -------------------------------------------------------------------------------- 1 | 661,239,662,203,765,207,764,244 2 | 279,99,280,78,368,81,367,102 3 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_258.txt: -------------------------------------------------------------------------------- 1 | 549,5,615,3,616,36,550,38 2 | 492,174,545,161,554,200,500,212 3 | 446,181,500,173,506,211,452,219 4 | 404,193,450,185,456,220,410,228 5 | 371,201,413,195,418,229,376,235 6 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_260.txt: -------------------------------------------------------------------------------- 1 | 1148,196,1274,162,1289,217,1163,251 2 | 1203,251,1258,242,1262,265,1207,274 3 | 509,296,529,296,529,369,509,369 4 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_261.txt: -------------------------------------------------------------------------------- 1 | 279,350,345,335,348,351,282,366 2 | 117,417,120,122,203,123,200,418 3 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_262.txt: -------------------------------------------------------------------------------- 1 | 953,72,1058,21,1075,56,970,108 2 | -1,209,217,164,226,211,8,256 3 | 66,245,151,227,154,244,69,261 4 | 856,191,906,172,910,185,861,204 5 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_263.txt: -------------------------------------------------------------------------------- 1 | 1061,171,1064,123,1243,132,1240,180 2 | 999,169,999,137,1079,137,1079,169 3 | -------------------------------------------------------------------------------- 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1 | 167,387,219,382,220,397,168,402 2 | 160,364,207,356,212,383,165,392 3 | 204,364,230,360,232,379,206,382 4 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_268.txt: -------------------------------------------------------------------------------- 1 | 355,176,408,145,422,168,369,200 2 | 500,67,591,7,611,38,521,98 3 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_269.txt: -------------------------------------------------------------------------------- 1 | 245,161,253,135,328,158,320,184 2 | 134,125,143,99,258,135,249,162 3 | 53,98,65,67,144,97,132,128 4 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_27.txt: -------------------------------------------------------------------------------- 1 | 52,133,64,68,286,109,274,174 2 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_270.txt: -------------------------------------------------------------------------------- 1 | 1017,297,1018,279,1087,282,1086,300 2 | 762,35,837,4,857,53,782,84 3 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_272.txt: -------------------------------------------------------------------------------- 1 | 345,425,415,402,425,435,356,458 2 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_274.txt: -------------------------------------------------------------------------------- 1 | 24,170,36,89,226,117,214,198 2 | 840,164,887,157,890,173,842,181 3 | 314,292,344,236,370,249,340,306 4 | 421,283,455,276,460,299,426,306 5 | 597,168,599,152,627,155,625,171 6 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_275.txt: -------------------------------------------------------------------------------- 1 | 296,79,419,0,442,34,319,114 2 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_276.txt: -------------------------------------------------------------------------------- 1 | 0,489,134,477,136,502,2,514 2 | 42,87,47,70,128,95,123,112 3 | 40,120,44,109,69,117,66,128 4 | 69,83,73,70,130,86,126,100 5 | 218,115,224,98,275,116,269,133 6 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_277.txt: -------------------------------------------------------------------------------- 1 | 619,147,620,125,699,131,697,153 2 | 598,170,599,143,716,148,715,175 3 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_278.txt: -------------------------------------------------------------------------------- 1 | 696,2,814,-2,822,156,704,161 2 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_279.txt: -------------------------------------------------------------------------------- 1 | 638,253,740,226,749,258,647,286 2 | 739,210,1001,150,1012,198,750,258 3 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_281.txt: -------------------------------------------------------------------------------- 1 | 336,275,439,272,440,305,337,308 2 | 143,291,327,275,330,311,146,327 3 | 0,80,107,73,109,104,2,110 4 | 169,103,170,60,269,63,268,106 5 | 1007,259,1184,235,1194,305,1016,329 6 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_282.txt: -------------------------------------------------------------------------------- 1 | 1022,116,1211,105,1214,148,1025,159 2 | 423,172,644,159,647,207,426,220 3 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_283.txt: -------------------------------------------------------------------------------- 1 | 104,283,109,228,365,251,360,305 2 | 331,292,336,258,492,280,487,314 3 | 22,437,23,419,98,424,97,442 4 | 409,472,410,458,462,461,461,475 5 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_284.txt: -------------------------------------------------------------------------------- 1 | 594,450,700,444,701,471,596,477 2 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_285.txt: -------------------------------------------------------------------------------- 1 | 1014,290,1015,249,1217,258,1215,299 2 | 494,121,547,115,550,138,496,143 3 | 480,20,481,-4,540,-1,539,24 4 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_286.txt: -------------------------------------------------------------------------------- 1 | 896,58,1136,-7,1151,45,911,111 2 | 168,170,169,144,262,147,261,173 3 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_287.txt: -------------------------------------------------------------------------------- 1 | 215,82,227,31,369,66,357,117 2 | 523,162,527,145,567,153,564,170 3 | 561,170,564,153,599,158,597,175 4 | -------------------------------------------------------------------------------- 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-------------------------------------------------------------------------------- 1 | 609,293,655,288,657,311,611,317 2 | 655,286,701,279,705,305,658,312 3 | 164,180,188,123,332,184,308,241 4 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_293.txt: -------------------------------------------------------------------------------- 1 | 740,303,817,297,819,331,742,337 2 | 821,303,879,299,881,325,823,329 3 | 954,272,954,243,1078,243,1078,272 4 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_294.txt: -------------------------------------------------------------------------------- 1 | 673,209,739,207,740,233,674,235 2 | 158,185,319,170,322,200,161,215 3 | 69,263,70,237,137,239,136,265 4 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_295.txt: 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-------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_298.txt: -------------------------------------------------------------------------------- 1 | 321,28,335,-1,412,33,398,63 2 | 0,93,4,62,81,72,78,102 3 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_299.txt: -------------------------------------------------------------------------------- 1 | 337,132,342,108,403,121,398,145 2 | 394,144,399,120,500,142,495,166 3 | 492,162,495,140,566,153,562,174 4 | 288,123,294,99,343,110,338,134 5 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_3.txt: -------------------------------------------------------------------------------- 1 | 478,17,533,14,534,36,479,39 2 | 533,58,578,58,578,75,533,75 3 | 483,61,534,59,535,74,484,76 4 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_30.txt: -------------------------------------------------------------------------------- 1 | 829,289,830,254,1004,257,1003,292 2 | 899,324,935,324,935,337,899,337 3 | 520,137,521,118,574,122,573,141 4 | 832,332,833,320,864,323,863,334 5 | 867,333,868,322,895,324,894,335 6 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_300.txt: -------------------------------------------------------------------------------- 1 | 595,314,653,308,656,333,598,339 2 | 781,302,841,297,843,317,783,322 3 | 853,328,909,324,910,340,854,343 4 | 850,59,914,43,918,60,855,76 5 | 352,664,389,664,389,699,352,699 6 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_301.txt: -------------------------------------------------------------------------------- 1 | 692,149,759,135,763,155,696,169 2 | 6,639,127,592,144,636,24,684 3 | 255,345,296,345,296,362,255,362 4 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_302.txt: -------------------------------------------------------------------------------- 1 | 725,59,771,37,782,59,736,82 2 | 430,203,436,173,501,187,495,216 3 | 796,287,844,284,846,302,797,306 4 | 490,212,495,183,539,190,535,218 5 | 654,96,676,94,678,114,656,116 6 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_303.txt: -------------------------------------------------------------------------------- 1 | 543,144,549,113,626,130,619,161 2 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_304.txt: -------------------------------------------------------------------------------- 1 | 388,84,614,75,615,117,389,126 2 | 1005,155,1005,142,1036,142,1036,155 3 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_305.txt: -------------------------------------------------------------------------------- 1 | 486,251,705,238,708,275,488,289 2 | 616,174,616,152,670,152,670,174 3 | 673,175,673,152,789,152,789,175 4 | 522,172,523,151,614,154,613,175 5 | 818,274,819,244,955,247,954,277 6 | 739,254,804,252,805,272,740,274 7 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_306.txt: -------------------------------------------------------------------------------- 1 | 1150,133,1188,133,1188,154,1150,154 2 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_307.txt: -------------------------------------------------------------------------------- 1 | 1112,106,1112,48,1252,48,1252,106 2 | 774,170,832,141,849,176,792,205 3 | 595,277,595,259,624,259,624,277 4 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_308.txt: -------------------------------------------------------------------------------- 1 | 734,42,862,32,866,75,737,86 2 | 901,66,901,50,940,50,940,66 3 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_309.txt: -------------------------------------------------------------------------------- 1 | 704,480,740,447,832,547,797,580 2 | 773,559,820,511,981,668,934,716 3 | 689,434,762,297,1063,456,990,594 4 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_31.txt: -------------------------------------------------------------------------------- 1 | 560,113,653,94,660,127,567,146 2 | 301,176,363,163,370,193,308,206 3 | 519,126,563,117,569,147,525,156 4 | 274,187,305,181,309,206,278,211 5 | 133,221,174,212,180,235,138,244 6 | 942,56,987,47,992,71,947,81 7 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_311.txt: -------------------------------------------------------------------------------- 1 | 1128,39,1272,-6,1306,99,1162,146 2 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_314.txt: -------------------------------------------------------------------------------- 1 | 84,125,109,53,246,101,221,174 2 | -16,94,10,18,112,55,85,131 3 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_315.txt: -------------------------------------------------------------------------------- 1 | 672,33,805,33,805,65,672,65 2 | 670,6,800,6,800,38,670,38 3 | 1069,201,1187,178,1193,207,1074,231 4 | 779,163,815,146,823,163,786,179 5 | 808,145,874,108,884,126,818,163 6 | 680,99,681,82,774,84,773,101 7 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_316.txt: -------------------------------------------------------------------------------- 1 | 819,61,882,21,895,42,833,82 2 | 882,18,943,-8,953,14,892,41 3 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_317.txt: -------------------------------------------------------------------------------- 1 | 757,251,852,245,854,272,758,278 2 | 1039,229,1176,204,1193,298,1057,324 3 | 1103,351,1104,335,1140,337,1139,353 4 | -------------------------------------------------------------------------------- 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53,513,127,513,127,534,53,534 2 | 55,514,55,485,126,485,126,514 3 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_321.txt: -------------------------------------------------------------------------------- 1 | 1033,325,1038,301,1097,313,1092,337 2 | 1092,335,1099,305,1226,334,1219,365 3 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_322.txt: -------------------------------------------------------------------------------- 1 | 134,120,143,84,294,122,285,158 2 | 0,92,7,56,139,84,131,120 3 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_323.txt: -------------------------------------------------------------------------------- 1 | 109,334,109,285,255,285,255,334 2 | 958,343,958,183,1064,183,1064,343 3 | 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906,63,1129,15,1138,57,915,106 4 | 428,56,472,54,473,78,429,79 5 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_333.txt: -------------------------------------------------------------------------------- 1 | 614,156,727,152,728,189,615,192 2 | 1056,95,1223,60,1230,96,1063,130 3 | 132,174,134,142,188,145,186,177 4 | 61,167,65,134,133,144,129,176 5 | 335,135,362,135,362,149,335,149 6 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_334.txt: -------------------------------------------------------------------------------- 1 | 530,197,584,193,585,205,531,209 2 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_335.txt: -------------------------------------------------------------------------------- 1 | 139,225,140,193,280,200,279,232 2 | 436,335,439,304,484,307,481,339 3 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_336.txt: -------------------------------------------------------------------------------- 1 | 735,124,738,103,808,111,806,132 2 | 536,264,537,247,581,249,580,266 3 | 529,516,574,512,575,530,530,534 4 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_337.txt: -------------------------------------------------------------------------------- 1 | 514,262,514,248,560,248,560,262 2 | 463,261,464,246,509,248,508,263 3 | 430,248,467,248,467,263,430,263 4 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_338.txt: -------------------------------------------------------------------------------- 1 | 506,252,507,221,578,225,576,256 2 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_339.txt: -------------------------------------------------------------------------------- 1 | 730,172,813,160,818,195,735,207 2 | 679,193,730,182,736,208,684,219 3 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_34.txt: -------------------------------------------------------------------------------- 1 | 653,544,664,519,730,549,719,574 2 | 720,204,795,201,796,238,721,241 3 | 677,248,677,211,718,211,718,248 4 | 720,570,734,544,795,577,781,603 5 | 657,517,666,499,720,525,711,543 6 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_340.txt: -------------------------------------------------------------------------------- 1 | 755,269,760,228,854,240,849,281 2 | 847,278,857,241,933,262,923,299 3 | -2,205,124,203,125,248,-1,250 4 | 647,415,722,391,728,410,652,434 5 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_341.txt: -------------------------------------------------------------------------------- 1 | 708,155,866,111,883,173,725,217 2 | 872,81,1143,16,1162,94,891,159 3 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_342.txt: -------------------------------------------------------------------------------- 1 | 995,317,1063,317,1063,353,995,353 2 | 995,252,1061,250,1062,282,996,283 3 | 999,279,1061,279,1061,305,999,305 4 | 1014,357,1049,357,1049,379,1014,379 5 | 551,278,551,253,578,253,578,278 6 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_343.txt: -------------------------------------------------------------------------------- 1 | 215,360,283,360,283,378,215,378 2 | 332,355,403,354,404,374,333,376 3 | 214,130,218,111,278,123,274,142 4 | 288,358,326,358,326,373,288,373 5 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_344.txt: -------------------------------------------------------------------------------- 1 | 338,290,438,287,439,310,339,313 2 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_345.txt: -------------------------------------------------------------------------------- 1 | 22,172,27,140,124,155,119,188 2 | 15,199,20,170,64,178,58,207 3 | 63,207,68,180,126,190,121,217 4 | 259,321,261,300,323,305,321,326 5 | 266,294,267,276,321,278,320,296 6 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_346.txt: -------------------------------------------------------------------------------- 1 | 319,49,342,26,398,82,375,105 2 | 100,212,105,191,140,198,136,219 3 | 53,202,57,180,107,190,103,212 4 | 72,256,77,233,114,240,109,263 5 | 64,230,70,209,102,217,96,238 6 | 96,239,96,215,123,215,123,239 7 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_347.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mindspore-ai/contrib/c6dfba7f4320f116c4656e9fc341e2252b083bc3/papers/FCENet/dataset/icdar15/submit/res_img_347.txt -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_348.txt: -------------------------------------------------------------------------------- 1 | 5,281,21,239,193,301,177,344 2 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_349.txt: -------------------------------------------------------------------------------- 1 | 70,74,126,72,127,103,71,105 2 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_35.txt: -------------------------------------------------------------------------------- 1 | 238,654,337,572,351,588,251,670 2 | 329,60,331,30,448,38,446,68 3 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_351.txt: -------------------------------------------------------------------------------- 1 | 953,310,954,288,1022,290,1021,312 2 | 619,342,620,314,705,316,704,344 3 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_352.txt: -------------------------------------------------------------------------------- 1 | 1009,363,1110,340,1117,369,1016,392 2 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_353.txt: -------------------------------------------------------------------------------- 1 | 426,422,427,406,499,411,498,427 2 | 94,354,109,311,161,330,146,372 3 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_354.txt: -------------------------------------------------------------------------------- 1 | 426,125,427,78,674,83,673,130 2 | 583,56,647,56,647,81,583,81 3 | 444,78,445,45,561,48,560,81 4 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_355.txt: -------------------------------------------------------------------------------- 1 | 996,410,997,377,1145,383,1144,416 2 | 1050,423,1051,408,1143,411,1142,426 3 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_356.txt: -------------------------------------------------------------------------------- 1 | 816,84,871,78,874,103,819,109 2 | 771,93,816,89,818,110,773,114 3 | 954,69,1005,62,1007,78,956,85 4 | 1196,95,1280,91,1281,107,1197,111 5 | 902,75,939,72,941,89,903,92 6 | 1006,62,1076,51,1079,67,1009,78 7 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_357.txt: -------------------------------------------------------------------------------- 1 | 895,173,977,129,991,157,910,200 2 | 855,192,903,172,915,201,867,221 3 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_358.txt: -------------------------------------------------------------------------------- 1 | 1204,206,1261,206,1261,239,1204,239 2 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_359.txt: -------------------------------------------------------------------------------- 1 | 211,224,213,197,312,203,310,230 2 | 311,232,312,206,447,215,445,241 3 | 106,225,106,205,144,205,144,225 4 | 138,201,191,198,192,220,139,223 5 | 322,283,324,269,366,273,365,288 6 | 366,286,367,273,411,277,410,290 7 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_36.txt: -------------------------------------------------------------------------------- 1 | 432,240,434,221,473,224,472,243 2 | 384,237,386,218,434,222,432,241 3 | 545,243,546,226,623,231,622,248 4 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_361.txt: -------------------------------------------------------------------------------- 1 | 667,157,743,88,774,122,698,191 2 | 718,103,821,15,855,54,752,143 3 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_362.txt: -------------------------------------------------------------------------------- 1 | 44,182,49,165,86,174,82,192 2 | 377,37,418,30,421,46,379,53 3 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_363.txt: -------------------------------------------------------------------------------- 1 | 297,59,344,59,344,74,297,74 2 | 493,51,494,38,530,41,529,54 3 | 321,69,354,69,354,83,321,83 4 | 398,56,452,56,452,71,398,71 5 | 785,351,816,351,816,373,785,373 6 | 356,61,396,61,396,76,356,76 7 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_364.txt: -------------------------------------------------------------------------------- 1 | 1157,231,1160,180,1274,185,1272,236 2 | 760,227,761,164,929,168,928,231 3 | 1177,374,1180,345,1244,350,1242,380 4 | 957,232,958,169,1121,172,1120,236 5 | 1054,366,1057,333,1175,344,1172,377 6 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_367.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mindspore-ai/contrib/c6dfba7f4320f116c4656e9fc341e2252b083bc3/papers/FCENet/dataset/icdar15/submit/res_img_367.txt -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_368.txt: -------------------------------------------------------------------------------- 1 | 622,88,730,16,748,43,640,115 2 | 1055,406,1056,372,1120,374,1119,408 3 | 1117,413,1120,380,1162,383,1159,416 4 | 1049,454,1057,397,1171,415,1163,471 5 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_369.txt: -------------------------------------------------------------------------------- 1 | 619,72,738,-7,756,19,636,98 2 | 1144,393,1150,335,1267,348,1260,406 3 | 1148,339,1158,274,1263,290,1253,355 4 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_37.txt: -------------------------------------------------------------------------------- 1 | 803,78,803,56,835,56,835,78 2 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_370.txt: -------------------------------------------------------------------------------- 1 | 67,241,79,175,301,218,288,284 2 | 377,325,459,315,462,345,380,356 3 | 485,350,485,71,548,71,548,350 4 | 135,277,139,250,250,267,246,294 5 | 857,62,916,53,919,71,859,80 6 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_371.txt: -------------------------------------------------------------------------------- 1 | 223,125,224,87,304,90,302,128 2 | 337,110,346,61,504,90,495,139 3 | 551,167,626,160,629,187,554,195 4 | 353,135,356,116,439,132,435,151 5 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_372.txt: -------------------------------------------------------------------------------- 1 | 194,195,202,146,438,182,430,231 2 | 272,347,347,344,348,368,273,371 3 | 853,270,857,247,893,254,889,277 4 | 1213,392,1215,361,1278,366,1276,396 5 | 644,277,645,258,711,264,710,283 6 | 885,291,889,273,926,280,922,298 7 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_373.txt: -------------------------------------------------------------------------------- 1 | 1046,245,1047,197,1268,200,1267,248 2 | 1060,360,1115,360,1115,377,1060,377 3 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_374.txt: -------------------------------------------------------------------------------- 1 | 1002,244,1100,234,1105,288,1008,298 2 | -4,150,2,99,128,116,121,167 3 | 383,247,386,219,502,230,499,258 4 | 1018,319,1077,310,1084,355,1024,363 5 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_375.txt: -------------------------------------------------------------------------------- 1 | 106,213,110,181,193,193,189,224 2 | 443,230,453,200,498,215,488,245 3 | 485,267,491,244,530,254,524,277 4 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_376.txt: -------------------------------------------------------------------------------- 1 | 523,159,538,111,717,166,701,215 2 | 709,207,720,168,810,193,799,232 3 | 358,233,398,233,398,357,358,357 4 | 5,378,47,376,48,393,6,395 5 | 147,357,148,334,238,337,237,360 6 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_377.txt: -------------------------------------------------------------------------------- 1 | 646,195,691,180,697,197,652,212 2 | 843,266,903,259,906,286,846,293 3 | 804,304,806,283,849,286,848,307 4 | 896,274,950,272,951,302,897,304 5 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_378.txt: -------------------------------------------------------------------------------- 1 | -23,175,3,100,146,151,119,226 2 | 381,244,388,222,432,236,425,258 3 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_379.txt: -------------------------------------------------------------------------------- 1 | 340,92,372,55,474,145,442,181 2 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_38.txt: -------------------------------------------------------------------------------- 1 | 648,153,650,95,931,106,929,164 2 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_380.txt: -------------------------------------------------------------------------------- 1 | 673,571,691,537,806,597,787,631 2 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_381.txt: -------------------------------------------------------------------------------- 1 | 630,116,784,116,784,168,630,168 2 | 940,305,1015,277,1027,310,951,338 3 | 5,155,47,153,48,173,6,174 4 | 470,242,539,206,557,239,487,275 5 | 795,119,852,119,852,166,795,166 6 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_382.txt: -------------------------------------------------------------------------------- 1 | 682,298,754,257,774,291,701,332 2 | 70,144,154,131,159,162,75,175 3 | 72,178,160,163,165,192,77,207 4 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_383.txt: -------------------------------------------------------------------------------- 1 | 947,41,1018,32,1022,58,951,67 2 | 1150,298,1177,295,1179,312,1152,316 3 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_384.txt: -------------------------------------------------------------------------------- 1 | 0,238,0,222,44,222,44,238 2 | 757,172,758,158,786,160,785,174 3 | 717,171,718,155,761,158,760,174 4 | 1019,95,1021,14,1074,15,1072,96 5 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_385.txt: -------------------------------------------------------------------------------- 1 | 105,304,105,285,175,285,175,304 2 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_386.txt: -------------------------------------------------------------------------------- 1 | 509,493,510,461,591,463,590,495 2 | 589,467,630,465,631,494,590,495 3 | 332,220,333,197,416,199,415,222 4 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_387.txt: -------------------------------------------------------------------------------- 1 | 808,333,950,325,951,352,809,360 2 | 584,337,630,337,630,362,584,362 3 | 643,332,690,332,690,357,643,357 4 | 732,98,773,83,780,100,738,116 5 | 551,413,552,400,581,402,580,415 6 | 122,353,124,331,166,334,164,356 7 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_390.txt: -------------------------------------------------------------------------------- 1 | 339,78,367,-14,751,100,723,193 2 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_391.txt: -------------------------------------------------------------------------------- 1 | 741,266,757,226,819,251,804,291 2 | 800,287,813,251,866,270,853,306 3 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_392.txt: -------------------------------------------------------------------------------- 1 | 485,90,486,50,632,55,631,95 2 | 794,95,795,60,924,63,923,98 3 | 277,88,278,47,382,50,381,91 4 | 579,277,622,264,628,285,585,298 5 | 578,239,619,230,623,248,581,257 6 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_393.txt: -------------------------------------------------------------------------------- 1 | 574,178,628,162,637,192,582,208 2 | 580,207,628,193,636,218,587,232 3 | 571,158,628,139,635,162,578,181 4 | 590,228,615,218,620,232,595,241 5 | 592,239,610,231,614,239,595,247 6 | 612,222,636,217,639,230,614,235 7 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_394.txt: -------------------------------------------------------------------------------- 1 | 564,335,624,316,633,346,573,365 2 | 562,303,627,284,637,317,572,336 3 | 561,277,627,257,635,283,569,304 4 | 1159,99,1163,49,1280,58,1276,108 5 | 434,66,437,-1,704,11,701,79 6 | 1024,92,1029,38,1155,50,1150,104 7 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_395.txt: -------------------------------------------------------------------------------- 1 | 324,134,329,101,387,108,383,141 2 | 430,167,432,139,471,142,469,170 3 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_397.txt: -------------------------------------------------------------------------------- 1 | 315,287,321,237,430,250,424,300 2 | 314,230,323,175,477,202,468,258 3 | 455,300,459,257,545,264,541,307 4 | 1145,153,1259,150,1260,185,1146,188 5 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_399.txt: -------------------------------------------------------------------------------- 1 | 233,185,296,178,299,198,236,206 2 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_4.txt: -------------------------------------------------------------------------------- 1 | 766,422,770,406,820,421,815,436 2 | 773,389,775,369,816,374,814,395 3 | 1178,335,1244,317,1252,347,1187,365 4 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_40.txt: -------------------------------------------------------------------------------- 1 | 649,155,705,155,705,176,649,176 2 | 703,149,754,147,755,170,704,172 3 | 688,129,729,127,731,151,689,154 4 | 643,132,696,130,697,152,644,153 5 | 729,126,764,124,765,146,730,148 6 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_400.txt: -------------------------------------------------------------------------------- 1 | 131,486,195,436,221,469,156,519 2 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_401.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mindspore-ai/contrib/c6dfba7f4320f116c4656e9fc341e2252b083bc3/papers/FCENet/dataset/icdar15/submit/res_img_401.txt -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_402.txt: -------------------------------------------------------------------------------- 1 | 978,636,979,618,1005,620,1003,638 2 | 949,610,951,586,1001,590,999,614 3 | 941,632,943,613,970,616,968,635 4 | 952,572,953,560,991,564,990,575 5 | 680,309,701,309,701,323,680,323 6 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_403.txt: -------------------------------------------------------------------------------- 1 | 238,476,318,428,343,470,263,518 2 | 241,590,343,510,360,533,258,613 3 | 255,556,335,497,349,516,269,576 4 | 150,483,204,452,214,469,160,500 5 | 200,461,239,447,246,467,208,481 6 | 428,292,477,282,482,308,434,318 7 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_404.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mindspore-ai/contrib/c6dfba7f4320f116c4656e9fc341e2252b083bc3/papers/FCENet/dataset/icdar15/submit/res_img_404.txt -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_407.txt: -------------------------------------------------------------------------------- 1 | 673,294,747,283,753,324,679,334 2 | 837,273,918,265,922,302,841,309 3 | 754,285,828,276,833,313,758,322 4 | 921,265,995,258,999,294,925,301 5 | -------------------------------------------------------------------------------- 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-------------------------------------------------------------------------------- 1 | 692,233,693,216,764,219,763,236 2 | 705,248,706,230,789,234,788,252 3 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_411.txt: -------------------------------------------------------------------------------- 1 | 352,258,357,230,498,252,493,280 2 | 350,235,353,210,484,229,481,254 3 | 0,348,0,330,36,330,36,348 4 | 80,217,83,200,122,208,118,225 5 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_412.txt: -------------------------------------------------------------------------------- 1 | 1060,201,1137,199,1138,229,1061,231 2 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_414.txt: -------------------------------------------------------------------------------- 1 | 491,203,494,157,685,169,682,215 2 | 693,210,694,173,849,178,848,215 3 | 421,192,422,154,479,156,478,194 4 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_415.txt: -------------------------------------------------------------------------------- 1 | 189,279,197,225,399,253,391,307 2 | 181,329,184,283,387,294,384,340 3 | 688,231,689,192,856,195,855,234 4 | 255,341,373,341,373,389,255,389 5 | 388,202,418,202,418,215,388,215 6 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_416.txt: -------------------------------------------------------------------------------- 1 | 738,176,774,173,776,194,740,198 2 | 52,116,234,99,237,135,55,151 3 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_417.txt: -------------------------------------------------------------------------------- 1 | 83,144,130,89,276,214,229,268 2 | -30,58,20,0,158,121,107,180 3 | 268,595,268,572,330,572,330,595 4 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_418.txt: -------------------------------------------------------------------------------- 1 | 549,324,705,311,708,340,552,354 2 | 616,374,650,371,651,387,617,389 3 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_419.txt: -------------------------------------------------------------------------------- 1 | 1032,460,1033,435,1126,442,1124,467 2 | 1025,436,1027,411,1123,418,1121,443 3 | 625,324,626,299,669,301,668,326 4 | 1022,363,1023,339,1117,343,1116,367 5 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_42.txt: -------------------------------------------------------------------------------- 1 | 502,164,621,111,635,143,517,196 2 | 1091,149,1151,148,1153,410,1093,411 3 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_420.txt: -------------------------------------------------------------------------------- 1 | 428,219,454,179,526,225,501,265 2 | 286,142,320,85,484,181,451,238 3 | 511,268,522,247,563,268,552,290 4 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_423.txt: -------------------------------------------------------------------------------- 1 | 776,187,899,178,902,223,779,233 2 | 705,403,705,387,732,387,732,403 3 | 686,388,711,388,711,405,686,405 4 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_424.txt: -------------------------------------------------------------------------------- 1 | 690,141,831,38,864,84,724,187 2 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_426.txt: -------------------------------------------------------------------------------- 1 | 1003,123,1147,116,1149,148,1004,155 2 | 305,127,571,120,572,164,306,172 3 | 526,171,608,168,609,190,527,192 4 | 451,178,520,175,521,195,452,197 5 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_428.txt: -------------------------------------------------------------------------------- 1 | 145,175,344,169,345,215,146,221 2 | 1051,285,1178,263,1185,300,1058,322 3 | 1138,378,1235,378,1235,412,1138,412 4 | 404,346,405,332,463,334,462,348 5 | 657,304,659,281,702,284,700,307 6 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_429.txt: -------------------------------------------------------------------------------- 1 | -1,201,100,195,102,226,0,232 2 | 665,384,726,384,726,410,665,410 3 | 155,358,156,343,219,346,218,361 4 | 389,324,390,304,432,307,430,327 5 | 647,315,707,312,708,337,648,340 6 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_43.txt: -------------------------------------------------------------------------------- 1 | 13,189,183,168,188,208,18,229 2 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_430.txt: -------------------------------------------------------------------------------- 1 | 265,46,291,15,370,80,344,112 2 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_431.txt: -------------------------------------------------------------------------------- 1 | 291,212,291,189,389,189,389,212 2 | 96,196,97,141,415,145,414,200 3 | 297,282,302,263,345,274,340,294 4 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_432.txt: -------------------------------------------------------------------------------- 1 | 391,123,433,42,686,174,644,255 2 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_433.txt: -------------------------------------------------------------------------------- 1 | 608,196,676,196,676,218,608,218 2 | 596,165,735,165,735,200,596,200 3 | 490,198,490,166,593,166,593,198 4 | 682,197,732,195,733,214,683,217 5 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_434.txt: -------------------------------------------------------------------------------- 1 | 222,250,224,81,288,82,286,251 2 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_437.txt: -------------------------------------------------------------------------------- 1 | 936,122,1156,-14,1179,21,958,158 2 | 1046,358,1159,356,1160,376,1047,378 3 | 698,490,698,460,734,460,734,490 4 | 696,415,731,410,735,436,700,441 5 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_438.txt: -------------------------------------------------------------------------------- 1 | 978,141,1074,55,1106,90,1009,176 2 | 1148,449,1271,438,1276,491,1153,502 3 | 1138,400,1270,390,1274,444,1143,454 4 | 1067,60,1154,-5,1182,30,1095,97 5 | 667,356,715,346,719,363,671,373 6 | -------------------------------------------------------------------------------- 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-------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_441.txt: -------------------------------------------------------------------------------- 1 | 168,32,177,-1,247,18,237,52 2 | 115,70,127,18,224,40,212,92 3 | 185,115,192,82,263,98,256,131 4 | 130,99,138,71,190,86,182,114 5 | 210,88,223,40,286,58,273,105 6 | 277,463,288,243,340,245,330,465 7 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_442.txt: -------------------------------------------------------------------------------- 1 | 259,183,264,160,380,184,375,207 2 | 588,404,590,201,630,202,628,405 3 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_443.txt: -------------------------------------------------------------------------------- 1 | 1011,163,1083,147,1090,176,1018,193 2 | 1109,147,1201,120,1206,139,1114,166 3 | 159,44,185,6,271,65,245,103 4 | 1202,120,1233,112,1238,132,1207,139 5 | 1089,150,1122,143,1126,161,1093,168 6 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_446.txt: -------------------------------------------------------------------------------- 1 | -6,107,32,60,178,182,139,229 2 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_447.txt: -------------------------------------------------------------------------------- 1 | 146,160,160,127,308,193,294,226 2 | 304,245,315,212,405,242,394,275 3 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_448.txt: -------------------------------------------------------------------------------- 1 | 212,237,222,210,284,233,274,260 2 | 272,257,282,231,329,249,320,275 3 | 11,205,18,178,132,208,124,235 4 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_45.txt: -------------------------------------------------------------------------------- 1 | 724,133,725,107,783,109,782,135 2 | 459,116,460,97,502,99,501,117 3 | 377,112,378,93,458,97,457,116 4 | 163,145,202,145,202,174,163,174 5 | 162,202,163,176,239,177,238,204 6 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_450.txt: -------------------------------------------------------------------------------- 1 | 738,56,796,52,798,74,739,79 2 | 444,287,529,268,534,291,449,310 3 | 698,62,733,58,736,80,701,84 4 | 478,87,524,79,528,103,482,111 5 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_453.txt: -------------------------------------------------------------------------------- 1 | 457,149,572,143,573,166,458,172 2 | 198,162,280,158,281,180,199,184 3 | 460,388,539,362,547,384,467,411 4 | 808,411,900,365,914,392,822,439 5 | 376,411,465,386,471,408,382,433 6 | 435,170,595,155,598,182,438,197 7 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_455.txt: -------------------------------------------------------------------------------- 1 | 735,142,803,136,805,155,737,161 2 | 697,151,735,144,738,162,700,169 3 | 973,17,1014,12,1017,34,976,39 4 | 750,298,814,295,815,308,751,311 5 | 586,176,622,173,623,186,587,189 6 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_456.txt: -------------------------------------------------------------------------------- 1 | 262,409,342,402,345,425,264,432 2 | 129,93,130,67,205,70,204,96 3 | 166,415,262,410,263,433,167,438 4 | 239,99,240,71,320,75,319,103 5 | 206,95,206,70,243,70,243,95 6 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_457.txt: -------------------------------------------------------------------------------- 1 | 297,10,615,-3,618,72,300,86 2 | 640,5,898,-5,901,51,643,62 3 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_458.txt: -------------------------------------------------------------------------------- 1 | 112,38,172,17,182,46,122,67 2 | 170,17,246,-7,256,22,180,47 3 | 365,329,365,205,389,205,389,329 4 | 707,199,743,198,745,342,709,343 5 | 997,236,1036,236,1036,357,997,357 6 | 250,-2,296,-11,300,10,255,19 7 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_459.txt: -------------------------------------------------------------------------------- 1 | 828,249,880,247,881,265,829,267 2 | 766,252,826,249,827,269,767,272 3 | 745,313,800,310,801,330,746,333 4 | 714,175,762,162,769,188,721,202 5 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_46.txt: -------------------------------------------------------------------------------- 1 | 846,240,927,235,929,269,848,273 2 | 1065,294,1153,294,1153,324,1065,324 3 | 1074,343,1094,343,1094,356,1074,356 4 | 1073,345,1073,333,1091,333,1091,345 5 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_463.txt: -------------------------------------------------------------------------------- 1 | 1052,631,1106,513,1289,597,1235,716 2 | 1092,528,1119,451,1280,508,1254,584 3 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_465.txt: -------------------------------------------------------------------------------- 1 | 883,311,884,271,994,275,993,315 2 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_467.txt: -------------------------------------------------------------------------------- 1 | 903,22,1018,12,1021,46,906,56 2 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_469.txt: -------------------------------------------------------------------------------- 1 | 82,243,83,209,175,212,174,246 2 | 853,255,854,227,959,230,958,258 3 | 627,249,628,209,850,218,849,258 4 | 178,215,241,215,241,246,178,246 5 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_470.txt: -------------------------------------------------------------------------------- 1 | 417,66,425,40,526,71,518,97 2 | 638,130,644,103,723,122,717,148 3 | 516,95,523,69,602,91,595,117 4 | 1173,295,1181,247,1279,265,1271,313 5 | 1195,253,1203,213,1282,229,1274,269 6 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_471.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mindspore-ai/contrib/c6dfba7f4320f116c4656e9fc341e2252b083bc3/papers/FCENet/dataset/icdar15/submit/res_img_471.txt -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_472.txt: -------------------------------------------------------------------------------- 1 | 1130,208,1221,203,1225,257,1134,263 2 | 1117,169,1181,160,1188,207,1124,216 3 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_473.txt: -------------------------------------------------------------------------------- 1 | 217,131,217,107,329,107,329,131 2 | 684,264,747,257,748,273,685,280 3 | 212,110,212,71,325,71,325,110 4 | 765,254,839,250,840,267,766,271 5 | 750,269,854,259,857,285,753,295 6 | 721,317,813,306,815,321,723,333 7 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_475.txt: -------------------------------------------------------------------------------- 1 | 513,106,615,99,619,149,516,156 2 | 493,197,534,194,535,211,494,214 3 | 737,426,823,397,832,425,746,454 4 | 436,200,492,199,493,217,437,219 5 | 435,185,435,167,490,167,490,185 6 | 443,268,479,261,483,281,447,288 7 | -------------------------------------------------------------------------------- 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445,47,480,46,482,111,447,112 6 | 122,525,189,494,200,518,133,549 7 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_481.txt: -------------------------------------------------------------------------------- 1 | 313,432,399,417,404,444,318,459 2 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_483.txt: -------------------------------------------------------------------------------- 1 | 874,82,970,1,995,30,899,112 2 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_484.txt: -------------------------------------------------------------------------------- 1 | 267,328,320,324,321,340,268,344 2 | 281,357,321,357,321,377,281,377 3 | -------------------------------------------------------------------------------- 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326,240,353,238,354,249,327,251 7 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_495.txt: -------------------------------------------------------------------------------- 1 | 532,68,548,21,705,75,688,122 2 | 31,342,34,284,270,294,267,353 3 | 515,360,545,360,545,379,515,379 4 | 411,345,413,331,453,336,451,351 5 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_498.txt: -------------------------------------------------------------------------------- 1 | 814,66,814,26,940,26,940,66 2 | 898,68,978,65,979,100,899,103 3 | 819,73,891,70,892,103,820,106 4 | 949,26,987,26,987,59,949,59 5 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_499.txt: -------------------------------------------------------------------------------- 1 | 344,262,379,256,382,271,346,277 2 | 384,217,425,211,428,228,386,234 3 | 343,244,391,237,393,253,346,260 4 | 397,237,423,230,427,244,400,251 5 | 341,227,380,220,383,236,344,242 6 | 383,253,425,247,427,264,385,270 7 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_5.txt: -------------------------------------------------------------------------------- 1 | 618,73,661,73,661,94,618,94 2 | 621,142,662,142,662,160,621,160 3 | 608,199,679,196,681,231,610,234 4 | 603,116,604,93,674,96,673,119 5 | 617,134,619,115,661,119,660,138 6 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_50.txt: -------------------------------------------------------------------------------- 1 | 815,438,880,407,895,438,831,469 2 | 747,441,819,433,822,464,750,472 3 | 688,629,758,592,776,626,706,663 4 | 1238,18,1275,18,1275,39,1238,39 5 | 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-------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_54.txt: -------------------------------------------------------------------------------- 1 | 15,15,185,-10,190,22,20,48 2 | 900,46,955,40,958,68,903,75 3 | 243,144,252,118,313,139,304,165 4 | 211,132,220,109,257,124,248,147 5 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_56.txt: -------------------------------------------------------------------------------- 1 | 733,246,770,246,770,263,733,263 2 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_57.txt: -------------------------------------------------------------------------------- 1 | 601,174,654,167,656,185,603,192 2 | 373,205,437,198,440,221,376,229 3 | -------------------------------------------------------------------------------- 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-------------------------------------------------------------------------------- 1 | 1216,202,1279,202,1279,227,1216,227 2 | 1081,249,1165,247,1166,281,1082,284 3 | 1166,200,1214,200,1214,226,1166,226 4 | 1154,369,1154,354,1198,354,1198,369 5 | 1143,386,1143,370,1202,370,1202,386 6 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_70.txt: -------------------------------------------------------------------------------- 1 | 67,488,176,443,190,477,81,522 2 | 98,431,149,412,158,437,107,456 3 | 94,540,167,496,179,517,106,560 4 | 68,448,99,438,106,457,75,468 5 | 63,403,148,373,156,396,72,426 6 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_71.txt: -------------------------------------------------------------------------------- 1 | 526,158,590,146,597,180,533,193 2 | 476,174,529,161,537,192,484,205 3 | 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565,142,567,117,623,121,621,146 2 | 454,138,455,114,529,116,528,140 3 | 815,111,897,59,919,94,837,145 4 | 408,151,409,137,467,141,466,155 5 | 889,63,995,6,1016,46,909,102 6 | 503,168,504,156,545,160,544,171 7 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_8.txt: -------------------------------------------------------------------------------- 1 | 470,88,540,82,541,100,471,106 2 | 1020,232,1086,215,1100,268,1033,284 3 | 871,423,980,412,983,441,873,452 4 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_80.txt: -------------------------------------------------------------------------------- 1 | 1169,184,1202,181,1203,196,1171,199 2 | 1129,204,1272,182,1277,218,1134,240 3 | 1148,187,1175,182,1177,196,1150,201 4 | 783,86,898,28,919,70,804,128 5 | 1201,178,1240,173,1242,191,1203,197 6 | 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380,301,436,301,436,318,380,318 7 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_97.txt: -------------------------------------------------------------------------------- 1 | 1114,162,1266,128,1274,166,1123,200 2 | 1012,205,1075,194,1078,214,1015,225 3 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/icdar15/submit/res_img_99.txt: -------------------------------------------------------------------------------- 1 | 869,262,1098,262,1098,324,869,324 2 | 865,373,866,311,1150,317,1149,379 3 | 886,654,892,591,1175,616,1169,679 4 | 276,213,277,198,330,200,329,215 5 | 895,705,899,649,1128,664,1124,720 6 | -------------------------------------------------------------------------------- /papers/FCENet/dataset/total_text/Evaluation_Protocol/Examples/Groundtruth/poly_gt_img1.mat: -------------------------------------------------------------------------------- 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