├── .gitignore ├── LICENSE ├── README.md ├── code ├── chapter01_preliminary_knowledge │ ├── 1.3_automatic_gradient │ │ └── autograd.py │ ├── 1.4_linear_regression_pytorch │ │ └── linear_regression_pytorch.py │ └── 1.5_FC_MNIST_Classification │ │ └── FC_MNIST_Classification.py ├── chapter02_image_classification_introduction │ ├── 2.2_introduction_of_image_classification │ │ └── classical_cnn_models │ │ │ ├── AlexNet │ │ │ └── AlexNet.py │ │ │ ├── BN │ │ │ └── BatchNormalization.py │ │ │ ├── GoogLeNet │ │ │ ├── .gitignore │ │ │ ├── GoogLeNet.py │ │ │ └── main.py │ │ │ ├── NiN │ │ │ └── NiN.py │ │ │ ├── ResNet │ │ │ └── ResNet.py │ │ │ ├── VGG │ │ │ ├── main.py │ │ │ └── vgg_models.py │ │ │ ├── lenet │ │ │ ├── LeNet.ipynb │ │ │ └── LeNet.py │ │ │ └── pytorch-vgg-cifar10 │ │ │ ├── LICENSE │ │ │ ├── README.md │ │ │ ├── main.py │ │ │ ├── plot.ipynb │ │ │ ├── requirements.txt │ │ │ ├── run.sh │ │ │ ├── vgg.py │ │ │ └── vgg_plot.png │ └── 2.4_classification_action_SVHN │ │ └── baseline.py ├── chapter03_object_detection_introduction │ └── tiny_detector_demo │ │ ├── create_data_lists.py │ │ ├── datasets.py │ │ ├── detect.py │ │ ├── eval.py │ │ ├── model.py │ │ ├── train.py │ │ └── utils.py ├── chapter04_segmentation_introduction │ └── building_identification_baseline │ │ ├── checkpoint │ │ └── pretrain │ │ │ └── .gitkeep │ │ ├── dataset.py │ │ ├── logs │ │ └── .gitkeep │ │ ├── train.py │ │ ├── training_model.sh │ │ ├── unet │ │ ├── __init__.py │ │ ├── unet_model.py │ │ └── unet_parts.py │ │ └── utils │ │ ├── __init__.py │ │ ├── data_vis.py │ │ └── model_saver.py └── chapter06_transformer │ ├── 6.1_hello_transformer │ ├── first_train_demo.py │ └── transformer.py │ ├── 6.2_recognition_by_transformer(online_dataset) │ ├── analysis_recognition_dataset.py │ ├── ocr_by_transformer.py │ ├── train_utils.py │ └── transformer.py │ └── 6.2_recognition_by_transformer │ ├── analysis_recognition_dataset.py │ ├── ocr_by_transformer.py │ ├── train_utils.py │ └── transformer.py ├── dataset └── .gitkeep ├── docs ├── .nojekyll ├── README.md ├── _coverpage.md ├── _sidebar.md ├── chapter01_preliminary_knowledge │ ├── 1.1_environment_install │ │ └── README.md │ ├── 1.2_pytorch_basic_usage_introduction │ │ └── README.md │ ├── 1.3_automatic_gradient │ │ └── README.md │ ├── 1.4_linear_regression_pytorch │ │ └── README.md │ └── 1.5_mnist_classification │ │ └── README.md ├── chapter02_image_classification_introduction │ ├── 2.1_dataloader_and_augmentation │ │ └── README.md │ ├── 2.2_introduction_of_image_classification │ │ └── README.md │ ├── 2.3_model_training_and_verification │ │ └── README.md │ ├── 2.4_classification_action_SVHN │ │ ├── baseline.md │ │ └── ideas_and_tricks_summary.md │ └── appendix │ │ └── appendixA_data_augment.md ├── chapter03_object_detection_introduction │ ├── 3_1.md │ ├── 3_2.md │ ├── 3_3.md │ ├── 3_4.md │ ├── 3_5.md │ ├── 3_6.md │ ├── gongshi_test.md │ └── introduction.md ├── chapter05_gan │ ├── 5_1.md │ ├── 5_2.md │ ├── 5_3.md │ ├── 5_4.md │ ├── introduction.md │ └── temp.md ├── chapter06_transformer │ ├── 6_1_hello_transformer.md │ └── 6_2_ocr_by_transformer.md └── index.html └── markdown_imgs ├── .gitkeep ├── chapter01 ├── 1.1_environment_install │ ├── cuda11_install_1.png │ ├── cuda11_install_2.png │ ├── cuda11_install_3.png │ ├── cuda11_install_4.png │ ├── select_CUDA_version.png │ ├── select_installer.png │ ├── select_pytorch.png │ └── torch_test.png ├── 1.4_linear_regression_pytorch │ ├── estimate_after_train.png │ ├── estimate_before_train.png │ ├── grad_decent.png │ └── src_data.png └── 1.5_FC_MNIST_Classification │ ├── FC.png │ ├── FC2.png │ ├── MNIST.png │ ├── acticve.png │ ├── datashow.png │ └── resultshow.png ├── chapter02 ├── 2.1_dataloader_and_augmentation │ ├── CIFAR10展示.png │ ├── MNIST展示1.png │ ├── MNIST展示2.png │ ├── cat.png │ ├── crop.png │ ├── dataset_show.png │ ├── flip_and_rotation.png │ ├── imageNet下载方式.png │ ├── imageNet层次结构.png │ ├── imageNet展示.png │ ├── ori.png │ ├── train_test_file.png │ └── transform.png ├── 2.2_introduction_of_image_classification │ ├── 5.11_residual-block.svg │ ├── 5.9_inception.svg │ ├── 640.png │ ├── Alex-net.png │ ├── Incep-net.png │ ├── Le_CNN.png │ ├── Le_net.png │ ├── NiN.png │ ├── Resnet50.png │ ├── VGG.png │ ├── VGG1.png │ ├── alex_test1.png │ ├── alexnet.png │ ├── cnn.jpeg │ ├── cnn1.jpeg │ ├── conv.png │ ├── convlayer_overview_demo.gif │ ├── googlenet.png │ ├── lenet.png │ ├── linear_conv.png │ ├── loss.png │ ├── mlp_conv.png │ ├── overview.png │ ├── relu_graph.svg │ ├── softmax_animation.gif │ ├── 卷积.png │ └── 网络发展.png ├── 2.3_model_training_and_verification │ ├── dataset_check.png │ ├── dataset_ex.png │ └── relu.png └── 2.4_classification_action_SVHN │ ├── 2_5_SVHN_dataset.png │ ├── 2_5_baseline_bug.png │ ├── 2_5_char_detect.png │ ├── 2_5_chardetect_identify.png │ ├── 2_5_data_augment.png │ ├── 2_5_diff_long_char.png │ ├── 2_5_fix_len_identify.png │ ├── 2_5_linedetect_identify.png │ ├── 2_5_paddXX.png │ ├── 2_5_svhn_hen.png │ ├── 2_5_test_show.png │ └── 2_5_val_show.png ├── chapter03 ├── 3-1.png ├── 3-10.png ├── 3-11.png ├── 3-12.png ├── 3-13.png ├── 3-14.png ├── 3-15.jpg ├── 3-16.jpg ├── 3-17.png ├── 3-18_a.jpg ├── 3-18_b.png ├── 3-19.png ├── 3-2.png ├── 3-20.png ├── 3-21.png ├── 3-22.png ├── 3-23.png ├── 3-24.png ├── 3-25.png ├── 3-26.png ├── 3-27.png ├── 3-28.png ├── 3-29.png ├── 3-3.png ├── 3-30.png ├── 3-31.png ├── 3-32.png ├── 3-33.jpg ├── 3-4.png ├── 3-5.png ├── 3-6.png ├── 3-7.png ├── 3-8.png ├── 3-9.png └── 3.6_train_and_test │ ├── inference_000001.jpg │ ├── inference_000004.jpg │ ├── inference_000007.jpg │ ├── inference_000010.jpg │ ├── inference_000011.jpg │ ├── inference_000021.jpg │ ├── inference_000030.jpg │ ├── inference_000042.jpg │ └── inference_000050.jpg ├── chapter05 ├── CGAN.png ├── CGAN_img.png ├── CGAN架构.png ├── DCGAN_generator.png ├── DCGAN图片生成过程可视化.png ├── DCGAN真实图片和虚假图片.png ├── D_1.png ├── D_2.png ├── DandG_Loss.png ├── GAN.png ├── GAN的应用.png ├── GAN的训练过程.png ├── GAN网络框架.png ├── 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