deep learning for image processing

Tutorial on application of deep learning in image processing

Preface

• This tutorial is a summary of my research content during my postgraduate study. I hope to help more friends. If you learn new knowledge later, you will share it with you.
• This tutorial will be shared in a video format, and the teaching process is as follows:
  1) Introduce the structure and innovation points of the network
  2) Use Pytorch to build and train networks
  3) Use Tensorflow (internal keras module) to build and train networks
• All PPTs in the course are placed in the course_ppt folder and download them yourself if needed.

Tutorial directory, click to jump to the corresponding video (it will be added according to the learning content later)

• Image classification

  • LeNet (completed)

    • Pytorch official demo (Lenet)
    • Tensorflow2 official demo

  • AlexNet (completed)

    • AlexNet Network Explanation
    • Pytorch builds AlexNet
    • Tensorflow2 builds Alexnet

  • VggNet (completed)

    • VggNet Network Explanation
    • Pytorch builds VGG network
    • Tensorflow2 builds VGG network

  • GoogLeNet (Completed)

    • GoogleLeNet Network Explanation
    • Pytorch builds GoogleLeNet network
    • Tensorflow2 builds GoogleLeNet network

  • ResNet (completed)

    • ResNet Network Explanation
    • Pytorch builds ResNet network
    • Tensorflow2 builds ResNet network

  • ResNeXt (Completed)

    • ResNeXt Network Explanation
    • Pytorch builds ResNeXt network

  • MobileNet_V1_V2 (completed)

    • MobileNet_V1_V2 Network Explanation
    • Pytorch builds MobileNetV2 network
    • Tensorflow2 builds MobileNetV2 network

  • MobileNet_V3 (completed)

    • MobileNet_V3 Network Explanation
    • Pytorch builds MobileNetV3 network
    • Tensorflow2 builds MobileNetV3 network

  • ShuffleNet_V1_V2 (completed)

    • ShuffleNet_V1_V2 Network Explanation
    • Use Pytorch to build ShuffleNetV2
    • Use Tensorflow2 to build ShuffleNetV2

  • EfficientNet_V1 (completed)

    • EfficientNet Network Explanation
    • Use Pytorch to build EfficientNet
    • Building EfficientNet with Tensorflow2

  • EfficientNet_V2 (completed)

    • EfficientNetV2 network explanation
    • Use Pytorch to build EfficientNetV2
    • Use Tensorflow to build EfficientNetV2

  • RepVGG (Completed)

    • RepVGG Network Explanation

  • Vision Transformer(completed)

    • Multi-Head Attention Explanation
    • Vision Transformer Network Explanation
    • Use Pytorch to build Vision Transformer
    • Use tensorflow2 to build Vision Transformer

  • Swin Transformer(completed)

    • Swin Transformer Network Explanation
    • Use Pytorch to build Swin Transformer
    • Use Tensorflow2 to build Swin Transformer

  • ConvNeXt(completed)

    • ConvNeXt Network Explanation
    • Use Pytorch to build ConvNeXt
    • Using Tensorflow2 to build ConvNeXt

  • MobileViT (completed)

    • MobileViT Network Explanation
    • Using Pytorch to build MobileViT

• Target detection

  • Faster-RCNN/FPN (Completed)

    • Faster-RCNN Network Explanation
    • FPN Network Explanation
    • Faster-RCNN source code analysis (Pytorch)

  • SSD/RetinaNet (Completed)

    • SSD Network Explanation
    • RetinaNet Network Explanation
    • SSD source code analysis (Pytorch)

  • YOLO Series (Completed)

    • YOLO series network explanation (V1~V3)
    • YOLOv3 SPP source code analysis (Pytorch version)
    • YOLOV4 Network Explanation
    • YOLOV5 Network Explanation
    • YOLOX Online Explanation

  • FCOS (Completed)

    • FCOS Network Explanation

• Semantic segmentation

  • FCN (Completed)

    • FCN Network Explanation
    • FCN source code analysis (Pytorch version)

  • DeepLabV3 (Completed)

    • DeepLabV1 Network Explanation
    • DeepLabV2 network explanation
    • DeepLabV3 network explanation
    • DeepLabV3 source code analysis (Pytorch version)

  • LR-ASPP (Completed)

    • LR-ASPP Network Explanation
    • LR-ASPP source code analysis (Pytorch version)

  • U-Net (Completed)

    • U-Net Network Explanation
    • U-Net source code analysis (Pytorch version)

  • U2Net (Completed)

    • U2Net Network Explanation
    • U2Net source code analysis (Pytorch version)

• Instance segmentation

  • Mask R-CNN (Completed)
    • Mask R-CNN Network Explanation
    • Mask R-CNN source code analysis (Pytorch version)

• Key point detection

  • DeepPose (completed)

    • DeepPose Network Explanation
    • DeepPose source code analysis (Pytorch version)

  • HRNet (Completed)

    • HRNet Network Explanation
    • HRNet source code analysis (Pytorch version)

For more related videos, please visit my bilibili channel.

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Required environment

• Anaconda3 (recommended)
• python3.6/3.7/3.8
• pycharm (IDE)
• pytorch 1.10 (pip package)
• torchvision 0.11.1 (pip package)
• tensorflow 2.4.1 (pip package)

Everyone is welcome to follow my WeChat official account ( Azhe’s study notes ), and will summarize some related learning blog posts in daily life.

If you have any questions, you can also discuss them in my CSDN. https://blog.csdn.net/qq_37541097/article/details/103482003

My bilibili channel: https://space.bilibili.com/18161609/channel/index