Introduction to the Deep Learning for Image Processing Project
Overview
This project, centered on deep learning applications in image processing, is a comprehensive tutorial based on the organizer's graduate research. They have curated the content to share knowledge and expertise with others, with plans to regularly update and share new insights as they become available. The tutorials are delivered through video lectures, encompassing a systematic teaching approach which includes:
- Introducing the structure and innovations of various network architectures.
- Training and building these networks using Pytorch.
- Implementing the networks with Tensorflow, specifically using the internal Keras module.
Additionally, the course materials, including PowerPoint presentations, are readily accessible in the course_ppt folder for download.
Course Contents
The tutorial is organized into different modules, each focusing on a specific task within image processing such as image classification, object detection, semantic segmentation, instance segmentation, and key point detection. Each module includes video links that provide detailed explanations and instructions.
Image Classification
- LeNet:
- Introduction and Pytorch demo as well as a Tensorflow2 demo.
- AlexNet, VggNet, GoogLeNet, ResNet, ResNeXt, MobileNet Series, ShuffleNet, EfficientNet Series, Vision Transformer, Swin Transformer, ConvNext, MobileViT:
- Each network includes discussions that cover architecture details and network building tutorials using both Pytorch and TensorFlow.
Object Detection
- Faster-RCNN/FPN, SSD/RetinaNet, YOLO Series, FCOS:
- Video content covers the explanation of each network’s dynamics and Pytorch-based source code analysis.
Semantic Segmentation
- FCN, DeepLabV3 Series, LR-ASPP, U-Net Series:
- These videos provide an understanding of network functionality and source code breakdown for those interested in practical implementation.
Instance Segmentation
- Mask R-CNN:
- Detailed network walkthrough and Pytorch code analysis are featured.
Key Point Detection
- DeepPose, HRNet:
- Videos include network explaining and Pytorch-based source code explorations.
Technical Requirements
For those looking to engage with the tutorials and implement the discussed networks, a technical setup including Anaconda3, Python 3.6/3.7/3.8, Pycharm (IDE), Pytorch 1.10, Torchvision 0.11.1, and Tensorflow 2.4.1 is recommended.
Additional Resources
Participants are encouraged to subscribe to the organizer's channel on Bilibili for further videos and insights, or follow their WeChat for summarized learning notes. For any queries, discussions are welcome on the organizer’s CSDN blog.
By providing a foundational understanding and step-by-step illustrations, this project is a valuable resource for anyone looking to deepen their knowledge and hands-on skills in deep learning for image processing.