Project Title
deep-learning-for-image-processing — Comprehensive Deep Learning Tutorials for Image Processing
Overview
The deep-learning-for-image-processing project is a comprehensive resource for learning deep learning techniques applied to image processing tasks such as classification and object detection. It stands out for its structured video tutorials and code examples in both PyTorch and TensorFlow, catering to a wide range of learners.
Key Features
- Extensive video tutorials covering various deep learning architectures for image processing
- Code examples in both PyTorch and TensorFlow
- Detailed explanations of network structures and their innovations
- PPT resources for further study
Use Cases
- Researchers and students learning deep learning for image processing
- Developers implementing image classification and object detection models in applications
- Educators looking for structured course materials on deep learning in image processing
Advantages
- Dual framework support (PyTorch and TensorFlow) for a broader audience
- In-depth video content for a better understanding of complex topics
- Practical code examples that can be directly applied or modified for various projects
Limitations / Considerations
- The project's effectiveness is highly dependent on the viewer's ability to follow video tutorials
- The tutorials may require a pre-existing understanding of deep learning and Python programming
- The project's license is unknown, which may affect its use in commercial applications
Similar / Related Projects
- TensorFlow Official Models: A collection of models for various tasks, including image processing, provided by TensorFlow. It differs in that it offers pre-trained models and is more focused on TensorFlow.
- PyTorch Image Models: A library of state-of-the-art models for image classification, provided by PyTorch. It is similar in providing code examples but is more focused on PyTorch and image classification.
- Keras Applications: A collection of pre-trained models for various deep learning tasks, including image processing, using Keras. It differs in that it provides pre-trained models rather than tutorials.
Basic Information
- GitHub: https://github.com/WZMIAOMIAO/deep-learning-for-image-processing
- Stars: 25,331
- License: Unknown
- Last Commit: 2025-08-20
📊 Project Information
- Project Name: deep-learning-for-image-processing
- GitHub URL: https://github.com/WZMIAOMIAO/deep-learning-for-image-processing
- Programming Language: Python
- ⭐ Stars: 25,331
- 🍴 Forks: 8,217
- 📅 Created: 2019-11-14
- 🔄 Last Updated: 2025-08-20
🏷️ Project Topics
Topics: [, ", b, i, l, i, b, i, l, i, ", ,, , ", c, l, a, s, s, i, f, i, c, a, t, i, o, n, ", ,, , ", d, e, e, p, -, l, e, a, r, n, i, n, g, ", ,, , ", o, b, j, e, c, t, -, d, e, t, e, c, t, i, o, n, ", ,, , ", p, y, t, o, r, c, h, ", ,, , ", s, e, g, m, e, n, t, a, t, i, o, n, ", ,, , ", t, e, n, s, o, r, f, l, o, w, 2, ", ]
🔗 Related Resource Links
🎥 Video Tutorials
- Pytorch官方demo(Lenet)
- Tensorflow2官方demo
- AlexNet网络讲解
- Pytorch搭建AlexNet
- Tensorflow2搭建Alexnet
- VggNet网络讲解
- Pytorch搭建VGG网络
- Tensorflow2搭建VGG网络
- GoogLeNet网络讲解
- Pytorch搭建GoogLeNet网络
- Tensorflow2搭建GoogLeNet网络
- ResNet网络讲解
- Pytorch搭建ResNet网络
- Tensorflow2搭建ResNet网络
- ResNeXt网络讲解
- Pytorch搭建ResNeXt网络
- MobileNet_V1_V2网络讲解
- Pytorch搭建MobileNetV2网络
- Tensorflow2搭建MobileNetV2网络
- MobileNet_V3网络讲解
- Pytorch搭建MobileNetV3网络
- Tensorflow2搭建MobileNetV3网络
- ShuffleNet_V1_V2网络讲解
- 使用Pytorch搭建ShuffleNetV2
- 使用Tensorflow2搭建ShuffleNetV2
- EfficientNet网络讲解
- 使用Pytorch搭建EfficientNet
- 使用Tensorflow2搭建EfficientNet
- EfficientNetV2网络讲解
- 使用Pytorch搭建EfficientNetV2
- 使用Tensorflow搭建EfficientNetV2
- RepVGG网络讲解
- Multi-Head Attention讲解
- Vision Transformer网络讲解
- 使用Pytorch搭建Vision Transformer
- 使用tensorflow2搭建Vision Transformer
- Swin Transformer网络讲解
- 使用Pytorch搭建Swin Transformer
- 使用Tensorflow2搭建Swin Transformer
- ConvNeXt网络讲解
- 使用Pytorch搭建ConvNeXt
- 使用Tensorflow2搭建ConvNeXt
- MobileViT网络讲解
- 使用Pytorch搭建MobileViT
- Faster-RCNN网络讲解
- FPN网络讲解
- Faster-RCNN源码解析(Pytorch)
- SSD网络讲解
- RetinaNet网络讲解
- SSD源码解析(Pytorch)
- YOLO系列网络讲解(V1~V3)
- YOLOv3 SPP源码解析(Pytorch版)
- YOLOV4网络讲解
- YOLOV5网络讲解
- YOLOX 网络讲解
- FCOS网络讲解
- FCN网络讲解
- FCN源码解析(Pytorch版)
This article is automatically generated by AI based on GitHub project information and README content analysis