使用 Python 和 PyTorch 进行现代计算机视觉和深度学习
上次更新时间：2024-10-30
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课程内容
1. Introduction
2. What is Computer Vision & its Applications
3. Deep Convolutional Neural Networks (CNN) for Computer Vision
4. Setting-up Google Colab for Writing Python Code
5. Image Classification Task of Computer Vision
6. Pretrained Models for Single and Multi-Label Image Classification
- 1. Introduction to Pretrained Models
- 2. Deep Learning ResNet and AlexNet Architectures
- 3. Access Data from Google Drive to Colab
- 4. Data Preprocessing for Image Classification
- 5. Single-Label Image Classification using ResNet and AlexNet PreTrained Models
- 7. Multi-Label Image Classification using Deep Learning Models
7. Transfer Learning for Image Classification
8. Semantic Segmentation Task Of Computer Vision
9. Deep Learning Architectures For Segmentation (UNet, PSPNet, PAN)
10. Segmentation Datasets, Annotations, Data Augmentation & Data Loading
11. Performance Metrics (IOU) For Segmentation Models Evaluation
12. Encoders and Decoders For Segmentation In PyTorch
13. Implementation, Optimization and Training Of Segmentation Models
14. Test Models and Visualize Segmentation Results
15. Complete Code and Dataset for Semantic Segmentation
16. Object Detection Task Of Computer Vision
17. Deep Learning Architectures for Object Detection (R-CNN Family)
18. Detectron2 for Ojbect Detection
19. Training, Evaluating and Visualizing Object Detection on Custom Dataset
21. Instance Segmentation Task of Computer Vision
22. Mask RCNN for Instance Segmentation
23. Training, Evaluating and Visualizing Instance Segmentation on Custom Dataset
课程内容
22个章节 , 53个讲座
1. Introduction
2. What is Computer Vision & its Applications
3. Deep Convolutional Neural Networks (CNN) for Computer Vision
4. Setting-up Google Colab for Writing Python Code
5. Image Classification Task of Computer Vision
6. Pretrained Models for Single and Multi-Label Image Classification
- 1. Introduction to Pretrained Models
- 2. Deep Learning ResNet and AlexNet Architectures
- 3. Access Data from Google Drive to Colab
- 4. Data Preprocessing for Image Classification
- 5. Single-Label Image Classification using ResNet and AlexNet PreTrained Models
- 7. Multi-Label Image Classification using Deep Learning Models