Deep Learning-Pedestrian Recognition Practical Combat (2020)
Extraction code: gahr
The pedestrian re-identification course mainly includes three core modules:
1. Detailed interpretation of 2020 classic algorithm (paper);
2. Project source code analysis;
3. Practical application; popularly explain the latest pedestrian re-recognition algorithm and its implementation at conferences such as CVPR, and start actual combat based on the PyTorch framework, explaining all project source codes and their application examples line by line. The overall style is easy to understand, leading the students in the most down-to-earth way to master the latest pedestrian re-identification algorithm and conduct actual project combat.
The course consists of 10 chapters:
Chapter 1: Chapter 1: Principle and Application of Pedestrian Re-identification
Chapter 2: Chapter 2: Interpretation of ReId Model Papers Based on Attention Mechanism
Chapter 3: Chapter 3: Attention-based pedestrian re-identification project combat
Chapter 4: Computer Vision Top Meeting Algorithm (Feature Fusion) Explained
Chapter 5: Practical re-identification based on the fusion of local features of pedestrians
Chapter 6: Interpretation of the latest algorithm of Megvii Research Institute (based on graph model)
Chapter 7: Pedestrian Recognition Project Based on Topology Map Actual Combat
Chapter 8: Algorithm Supplement-Convolutional Neural Network
Chapter 9: Basic Supplement-PyTorch Framework Basic Processing Operations
Chapter 10: Basic Supplement-PyTorch Convolution Model Example