2 dépôts
Awesome GitHub RepositoriesAppearance-Based Refinements
Techniques that use visual feature extraction to improve the consistency of object identity during tracking.
Distinct from Object Tracking Systems: Focuses specifically on using neural network feature vectors for identity refinement, whereas the parent covers general tracking systems.
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Awesome Appearance-Based Refinements GitHub Repositories
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- tingsongyu/pytorch_tutorial
TingsongYu/PyTorch_Tutorial
8,018Voir sur GitHubThis project is a comprehensive collection of educational examples and reference implementations for building vision and language models using PyTorch. It serves as a deep learning tutorial covering the end-to-end process of developing neural networks, from initial architecture definition to final production deployment. The repository provides detailed guides on implementing a wide range of domain-specific models, including convolutional neural networks for object detection and segmentation, as well as transformer and recurrent architectures for natural language processing. It emphasizes gene
Provides neural network-based feature extraction to reduce identity switching during object association in video streams.
Python - tingsongyu/pytorch-tutorial-2nd
TingsongYu/PyTorch-Tutorial-2nd
4,555Voir sur GitHubThis project is a comprehensive instructional resource and course for building neural networks using PyTorch. It covers the fundamental building blocks of deep learning, including tensor manipulation, automatic differentiation, and the construction of modular neural network components. The repository serves as a technical guide for several specialized domains. It provides implementation details for computer vision tasks such as image classification, object detection, and semantic segmentation, as well as natural language processing workflows involving transformers, recurrent networks, and gen
Matches tracks to detections using visual feature vectors and distance metrics to maintain identity.
Jupyter Notebookcomputer-visiondeepsortdiffusion-models
