#multi-GPU training

Torchreid is an advanced library designed for deep-learning person re-identification, supporting multi-GPU use and both image and video analysis. It facilitates complete training, cross-dataset evaluation, and straightforward dataset setup while implementing leading-edge models. The library is highly adaptable, permitting easy integration of new models, datasets, and techniques, and includes pre-trained models and innovative training methods. Torchreid additionally offers support for ONNX, OpenVINO, and TFLite for model exportation, enhancing adaptability across diverse platforms. It serves as a valuable tool for researchers and developers focused on efficient person re-identification solutions.

Explore semantic segmentation with this project using the Unicom model pre-trained on 400 million images. Achieve efficient results with just four training images. Start quickly with 'train_one_gpu.py' or optimize performance with 'train_multi_gpus.py' for multi-GPU support. Follow setup guides for seamless configuration. Connect via QQ group 679897018 for discussions.

This framework uses leading 2D text-to-image models to innovate 3D content creation from text prompts and images, seamlessly integrating existing setups for high-quality rendering. Discover its main features, advantages, and recent updates for diverse applications, ideal for developers in 3D animation and modeling.