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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Torchreid: Deep learning person re-identification in PyTorch.
| Date | Stars |
|---|---|
| 2026-07-24 | 4879 |
| 2026-07-25 | 4879 |
| 2026-07-28 | 4882 |
| 2026-07-30 | 4882 |
| 2026-07-31 | 4885 |
| 2026-08-06 | 4887 |
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Torchreid =========== Torchreid is a library for deep-learning person re-identification, written in `PyTorch <https://pytorch.org/>`_ and developed for our ICCV'19 project, `Omni-Scale Feature Learning for Person Re-Identification <https://arxiv.org/abs/1905.00953>`_. It features: - multi-GPU training - support both image- and video-reid - end-to-end training and evaluation - incredibly easy preparation of reid datasets - multi-dataset training - cross-dataset evaluation - standard protocol used by most research papers - highly extensible (easy to add models, datasets, training methods, etc.) - implementations of state-of-the-art deep reid models - access to pretrained reid models - advanced training techniques - visualization tools (tensorboard, ranks, etc.) Code: https://github.com/KaiyangZhou/deep-person-reid. Documentation: https://kaiyangzhou.github.io/deep-person-reid/. How-to instructions: https://kaiyangzhou.github.io/deep-person-reid/user_guide. Model zoo: https://kaiyangzhou.github.io/deep-person-reid/MODEL_ZOO. Tech report: https://arxiv.org/abs/1910.10093. You can find some research projects that are built on top of Torchreid `here <https://github.com/KaiyangZhou/deep-person-reid/tree/master/projects>`_. The model weights are also available on Huggingface: https://huggingface.co/kaiyangzhou/osnet. What's new ------------ - [Aug 2022] We have added model export capabilities to the following frameworks: ONNX, OpenVINO and TFLite. The export script can be found `here <https://github.com/KaiyangZhou/deep-person-reid/blob/master/tools/export.py>`_ - [Aug 2021] We have released the ImageNet-pretrained models of ``osnet_ain_x0_75``, ``osnet_ain_x0_5`` and ``osnet_ain_x0_25``. The pretraining setup follows `pycls <https://github.com/facebookresearch/pycls/blob/master/configs/archive/imagenet/resnet/R-50-1x64d_step_8gpu.yaml>`_. - [Apr 2021] We have updated the appendix in the `TPAMI version of OSNet <https://arxiv.org/abs/1910.06827v5>`_ to include results in the multi-source domain generalization setting. The trained models can be found in the `Model Zoo <https://kaiyangzhou.github.io/deep-person-reid/MODEL_ZOO.html>`_. - [Apr 2021] We have added a script to automate the process of calculating average results over multiple splits. For more details please see ``tools/parse_test_res.py``. - [Apr 2021] ``v1.4.0``: We added the person search dataset, `CUHK-SYSU <http://www.ee.cuhk.edu.hk/~xgwang/PS/dataset.html>`_. Please see the `documentation <https://kaiyangzhou.github.io/deep-person-reid/>`_ regarding how to download the dataset (it contains cropped person images). - [Apr 2021] All models in the model zoo have been moved to google drive. Please raise an issue if any model's performance is inconsistent with the numbers shown in the model zoo page (could be caused by wrong links). - [Mar 2021] `OSNet <https://arxiv.org/abs/1910.06827>`_ will appear in the TPAMI journal! Compared with the conference version, which focuses on discriminative feature learning using the omni-scale building block, this journal extension further considers generalizable feature learning by integrating `instance normalization layers <https://arxiv.org/abs/1607.08022>`_ with the OSNet architecture. We hope this journal paper can motivate more future work to taclke the generalization issue in cross-dataset re-ID. - [Mar 2021] Generalization across domains (datasets) in person re-ID is crucial in real-world applications, which is closely related to the topic of *domain generalization*. Interested in learning how the field of domain generalization has developed over the last decade? Check our recent survey in this topic at https://arxiv.org/abs/2103.02503, with coverage on the history, datasets, related problems, methodologies, potential directions, and so on (*methods designed for generalizable re-ID are also covered*!). - [Feb 2021] ``v1.3.6`` Added `University-1652 <https://dl.acm.org/doi/abs/10.1145/3394171.3413896>`_, a new dataset for
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:f7ad833e9c0ebc35, topic:deep-learning, topic:pytorch, readme:pretraining
matched fp:f7ad833e9c0ebc35, topic:computer-vision