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--- |
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license: mit |
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language: |
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- en |
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tags: |
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- person-reid |
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- deep-learning |
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- image-recognition |
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--- |
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This repo contains the pre-trained weights of OSNet, specialized for person recognition (i.e., person re-identification). |
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Related work: |
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- [Omni-Scale Feature Learning for Person Re-Identification](https://arxiv.org/abs/1905.00953) |
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- [Learning Generalisable Omni-Scale Representations for Person Re-Identification](https://arxiv.org/abs/1910.06827) |
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# Get started |
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Install the [Torchreid](https://github.com/KaiyangZhou/deep-person-reid) package. |
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```bash |
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# cd to your preferred directory and clone this repo |
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git clone https://github.com/KaiyangZhou/deep-person-reid.git |
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# create environment |
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cd deep-person-reid/ |
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conda create --name torchreid python=3.7 |
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conda activate torchreid |
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# install dependencies |
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# make sure `which python` and `which pip` point to the correct path |
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pip install -r requirements.txt |
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# install torch and torchvision (select the proper cuda version to suit your machine) |
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conda install pytorch torchvision cudatoolkit=9.0 -c pytorch |
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# install torchreid (don't need to re-build it if you modify the source code) |
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python setup.py develop |
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``` |
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Download OSNet models. |
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```python |
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from huggingface_hub import snapshot_download |
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# This will download the entire repo (containing all models) to cache_dir |
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snapshot_download(repo_id="kaiyangzhou/osnet", cache_dir="./") |
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``` |
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Use Torchreid as a feature extractor in your project. |
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```python |
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from torchreid.utils import FeatureExtractor |
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extractor = FeatureExtractor( |
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model_name='osnet_x1_0', |
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model_path='a/b/c/model.pth', |
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device='cuda' |
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) |
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image_list = [ |
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'a/b/c/image001.jpg', |
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'a/b/c/image002.jpg', |
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'a/b/c/image003.jpg', |
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'a/b/c/image004.jpg', |
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'a/b/c/image005.jpg' |
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] |
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features = extractor(image_list) |
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print(features.shape) # output (5, 512) |
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``` |
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# Model list |
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Models pre-trained on ImageNet are named in the following format: `osnet_x<scale>_imagenet.pth`. |
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Available re-id models: |
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- osnet_ain_x1_0_msmt17_256x128_amsgrad_ep50_lr0.0015_coslr_b64_fb10_softmax_labsmth_flip_jitter.pth |
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- osnet_ibn_x1_0_msmt17_combineall_256x128_amsgrad_ep150_stp60_lr0.0015_b64_fb10_softmax_labelsmooth_flip_jitter.pth |
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- osnet_x1_0_msmt17_combineall_256x128_amsgrad_ep150_stp60_lr0.0015_b64_fb10_softmax_labelsmooth_flip_jitter.pth |
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- osnet_x0_75_msmt17_combineall_256x128_amsgrad_ep150_stp60_lr0.0015_b64_fb10_softmax_labelsmooth_flip_jitter.pth |
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- osnet_x0_5_msmt17_combineall_256x128_amsgrad_ep150_stp60_lr0.0015_b64_fb10_softmax_labelsmooth_flip_jitter.pth |
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- osnet_x0_25_msmt17_combineall_256x128_amsgrad_ep150_stp60_lr0.0015_b64_fb10_softmax_labelsmooth_flip_jitter.pth |
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`ain` and `ibn` models are more generalizable. Please refer to https://arxiv.org/abs/1910.06827. |
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`osnet_x1_0`, `osnet_x0_75`, `osnet_x0_5`, and `osnet_x0_25` are OSNet models of different sizes. Please refer to https://arxiv.org/abs/1905.00953. |
