mirror of https://github.com/JDAI-CV/fast-reid.git
37 lines
862 B
Markdown
37 lines
862 B
Markdown
# AGW Baseline in FastReID
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## Training
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To train a model, run
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```bash
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CUDA_VISIBLE_DEVICES=gpus python train_net.py --config-file <config.yaml>
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```
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For example, to launch a end-to-end baseline training on market1501 dataset with ibn-net on 4 GPUs,
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one should excute:
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```bash
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CUDA_VISIBLE_DEVICES=0,1,2,3 python train_net.py --config-file='configs/AGW_market1501.yml'
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```
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## Experimental Results
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### Market1501 dataset
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| Method | Pretrained | Rank@1 | mAP | mINP |
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| :---: | :---: | :---: |:---: | :---: |
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| AGW | ImageNet | 94.9% | 87.4% | 63.1% |
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### DukeMTMC dataset
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| Method | Pretrained | Rank@1 | mAP | mINP |
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| :---: | :---: | :---: |:---: | :---: |
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| AGW | ImageNet | 88.9% | 79.1% | 43.2% |
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### MSMT17 dataset
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| Method | Pretrained | Rank@1 | mAP | mINP |
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| :---: | :---: | :---: |:---: | :---: |
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| AGW | ImageNet | 75.6% | 52.6% | 11.9% |
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