mirror of https://github.com/JDAI-CV/fast-reid.git
40 lines
1.0 KiB
Markdown
40 lines
1.0 KiB
Markdown
# Strong 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/baseline_ibn_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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| BagTricks | ImageNet | 93.6% | 85.1% | 58.1% |
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| BagTricks + Ibn-a | ImageNet | 94.8% | 87.3% | 63.5% |
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### DukeMTMC dataset
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| Method | Pretrained | Rank@1 | mAP | mINP |
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| :---: | :---: | :---: |:---: | :---: |
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| BagTricks | ImageNet | 86.1% | 75.9% | 38.7% |
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| BagTricks + Ibn-a | ImageNet | 89.0% | 78.8% | 43.6% |
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### MSMT17 dataset
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| Method | Pretrained | Rank@1 | mAP | mINP |
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| :---: | :---: | :---: |:---: | :---: |
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| BagTricks | ImageNet | 70.4% | 47.5% | 9.6% |
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| BagTricks + Ibn-a | ImageNet | 76.9% | 55.0% | 13.5% |
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