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