Strong 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/baseline_ibn_market1501.yml'
Experimental Results
Market1501 dataset
| Method |
Pretrained |
Rank@1 |
mAP |
mINP |
| BagTricks |
ImageNet |
93.6% |
85.1% |
58.1% |
| BagTricks + Ibn-a |
ImageNet |
94.8% |
87.3% |
63.5% |
DukeMTMC dataset
| Method |
Pretrained |
Rank@1 |
mAP |
mINP |
| BagTricks |
ImageNet |
86.1% |
75.9% |
38.7% |
| BagTricks + Ibn-a |
ImageNet |
89.0% |
78.8% |
43.6% |
MSMT17 dataset
| Method |
Pretrained |
Rank@1 |
mAP |
mINP |
| BagTricks |
ImageNet |
70.4% |
47.5% |
9.6% |
| BagTricks + Ibn-a |
ImageNet |
76.9% |
55.0% |
13.5% |
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