2021-01-18 11:36:38 +08:00
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# FastDistill in FastReID
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This project provides a strong distillation method for both embedding and classification training.
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The feature distillation comes from [overhaul-distillation](https://github.com/clovaai/overhaul-distillation/tree/master/ImageNet).
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## Datasets Prepration
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- DukeMTMC-reID
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## Train and Evaluation
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```shell
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# teacher model training
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python3 projects/FastDistill/train_net.py \
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--config-file projects/FastDistill/configs/sbs_r101ibn.yml \
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--num-gpus 4
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# loss distillation
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python3 projects/FastDistill/train_net.py \
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--config-file projects/FastDistill/configs/kd-sbs_r101ibn-sbs_r34.yaml \
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--num-gpus 4 \
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MODEL.META_ARCHITECTURE Distiller
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2021-01-30 11:52:47 +08:00
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KD.MODEL_CONFIG '("projects/FastDistill/logs/dukemtmc/r101_ibn/config.yaml",)' \
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KD.MODEL_WEIGHTS '("projects/FastDistill/logs/dukemtmc/r101_ibn/model_best.pth",)'
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2021-01-18 11:36:38 +08:00
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# loss+overhaul distillation
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python3 projects/FastDistill/train_net.py \
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--config-file projects/FastDistill/configs/kd-sbs_r101ibn-sbs_r34.yaml \
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--num-gpus 4 \
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MODEL.META_ARCHITECTURE DistillerOverhaul
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2021-01-30 11:52:47 +08:00
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KD.MODEL_CONFIG '("projects/FastDistill/logs/dukemtmc/r101_ibn/config.yaml",)' \
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KD.MODEL_WEIGHTS '("projects/FastDistill/logs/dukemtmc/r101_ibn/model_best.pth",)'
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2021-01-18 11:36:38 +08:00
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```
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## Experimental Results
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### Settings
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All the experiments are conducted with 4 V100 GPUs.
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### DukeMTMC-reID
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| Model | Rank@1 | mAP |
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| --- | --- | --- |
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| R101_ibn (teacher) | 90.66 | 81.14 |
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| R34 (student) | 86.31 | 73.28 |
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| JS Div | 88.60 | 77.80 |
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| JS Div + Overhaul | 88.73 | 78.25 |
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## Contact
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This project is conducted by [Xingyu Liao](https://github.com/L1aoXingyu) and [Guan'an Wang](https://wangguanan.github.io/) (guan.wang0706@gmail).
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