mirror of https://github.com/JDAI-CV/fast-reid.git
37 lines
1.3 KiB
Markdown
37 lines
1.3 KiB
Markdown
# Getting Started with Fastreid
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## Prepare pretrained model
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If you use origin ResNet, you do not need to do anything. But if you want to use ResNet-ibn or ResNeSt, you need to download pretrain model in [here](https://drive.google.com/open?id=1thS2B8UOSBi_cJX6zRy6YYRwz_nVFI_S).
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And then you need to put it in `~/.cache/torch/checkpoints` or anywhere you like.
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Then you should set the pretrain model path in `configs/Base-bagtricks.yml`.
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## Compile with cython to accelerate evalution
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```bash
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cd fastreid/evaluation/rank_cylib; make all
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```
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## Training & Evaluation in Command Line
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We provide a script in "tools/train_net.py", that is made to train all the configs provided in fastreid.
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You may want to use it as a reference to write your own training script.
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To train a model with "train_net.py", first setup up the corresponding datasets following [datasets/README.md](https://github.com/JDAI-CV/fast-reid/tree/master/datasets), then run:
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```bash
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./tools/train_net.py --config-file ./configs/Market1501/bagtricks_R50.yml MODEL.DEVICE "cuda:0"
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```
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The configs are made for 1-GPU training.
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To evaluate a model's performance, use
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```bash
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./tools/train_net.py --config-file ./configs/Market1501/bagtricks_R50.yml --eval-only \
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MODEL.WEIGHTS /path/to/checkpoint_file MODEL.DEVICE "cuda:0"
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```
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For more options, see `./tools/train_net.py -h`.
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