mirror of https://github.com/JDAI-CV/fast-reid.git
91 lines
3.3 KiB
Markdown
91 lines
3.3 KiB
Markdown
# ReID_baseline
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A strong baseline (state-of-the-art) for person re-identification.
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We support
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- [x] easy dataset preparation
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- [x] end-to-end training and evaluation
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- [x] multi-GPU distributed training
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- [x] fast data loader with prefetcher
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- [ ] fast training speed with fp16
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- [x] fast evaluation with cython
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- [ ] support both image and video reid
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- [x] multi-dataset training
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- [x] cross-dataset evaluation
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- [x] high modular management
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- [x] state-of-the-art performance with simple model
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- [x] high efficient backbone
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- [x] advanced training techniques
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- [x] various loss functions
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- [x] tensorboard visualization
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## Get Started
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The designed architecture follows this guide [PyTorch-Project-Template](https://github.com/L1aoXingyu/PyTorch-Project-Template), you can check each folder's purpose by yourself.
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1. `cd` to folder where you want to download this repo
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2. Run `git clone https://github.com/L1aoXingyu/reid_baseline.git`
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3. Install dependencies:
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- [pytorch 1.0.0+](https://pytorch.org/)
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- torchvision
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- tensorboard
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- [yacs](https://github.com/rbgirshick/yacs)
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4. Prepare dataset
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Create a directory to store reid datasets under this repo via
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```bash
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cd reid_baseline
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mkdir datasets
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```
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1. Download dataset to `datasets/` from [baidu pan](https://pan.baidu.com/s/1ntIi2Op) or [google driver](https://drive.google.com/file/d/0B8-rUzbwVRk0c054eEozWG9COHM/view)
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2. Extract dataset. The dataset structure would like:
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```bash
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datasets
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Market-1501-v15.09.15
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bounding_box_test/
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bounding_box_train/
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```
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5. 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, you need to download pretrain model in [here](https://drive.google.com/open?id=1thS2B8UOSBi_cJX6zRy6YYRwz_nVFI_S). And then you can put it in `~/.cache/torch/checkpoints` or anywhere you like.
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Then you should set this pretrain model path in `configs/softmax_triplet.yml`.
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6. compile with cython to accelerate evalution
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```bash
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cd csrc/eval_cylib; make
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```
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## Train
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Most of the configuration files that we provide, you can run this command for training market1501
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```bash
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bash scripts/train_openset.sh
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```
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Or you can just run code below to modify your cfg parameters
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```bash
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CUDA_VISIBLE_DEVICES='0,1' python tools/train.py -cfg='configs/softmax_triplet.yml' DATASETS.NAMES '("dukemtmc","market1501",)' SOLVER.IMS_PER_BATCH '256'
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```
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## Test
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You can test your model's performance directly by running this command
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```bash
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CUDA_VISIBLE_DEVICES='0' python tools/test.py -cfg='configs/softmax_triplet.yml' DATASET.TEST_NAMES 'dukemtmc' \
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MODEL.BACKBONE 'resnet50' \
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MODEL.WITH_IBN 'True' \
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TEST.WEIGHT '/save/trained_model/path'
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```
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## Experiment Results
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| size=(256, 128) batch_size=64 (16 id x 4 imgs) | | | | |
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| :------: | :-----: | :-----: | :--: | :---: |
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| softmax? | ✔︎ | ✔︎ | ✔︎ | ✔︎ |
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| label smooth? | | | ✔︎ | ✔︎ |
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| triplet? | | ✔︎ | ✔︎ | ✔︎ |
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| ibn? | | | ✔︎ | ✔︎ |
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| gcnet? | | | | ✔︎ |
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| Market1501 | 93.4 (82.9) | 94.2 (86.1) | 95.4 (87.9) | 95.2 (88.7) |
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| DukeMTMC-reid | 84.7 (72.7) | 87.3 (76.0) | 89.5 (79.7) | 90.0 (80.2) |
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🔥Any other tricks are welcomed！
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