113 lines
4.5 KiB
Markdown
113 lines
4.5 KiB
Markdown
# Bags of Tricks and A Strong ReID Baseline
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Paper:
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The codes are expanded on a [ReID-baseline](https://github.com/L1aoXingyu/reid_baseline) , which is open sourced by our co-author Xingyu Liao.
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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] high modular management
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Bag of tricks
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- Warm up learning rate
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- Random erasing augmentation
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- Label smoothing
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- Last stride
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- BNNeck
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- Center loss
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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... `
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3. Install dependencies:
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- [pytorch 1.0](https://pytorch.org/)
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- torchvision
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- [ignite](https://github.com/pytorch/ignite)
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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 data
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```
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（1）Market1501
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* Download dataset to `data/` from http://www.liangzheng.org/Project/project_reid.html
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* Extract dataset and rename to `market1501`. The data structure would like:
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```bash
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data
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market1501 # this folder contains 6 files.
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bounding_box_test/
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bounding_box_train/
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......
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```
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（2）DukeMTMC-reID
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* Download dataset to `data/` from https://github.com/layumi/DukeMTMC-reID_evaluation#download-dataset
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* Extract dataset and rename to `dukemtmc-reid`. The data structure would like:
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```bash
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data
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dukemtmc-reid
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DukeMTMC-reID # this folder contains 8 files.
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bounding_box_test/
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bounding_box_train/
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......
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```
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5. Prepare pretrained model if you don't have
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```python
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from torchvision import models
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models.resnet50(pretrained=True)
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```
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Then it will automatically download model in `~/.torch/models/`, you should set this path in `config/defaults.py` for all training or set in every single training config file in `configs/`.
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6. If you want to know the detained configurations and their meaning, please refer to `config/defaults.py`. If you want to set your own parameters, you can follow our method: create a new yml file, then set your own parameters. Add `--config_file='configs/your yml file'` int the commands described below, then our code will merge your configuration. automatically.
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## Train
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You can run these commands in `.sh ` files for training different datasets of differernt loss. You can also directly run code `sh *.sh` to run our demo.
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1. Market1501, cross entropy loss + triplet loss
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```bash
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python3 tools/train.py --config_file='configs/softmax_triplet.yml' MODEL.DEVICE_ID "('your device id')" DATASETS.NAMES "('market1501')" OUTPUT_DIR "('your path to save checkpoints and logs')"
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```
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2. DukeMTMC-reID, cross entropy loss + triplet loss + center loss
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```bash
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python3 tools/train.py --config_file='configs/softmax_triplet_with_center.yml' MODEL.DEVICE_ID "('your device id')" DATASETS.NAMES "('dukemtmc')" OUTPUT_DIR "('your path to save checkpoints and logs')"
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```
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## Test
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You can test your model's performance directly by running these commands in `.sh ` files. You can also change the configuration to determine which feature of BNNeck and whether the feature is normalized (equivalent to use Cosine distance or Euclidean distance) for testing.
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1. Test with Euclidean distance using feature before BN without re-ranking,.
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```bash
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python3 tools/test.py --config_file='configs/softmax_triplet_with_center.yml' MODEL.DEVICE_ID "('your device id')" DATASETS.NAMES "('market1501')" TEST.NECK_FEAT "('before')" TEST.FEAT_NORM "('no')" TEST.WEIGHT "('your path to trained checkpoints')"
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```
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2. Test with Cosine distance using feature after BN without re-ranking,.
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```bash
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python3 tools/test.py --config_file='configs/softmax_triplet_with_center.yml' MODEL.DEVICE_ID "('your device id')" DATASETS.NAMES "('market1501')" TEST.NECK_FEAT "('after')" TEST.FEAT_NORM "('yes')" TEST.WEIGHT "('your path to trained checkpoints')"
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```
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3. Test with Cosine distance using feature after BN with re-ranking
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```bash
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python3 tools/test.py --config_file='configs/softmax_triplet_with_center.yml' MODEL.DEVICE_ID "('your device id')" DATASETS.NAMES "('dukemtmc')" TEST.NECK_FEAT "('after')" TEST.FEAT_NORM "('yes')" TEST.RE_RANKING "('yes')" TEST.WEIGHT "('your path to trained checkpoints')"
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```
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## Results
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**Network architecture**
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