mirror of https://github.com/JDAI-CV/fast-reid.git
32 lines
1.1 KiB
Markdown
32 lines
1.1 KiB
Markdown
# FastAttr in FastReID
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This project provides a strong baseline for pedestrian attribute recognition.
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## Datasets Preparation
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We use `PA100k` to evaluate the model's performance.
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You can do download dataset from [HydraPlus-Net](https://github.com/xh-liu/HydraPlus-Net).
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## Usage
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The training config file can be found in `projects/FastAttr/config`, which you can use to reproduce the results of the repo.
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For example
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```bash
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python3 projects/FastAttr/train_net.py --config-file projects/FastAttr/configs/pa100.yml --num-gpus 4
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```
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## Experiment Results
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We refer to [A Strong Baseline of Pedestrian Attribute Recognition](https://github.com/valencebond/Strong_Baseline_of_Pedestrian_Attribute_Recognition/tree/master) as our baseline methods and conduct the experiment
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with 4 GPUs.
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More details can be found in the config file and code.
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### PA100k
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| Method | Pretrained | mA | Accu | Prec | Recall | F1 |
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| :---: | :---: | :---: |:---: | :---: | :---: | :---: |
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| attribute baseline | ImageNet | 80.50 | 78.84 | 87.24 | 87.12 | 86.78 |
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| FastAttr | ImageNet | 77.57 | 78.03 | 88.39 | 84.98 | 86.65 |
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