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
|
||
|
|
|
||
|
|
This project provides a strong baseline for pedestrian attribute recognition.
|
||
|
|
|
||
|
|
## Datasets Preparation
|
||
|
|
|
||
|
|
We use `PA100k` to evaluate the model's performance.
|
||
|
|
You can do download dataset from [HydraPlus-Net](https://github.com/xh-liu/HydraPlus-Net).
|
||
|
|
|
||
|
|
## Usage
|
||
|
|
|
||
|
|
The training config file can be found in `projects/FastAttr/config`, which you can use to reproduce the results of the repo.
|
||
|
|
|
||
|
|
For example
|
||
|
|
|
||
|
|
```bash
|
||
|
|
python3 projects/FastAttr/train_net.py --config-file projects/FastAttr/configs/pa100.yml --num-gpus 4
|
||
|
|
```
|
||
|
|
|
||
|
|
## Experiment Results
|
||
|
|
|
||
|
|
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
|
||
|
|
with 4 GPUs.
|
||
|
|
More details can be found in the config file and code.
|
||
|
|
|
||
|
|
### PA100k
|
||
|
|
|
||
|
|
| Method | Pretrained | mA | Accu | Prec | Recall | F1 |
|
||
|
|
| :---: | :---: | :---: |:---: | :---: | :---: | :---: |
|
||
|
|
| attribute baseline | ImageNet | 80.50 | 78.84 | 87.24 | 87.12 | 86.78 |
|
||
|
|
| FastAttr | ImageNet | 77.57 | 78.03 | 88.39 | 84.98 | 86.65 |
|