This is a PyTorch implementation of Peiyun Hu's awesome tiny face detector.
We use (and recommend) Python 3.6+ for minimal pain when using this codebase (plus Python 3.6 has really cool features).
NOTE Be sure to cite Peiyun's CVPR paper and this repo if you use this code!
This code gives the following mAP results on the WIDER Face dataset:
| Setting | mAP |
|---|---|
| easy | 0.902 |
| medium | 0.892 |
| hard | 0.797 |
- Clone this repository.
- Download the WIDER Face dataset and annotations files to
data/WIDER. - Install dependencies with
pip install -r requirements.txt.
Your data directory should look like this for WIDERFace
- data
- WIDER
- README.md
- wider_face_split
- WIDER_train
- WIDER_val
- WIDER_test
You can find the pretrained weights which get the above mAP results here.
Just type make at the repo root and you should be good to go!
In case you wish to change some settings (such as data location), you can modify the Makefile which should be super easy to work with.
To run evaluation and generate the output files as per the WIDERFace specification, simply run make evaluate. The results will be stored in the val_results directory.
You can then use the dataset's eval_tools to generate the mAP numbers (this needs Matlab/Octave).
Similarly, to run the model on the test set, run make test to generate results in the test_results directory.
To run the model on your own image, please use the detect_image.py script.
You may have to adjust the probability and NMS thresholds to get the best results.