Awesome

tiny-faces-pytorch

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

Getting Started

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

Pretrained Weights

You can find the pretrained weights which get the above mAP results here.

Training

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.

Evaluation

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.

Deployment

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.