HLA-Face: Joint High-Low Adaptation for Low Light Face Detection

The official PyTorch implementation for HLA-Face: Joint High-Low Adaptation for Low Light Face Detection (CVPR21).

You can find more information on our project website.

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1. Description

Our face detection mAP on DARK FACE test set (trained without DARK FACE labels): 44.4

Precision-Recall (PR) curves on DARK FACE.

2. Training code

Please follow [Training Code] to download and prepare the dataset.

Basically, to train the final Joint High-Low Adaptation framework

cd ./train_code
python train.py --multigpu

More details are given in [Training Code].

3. Testing code

First, download and cd ./test_code.

3.1 Requirements

• Python 3

• PyTorch 1.2.0

  • Higher versions may cause the RuntimeError: Legacy autograd function with non-static forward method is deprecated. Please use new-style autograd function with static forward method bug.

  • According to https://github.com/yxlijun/DSFD.pytorch, PyTorch 0.3 is also OK, but we have not checked it.

• opencv

• numpy

• easydict

3.2 Demo

Download the checkpoints from [Google] [Baidu (xnb6)], and save them as:

./final_weights/Illumination-Enhancer.pth
./final_weights/Face-Detector.pth

This script detects the example test.png image.

python test.py

The detection result (a text file) can be found in ./result/ and visualized by draw_result.py

Here, the left image is test.png, and the right image is the visualized face detection result.

3.3 Test more images

You can further edit the function load_images() in test.py to load your testing images. The detection results can be found in ./result where each detection result is named by the name of the testing image.

For example, to test the DSFD test dataset:

def load_images():
    import glob
    return glob.glob('./YOUR_PATH_TO_DARKFACE/images/test/*.png')

If you want to run without the multi-scale testing scheme (the mode we used in ablation studies), set:

USE_MULTI_SCALE = False
MY_SHRINK = 2

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This code is based on DSFD. Thanks a lot for the great work!