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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.
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.
<img src="./figures/Comparison-Results.png" alt="Comparison-Results"/>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
This code is based on DSFD. Thanks a lot for the great work!