Online Demo Page https://issai.nu.edu.kz/anyface/
Clone the repository and install all necessary packages:
git clone https://github.com/IS2AI/AnyFace.git
cd AnyFace
pip install -r requirements.txtDownload them from the following links and rename their folders accordingly.
| Dataset | Link | Folder Name |
|---|---|---|
| Wider Face | http://shuoyang1213.me/WIDERFACE/ | widerface |
| AnimalWeb | https://fdmaproject.wordpress.com/author/fdmaproject/ | animalweb |
| iCartoonFace | https://github.com/luxiangju-PersonAI/iCartoonFace#dataset | icartoon |
| TFW | https://github.com/IS2AI/TFW#downloading-the-dataset | tfw |
The following datasets were used to test the best model in addition to the test set of the main dataset. You should download them from the following links into the original-external/ directory. Then, rename the folders as shown in the table.
| Dataset | Link | Folder Name |
|---|---|---|
| Oxford-IIIT Pet Dataset | https://www.robots.ox.ac.uk/vgg/data/pets/ | pets |
| CUB-200-2011 | https://www.kaggle.com/datasets/veeralakrishna/200-bird-species-with-11788-images | birds |
| Artistic-Faces | https://faculty.runi.ac.il/arik/site/foa/artistic-faces-dataset.asp | artisticfaces |
| MetFaces | https://github.com/NVlabs/metfaces-dataset | metfaces |
| Sea Turtle Faces | https://www.kaggle.com/datasets/smaranjitghose/sea-turtle-face-detection | turtles |
| Anime-Face-Detector | https://github.com/qhgz2013/anime-face-detector | animefaces |
| Tom & Jerry's Faces | https://www.kaggle.com/datasets/boltuzamaki/tom-and-jerrys-face-detection-dateset | tj |
| Labeled Fishes in the Wild Dataset | https://swfscdata.nmfs.noaa.gov/labeled-fishes-in-the-wild/ | fishes |
Use notebooks in the main directory to pre-process the corresponding datasets.
The preprocessed datasets are saved in dataset/ directory. For each dataset, images are stored in dataset/<dataset_name>/images/ and the corresponding labels are stored in dataset/dataset_name/labels/ and in dataset/<dataset_name>/labels_eval/. Labels are saved in .txt files, where each .txt file has the same filename as corresponding image. Two labeling formats are used:
Annotations in dataset/<dataset_name>/labels/ follow the format used for training YOLOv5Face models:
face x_center y_center width height x1 y1 x2 y2 x3 y3 x4 y4 x5 y5x1,y1,...,x5,y5correspond to the coordinates of the left eye, right eye, nose top, left mouth corner, and right mouth corner- all coordinates are normalized to values [0-1]
- for faces without annotated landmarks, -1 is used instead of coordinates
Annotations in dataset/dataset_name/labels_eval/ follow the format used for Object-Detection-Metrics:
- each row contains labels of one face
- the format:
face x_left y_top width height - the coordinates are NOT normalized.
To make a random selection of 160 images from each external dataset, run the cells in external_selection.ipynb. Selected images for each dataset are in selected-external/<dataset_name>/ directory.
Run the following command to train the YOLOv5Face model on WIDER Face, AnimalWeb, iCartoonFace and TFW datasets. The paths to these datasets are specified in the yolov5-face/data/agnostic.yaml file. The model type is selected by --cfg models/yolov5l6.yaml. The weights are randomly initialized --weights ''. However, pre-trained weights can be also used by providing an appropriate path. We used default hyperparameters inside yolov5-face/data/hyp.scratch.yaml. They can be changed by providing a path to --hyp argument. The full list of arguments is in yolov5-face/train.py.
cd yolov5-face
CUDA_VISIBLE_DEVICES="0,1" python train.py --data data/agnostic.yaml --cfg models/yolov5l6.yaml --weights '' --workers 32 --name 'exp1' --batch-size 128 --epochs 350The trained model is saved in yolov5-face/runs/train/<exp_name>/weights/ directory.
For more information about training details, please refer to the YOLOv5 and YOLOv5-face repository.
To get the predictions on the validation and test sets of the main datasets, run the yolov5-face/evaluate.ipynb notebook. For the external datasets, use yolov5-face/evaluate-external.ipynb. These notebooks create detection .txt files in Object-Detection-Metrics format: face confidence_score x_left y_top width height. (The coordinates are not normalized)
The detections are organized as follows:
yolov5-face
└─── Object-Detection-Metrics
│ └─── results
│ │ └─── val
│ │ └─── test
│ │ └─── external
│ │ │ └─── <experiment_name>
│ │ │ │ └─── <dataset_name>
│ │ │ │ │ └─── detections_{IoU}_{conf}
│ │ │ │ │ │ └─── <image_name>.txt
To compute average precision, precision, and recall for each dataset, use the results.ipynb and results-external.ipynb notebooks in the Object-Detection-Metrics directory.
For the WIDER Face dataset, use yolov5-face/evaluate-widerface.ipynb that stores the predictions in WIDER Face submission format:
< image name >
< number of faces in this image >
< x_left y_top width height >
Please note that for the evaluation, the dataset images need to be copied to correspondingyolov5-face/widerface/<set>/images directory.
Then, use a matlab script from the website of WIDER Face to compute average precisions for the validation set. For more information refer to the WIDER Face dataset website. For more information refer to the WIDER Face dataset website.
To evaluate the model on the external datasets, yolov5-face/evaluate-selected-external.ipynb notebook saves the selected images with drawn predicted bounding boxes and confidence scores. The predictions are saved in yolov5-face/selected-external-results/.
A similar notebook is provided for arbitrary test images. The predictions are saved in yolov5-face/test-images-results. We also provide yolov5-face/evaluate-test-videos.ipynb which extracts frames from the input video, makes predictions on the frames and constructs video back with bounding boxes. Testing images and testing videos should be put into /test_images/ and /test_videos/ in the main directory.
Please note that we utilize augmentation procedure at the inference to get better results. Thus, you can find augment argument in evaluation code. By default, augmentation is not performed when the argument is not specified or None. augment=1 sets s = [1, 0.83, 0.67] for scaling and f = [None, 3, None] for flipping, where 3 is horizontal flipping. augment=2 sets s = [1, 0.83, 0.67, 1, 0.83, 0.67] for scaling and f = [None, None, None, 3, 3, 3] for flipping, meaning that for all three image scales we infer both original and flipped image. To change these parameters refer to yolov5-face/models/yolo.py.
To visualize CNN layers of the model for a given input image, you can use feature_vis parameter in yolov5-face/evaluate-selected-external.ipynb. By default feature maps of the C3 block are visualized. This can be changed in yolov5-face/models/yolo.py. Feature maps are saved to yolov5-face/feature_visualization/.
Download the most accurate model, YOLOv5l6, from Google Drive and save it in yolov5-face/weights directory. Then, run yolov5-face/detect_face.py to detect faces and facial landmarks on an image by providing image size, the path to weights and image.
python detect_face.py --weights weights/yolov5l6_best.pt --source image_path --img-size 800@INPROCEEDINGS{10066796,
author={Kuzdeuov, Askat and Koishigarina, Darina and Varol, Huseyin Atakan},
booktitle={2023 IEEE International Conference on Big Data and Smart Computing (BigComp)},
title={AnyFace: A Data-Centric Approach For Input-Agnostic Face Detection},
year={2023},
volume={},
number={},
pages={211-218},
doi={10.1109/BigComp57234.2023.00042}}