It could be confusing, but the model in this library perform classifications of the images. It takes image as an input and outputs probability of person in the image wearing a mask.
Hence in order to get expected results the model should be combined with face detector, for example from https://github.com/ternaus/retinaface.
Example on how to combine face detector with mask detector
WebApp with face detector and mask classifier
pip install -U facemask_detection
import albumentations as A
import torch
from facemask_detection.pre_trained_models import get_model
model = get_model("tf_efficientnet_b0_ns_2020-07-29")
model.eval()
transform = A.Compose([A.SmallestMaxSize(max_size=256, p=1),
A.CenterCrop(height=224, width=224, p=1),
A.Normalize(p=1)])image = <numpy array with the shape (height, width, 3)>
transformed_image = transform(image=image)['image']
input = torch.from_numpy(np.transpose(transformed_image, (2, 0, 1))).unsqueeze(0)
print("Probability of the mask on the face = ", model(input)[0].item())- Jupyter notebook with the example:
- Jupyter notebook with the example on how to combine face detector with mask detector:
Train dataset was composed from the data:
- https://www.kaggle.com/andrewmvd/face-mask-detection
- https://www.kaggle.com/alexandralorenzo/maskdetection
- https://github.com/X-zhangyang/Real-World-Masked-Face-Dataset
- https://humansintheloop.org/medical-mask-dataset
Define config, similar to facemask_detection_configs/2020-07-29.yaml.
Run
python facemask_detection/train.py -c <config>Inference
python -m torch.distributed.launch --nproc_per_node=1 facemask_detection/inference.py -h
usage: inference.py [-h] -i INPUT_PATH -c CONFIG_PATH -o OUTPUT_PATH
[-b BATCH_SIZE] [-j NUM_WORKERS] -w WEIGHT_PATH
[--world_size WORLD_SIZE] [--local_rank LOCAL_RANK]
[--fp16]
optional arguments:
-h, --help show this help message and exit
-i INPUT_PATH, --input_path INPUT_PATH
Path with images.
-c CONFIG_PATH, --config_path CONFIG_PATH
Path to config.
-o OUTPUT_PATH, --output_path OUTPUT_PATH
Path to save jsons.
-b BATCH_SIZE, --batch_size BATCH_SIZE
batch_size
-j NUM_WORKERS, --num_workers NUM_WORKERS
num_workers
-w WEIGHT_PATH, --weight_path WEIGHT_PATH
Path to weights.
--world_size WORLD_SIZE
number of nodes for distributed training
--local_rank LOCAL_RANK
node rank for distributed training
--fp16 Use fp6Example:
python -m torch.distributed.launch --nproc_per_node=<num_gpu> facemask_detection/inference.py \
-i <input_path> \
-w <path to weights> \
-o <path to the output_csv> \
-c <path to config>
-b <batch size>
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent