pip install -r requirements.txt
In eval.sh uncomment which version to use.
3 flags:
- model_version: which model version to use.
- dataset_name: this is the name of dataset, default custom.
- dataset_folder: path of the folder where all images are there.
(Note: dataset_folder here is the folder where images are stored inside given classes name)
output will be stored here: "./evaluation_results/{model_version}/{dataset_name}/"
If want to test on 105 and LFW, download the data from link and put it inside ./Data/ Folder.
docker build -t face_clust .
docker run -v ./evaluation_results/:/app/evaluation_results/ face_clust
Dataset Index | Dataset | Number of Identities | Total Number of Images |
---|---|---|---|
1. | 105_classes_pins_dataset_train | 59 | 9,848 |
2. | 105_classes_pins_dataset_test | 46 | 7,686 |
3. | CASIA | 10,575 | 452,627 |
4. | CELEBA | 10,177 | 201,172 |
5. | DigiFace | 10,000 | 720000 |
6. | LFW | 311 | 5425 |
Backbone | Precision | Training Dataset | Evaluation Metrics | Model Path | Model Size | Model FPS | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
MobileFaceNet | FP32 | 105_train + CELEBA + DigiFace + CASIA |
|
4.2 mb | 124 | |||||||||||||
MobileFaceNet | FP16 | 105_train + CELEBA + DigiFace + CASIA |
|
2.2 mb | 1000 | |||||||||||||
MobileFaceNet | PTQ(INT8) | 105_train + CELEBA + DigiFace + CASIA |
|
1.6 mb | 1300 | |||||||||||||
MobileFaceNetv2 | FP32 | 105_train + CELEBA + DigiFace + CASIA |
|
4.8 mb | 105 | |||||||||||||
MobileFaceNetv2 | FP16 | 105_train + CELEBA + DigiFace + CASIA |
|
2.6 mb | 938 | |||||||||||||
MobileFaceNetv2 | PTQ(INT8) | 105_train + CELEBA + DigiFace + CASIA |
|
2 mb | 1133 |
Model | 105_Classes(Kaggle) | LFW |
---|---|---|
MobileFaceNet | ||
MobileFaceNetv2 |
Model | 105_Classes(Kaggle) | LFW |
---|---|---|
MobileFaceNet | ||
MobileFaceNetv2 |