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| # EdgeFace: Efficient Face Recognition Model for Edge Devices | |
| [](https://paperswithcode.com/sota/lightweight-face-recognition-on-lfw?p=edgeface-efficient-face-recognition-model-for) | |
| [](https://paperswithcode.com/sota/lightweight-face-recognition-on-calfw?p=edgeface-efficient-face-recognition-model-for) | |
| [](https://paperswithcode.com/sota/lightweight-face-recognition-on-cplfw?p=edgeface-efficient-face-recognition-model-for) | |
| [](https://paperswithcode.com/sota/lightweight-face-recognition-on-cfp-fp?p=edgeface-efficient-face-recognition-model-for) | |
| [](https://paperswithcode.com/sota/lightweight-face-recognition-on-agedb-30?p=edgeface-efficient-face-recognition-model-for) | |
| [](https://paperswithcode.com/sota/lightweight-face-recognition-on-ijb-b?p=edgeface-efficient-face-recognition-model-for) | |
| [](https://paperswithcode.com/sota/lightweight-face-recognition-on-ijb-c?p=edgeface-efficient-face-recognition-model-for) | |
| [](https://arxiv.org/abs/2307.01838v2) | |
| [](https://huggingface.co/spaces/Idiap/EdgeFace) | |
| [](https://huggingface.co/collections/Idiap/edgeface-67f500eded03ecd8be56e63e) | |
| This repository contain inference code and pretrained models to use [**EdgeFace: Efficient Face Recognition Model for Edge Devices**](https://ieeexplore.ieee.org/abstract/document/10388036/), | |
| which is the **winning entry** in *the compact track of ["EFaR 2023: Efficient Face Recognition Competition"](https://arxiv.org/abs/2308.04168) organised at the IEEE International Joint Conference on Biometrics (IJCB) 2023*. For the complete source code of training and evaluation, please check the [official repository](https://gitlab.idiap.ch/bob/bob.paper.tbiom2023_edgeface). | |
|  | |
| ## Installation | |
| ```bash | |
| pip install -r requirements.txt | |
| ``` | |
| **Note:** If cannot `import cv2`, run above CLI in Linux | |
| ```bash | |
| chmod +x packages.txt | |
| sudo ./packages.txt | |
| ``` | |
| ## Inference | |
| The following code shows how to use the model for inference: | |
| ```python | |
| import torch | |
| from torchvision import transforms | |
| from face_alignment import align | |
| from backbones import get_model | |
| # load model | |
| model_name="edgeface_s_gamma_05" # or edgeface_xs_gamma_06 | |
| model=get_model(model_name) | |
| checkpoint_path=f'checkpoints/{model_name}.pt' | |
| model.load_state_dict(torch.load(checkpoint_path, map_location='cpu')) # Load state dict | |
| model.eval() # Call eval() on the model object | |
| transform = transforms.Compose([ | |
| transforms.ToTensor(), | |
| transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]), | |
| ]) | |
| paths = 'tests/test_images/Elon_Musk.jpg' | |
| batch_size = len(paths) if isinstance(paths, (list, tuple)) else 1 | |
| # Align faces (assuming align.get_aligned_face returns a list of tuples) | |
| aligned_result = align.get_aligned_face(paths, algorithm='yolo') | |
| transformed_inputs = [transform(result[1]) for result in aligned_result] | |
| transformed_inputs = torch.stack(transformed_inputs) | |
| # Extract embeddings | |
| embeddings = model(transformed_inputs) | |
| print(embeddings.shape) # Expected: torch.Size([batch_size, 512]) | |
| ``` | |
| ## Pre-trained models | |
| - EdgeFace-s (gamma=0.5): available in [`checkpoints/edgeface_s_gamma_05.pt`](checkpoints/edgeface_s_gamma_05.pt) | |
| - EdgeFace-xs (gamma=0.6): available in [`checkpoints/edgeface_xs_gamma_06.pt`](checkpoints/edgeface_xs_gamma_06.pt) | |
| ## Performance | |
| The performance of each model is reported in Table 2 of the [paper](https://arxiv.org/pdf/2307.01838v2.pdf): | |
|  | |
| ## :rocket: New! Using EdgeFace Models via `torch.hub` | |
| ### Available Models on `torch.hub` | |
| - `edgeface_base` | |
| - `edgeface_s_gamma_05` | |
| - `edgeface_xs_q` | |
| - `edgeface_xs_gamma_06` | |
| - `edgeface_xxs` | |
| - `edgeface_xxs_q` | |
| **NOTE:** Models with `_q` are quantised and require less storage. | |
| ### Loading EdgeFace Models with `torch.hub` | |
| You can load the models using `torch.hub` as follows: | |
| ```python | |
| import torch | |
| model = torch.hub.load('otroshi/edgeface', 'edgeface_xs_gamma_06', source='github', pretrained=True) | |
| model.eval() | |
| ``` | |
| ### Performance benchmarks of different variants of EdgeFace | |
| | Model | MPARAMS| MFLOPs | LFW(%) | CALFW(%) | CPLFW(%) | CFP-FP(%) | AgeDB30(%) | | |
| |:--------------------|-------:|-------:|:-------------|:-------------|:-------------|:-------------|:-------------| | |
| | edgeface_base | 18.23 |1398.83 | 99.83 ± 0.24 | 96.07 ± 1.03 | 93.75 ± 1.16 | 97.01 ± 0.94 | 97.60 ± 0.70 | | |
| | edgeface_s_gamma_05 | 3.65 | 306.12 | 99.78 ± 0.27 | 95.55 ± 1.05 | 92.48 ± 1.42 | 95.74 ± 1.09 | 97.03 ± 0.85 | | |
| | edgeface_xs_gamma_06| 1.77 | 154.00 | 99.73 ± 0.35 | 95.28 ± 1.37 | 91.58 ± 1.42 | 94.71 ± 1.07 | 96.08 ± 0.95 | | |
| | edgeface_xxs | 1.24 | 94.72 | 99.57 ± 0.33 | 94.83 ± 0.98 | 90.27 ± 0.93 | 93.63 ± 0.99 | 94.92 ± 1.15 | | |
| ## Reference | |
| If you use this repository, please cite the following paper, which is [published](https://ieeexplore.ieee.org/abstract/document/10388036/) in the IEEE Transactions on Biometrics, Behavior, and Identity Science (IEEE T-BIOM). The PDF version of the paper is available as [pre-print on arxiv](https://arxiv.org/pdf/2307.01838v2.pdf). The complete source code for reproducing all experiments in the paper (including training and evaluation) is also publicly available in the [official repository](https://gitlab.idiap.ch/bob/bob.paper.tbiom2023_edgeface). | |
| ```bibtex | |
| @article{edgeface, | |
| title={Edgeface: Efficient face recognition model for edge devices}, | |
| author={George, Anjith and Ecabert, Christophe and Shahreza, Hatef Otroshi and Kotwal, Ketan and Marcel, Sebastien}, | |
| journal={IEEE Transactions on Biometrics, Behavior, and Identity Science}, | |
| year={2024} | |
| } | |
| ``` | |