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--- |
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license: apache-2.0 |
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language: |
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- en |
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--- |
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# FaceNet Triplet ResNet Model (Grayscale, 112x112, Mobile-friendly) |
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This repository provides a FaceNet-style triplet embedding model using ResNet backbones, optimized for mobile and edge devices: |
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- Input: **Grayscale images** (`3` channels) |
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- Resolution: **112x112 pixels** |
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- Output: **Embeddings** suitable for face recognition and verification |
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## Model Details |
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- **Architecture:** ResNet50 with NdLinear |
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- **Embedding Dimension:** 512 |
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- **Input:** 3x112x112 grayscale images (NCHW format) |
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- **Exported weights:** `model.safetensors` |
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- **Config:** `config.json` |
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## Usage |
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### 1. Clone or Download Files |
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Download/copy the `models/` directory and dependencies (`ndlinear.py`, etc.) to your project. |
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### 2. Install requirements |
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```bash |
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pip install torch safetensors |
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``` |
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### 3. Load the model |
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```python |
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from models.resnet import Resnet50Triplet # or your chosen variant |
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model = Resnet50Triplet.from_pretrained(".", safe_serialization=True) |
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model.eval() |
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``` |
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### 4. Use for Face Recognition |
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Obtain a face embedding from an input image, and compare embeddings (e.g., with cosine similarity) to recognize or verify identities. |
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```python |
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import torch |
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# Example: batch of 1 grayscale image of 112x112 |
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images = torch.randn(1, 3, 112, 112) # (batch_size, channels, height, width) |
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with torch.no_grad(): |
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embedding = model(images) # embedding output suitable for face recognition |
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print(embedding.shape) # (batch_size, embedding_dim) |
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``` |
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To perform recognition or verification, compare the embedding against a database of known face embeddings using distance/similarity metrics. |
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## Files |
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- `model.safetensors` - Model weights |
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- `config.json` - Loader configuration |
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- `models/` - Model definition files |
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- `README.md` - This file |
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## Notes |
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- Model is optimized for runtime on edge/mobile devices (reduced input size, grayscale input for lower computational load) |
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- Make sure your image preprocess pipeline produces three identical grayscaled channels, 112x112 images. |
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## Credits |
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- Backbone based on [PyTorch torchvision ResNet](https://pytorch.org/vision/stable/models/generated/torchvision.models.resnet50.html) |
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- Architecture inspired by [Facenet PyTorch](https://github.com/timesler/facenet-pytorch) |
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--- |
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*For contributions or issues, open a discussion or pull request.* |
