FaceNet Triplet ResNet Model (Grayscale, 112x112, Mobile-friendly)

This repository provides a FaceNet-style triplet embedding model using ResNet backbones, optimized for mobile and edge devices:

• Input: Grayscale images (3 channels)
• Resolution: 112x112 pixels
• Output: Embeddings suitable for face recognition and verification

Model Details

• Architecture: ResNet50 with NdLinear
• Embedding Dimension: 512
• Input: 3x112x112 grayscale images (NCHW format)
• Exported weights: model.safetensors
• Config: config.json

Usage

1. Clone or Download Files

Download/copy the models/ directory and dependencies (ndlinear.py, etc.) to your project.

2. Install requirements

pip install torch safetensors

3. Load the model

from models.resnet import Resnet50Triplet  # or your chosen variant

model = Resnet50Triplet.from_pretrained(".", safe_serialization=True)
model.eval()

4. Use for Face Recognition

Obtain a face embedding from an input image, and compare embeddings (e.g., with cosine similarity) to recognize or verify identities.

import torch

# Example: batch of 1 grayscale image of 112x112
images = torch.randn(1, 3, 112, 112)  # (batch_size, channels, height, width)

with torch.no_grad():
    embedding = model(images)  # embedding output suitable for face recognition
print(embedding.shape)  # (batch_size, embedding_dim)

To perform recognition or verification, compare the embedding against a database of known face embeddings using distance/similarity metrics.

Files

• model.safetensors - Model weights
• config.json - Loader configuration
• models/ - Model definition files
• README.md - This file

Notes

• Model is optimized for runtime on edge/mobile devices (reduced input size, grayscale input for lower computational load)
• Make sure your image preprocess pipeline produces three identical grayscaled channels, 112x112 images.

Credits

• Backbone based on PyTorch torchvision ResNet
• Architecture inspired by Facenet PyTorch

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For contributions or issues, open a discussion or pull request.