Siamese Network for Few-Shot Image Recognition
Few-shot image recognition using a Siamese Network trained on Omniglot.
Recognises new character classes from as little as a single example.
Results
Few-shot evaluation results Configuration Accuracy 5-way 1-shot 95.10% 5-way 5-shot 97.07% 10-way 1-shot 90.05% 10-way 5-shot 94.83%
Evaluated on 145 unseen test classes (never seen during training).
Architecture
Backbone: ResNet-18 pretrained, final FC stripped → 512-d features
Embedding head: Linear(512→256) → BN → ReLU → Linear(256→128) → L2 norm
Loss: Contrastive loss with margin=1.0
Distance: Cosine similarity on unit-sphere embeddings
Project Structure
siamese-few-shot/
├── src/
│ ├── dataset.py # SiamesePairDataset + EpisodeDataset
│ ├── model.py # EmbeddingNet + SiameseNet
│ ├── loss.py # ContrastiveLoss
│ ├── train.py # Training + validation loop
│ ├── run_training.py # Main training entry point
│ ├── eval.py # N-way K-shot episodic evaluation
│ └── demo.py # Gradio demo
├── checkpoints/
│ ├── best.pt
│ └── siamese_embedding.onnx
├── data/
│ └── class_split.json
├── requirements.txt
└── README.mdQuickstart
git clone https://huggingface.co/<your-username>/siamese-few-shot
cd siamese-few-shot
pip install -r requirements.txt
# Run Gradio demo
cd src && python demo.py
# Run episodic evaluation
cd src && python eval.py
# Retrain from scratch
cd src && python run_training.pyTraining Details
Dataset: Omniglot (background split, 964 classes)
Train / val / test split: 70% / 15% / 15% of classes
Epochs: 30
Batch size: 32
Optimiser: Adam lr=1e-3
Scheduler: CosineAnnealingLR
Augmentation: RandomCrop, HorizontalFlip, ColorJitter
Requirements
torch>=2.0
torchvision>=0.15
timm
gradio
onnx
onnxruntime-gpu
pillow
numpy
matplotlib
scikit-learn
tqdm
wandbDemo
Upload any two handwritten character images. The model returns a
cosine similarity score and a same / different class decision.
Trained on Latin, Greek, Cyrillic, Japanese, and 25 other alphabets
via the Omniglot dataset. Also tested on Indian script characters
(Tamil, Hindi, Telugu, Kannada, Bengali, Malayalam, Gujarati, Punjabi).
