---
language:
- en
license: mit
library_name: pytorch
tags:
- computer-vision
- image-classification
- resnet
- sign-language
- handsign
metrics:
- accuracy
pipeline_tag: image-classification
---

# ResNet-18 American Sign Language (ASL) Classifier

This repository contains pre-trained PyTorch weights for a **ResNet-18** model fine-tuned for American Sign Language (ASL) alphabetic hand sign classification (static letters A through Y, excluding motion-based letters J and Z).

- **Overall Test Accuracy**: **98.48%** on 7,172 test images.
- **Framework**: PyTorch
- **Base Architecture**: ResNet-18 (ImageNet pre-trained)
- **Transfer Learning Strategy**: Frozen `layer1`-`layer3`, fine-tuned `layer4` + custom head:
  - `Linear(512, 256)` -> `BatchNorm1d(256)` -> `ReLU` -> `Dropout(0.3)` -> `Linear(256, 24)`

---

## Model Usage

```python
import torch
import torchvision.models as models
import torch.nn as nn
from PIL import Image
from torchvision import transforms
from huggingface_hub import hf_hub_download

# 1. Define Model Architecture
model = models.resnet18()
model.fc = nn.Sequential(
    nn.Linear(512, 256),
    nn.BatchNorm1d(256),
    nn.ReLU(),
    nn.Dropout(0.3),
    nn.Linear(256, 24)
)

# 2. Download and Load Model Weights
weights_path = hf_hub_download(repo_id="Vecrist/resnet18-handsign-classifier", filename="ResNet-18_9848AccModel_weights.pth")
model.load_state_dict(torch.load(weights_path, map_location="cpu"))
model.eval()

# 3. Preprocess Image
transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])

# 4. Predict
# img = Image.open("path_to_handsign_image.jpg").convert("RGB")
# outputs = model(transform(img).unsqueeze(0))
# predicted_class_idx = outputs.argmax(dim=1).item()
```

---

## Dataset & Training Details

- **Dataset**: Kaggle Hand Sign Images dataset (static alphabet signs).
- **Optimizer**: Adam with differential learning rates (`0.0001` for `layer4`, `0.001` for FC head).
- **Batch Size**: 64
- **Loss Function**: `CrossEntropyLoss`
- **Data Augmentations**: `RandomResizedCrop(224)`, `RandomHorizontalFlip`, ImageNet Normalization.