louiecerv/amer_sign_lang_data_augmentation

---
language: en
tags:
  - image-classification
  - deep-learning
  - cnn
license: apache-2.0
datasets:
  - louiecerv/american_sign_language

## Model Description

The model consists of three convolutional blocks followed by max-pooling layers, a flattening layer, and two fully connected layers. It is designed to classify images of ASL letters into 24 classes (A-X).

## Intended Uses & Limitations

This model is intended for educational purposes and as a demonstration of image classification using CNNs. It is not suitable for real-world applications without further validation and testing.

## How to Use

```python
import torch
from torchvision import transforms
from PIL import Image

model = base_model
model.load_state_dict(torch.load("path_to_model/pytorch_model.bin"))
model.eval()

transform = transforms.Compose([
    transforms.Grayscale(num_output_channels=1),
    transforms.Resize((28, 28)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.5], std=[0.5])
])

image = Image.open("path_to_image").convert("RGB")
image = transform(image).unsqueeze(0)

with torch.no_grad():
    output = model(image)
    _, predicted = torch.max(output.data, 1)
print(f"Predicted ASL letter: {predicted.item()}")
```

## Training Data

The model was trained on the `louiecerv/american_sign_language` dataset.

## Training Procedure

The model was trained using Adam optimizer with a learning rate of 0.001 and a batch size of 64 for 5 epochs.

## Evaluation Results

The model achieved an accuracy of 92% on the validation set.