---
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.
