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language: en
library_name: transformers
tags:
- image-classification
- resnet
- asl
- sign-language
license: mit
datasets:
- grassknoted/asl-alphabet
metrics:
- accuracy
model-index:
- name: asl-sign-language-classifier
results:
- task:
type: image-classification
name: Image Classification
dataset:
name: ASL Alphabet Dataset
type: image
split: test
metrics:
- name: Accuracy
type: accuracy
value: 0.9999
---
# ASL Sign Language Classification Model
This model is trained to recognize **American Sign Language (ASL)** alphabets using the [ASL Alphabet Dataset](https://www.kaggle.com/grassknoted/asl-alphabet).
It uses a ResNet50 backbone for image classification.
## Model Details
- **Base Architecture**: ResNet50
- **Number of Classes**: 29
- **Test Accuracy**: 0.9999
- **Dataset**: ASL Alphabet (A–Z, space, delete, nothing)
## Usage
```python
from transformers import AutoImageProcessor, AutoModelForImageClassification
from PIL import Image
import torch
# Load model and processor
model = AutoModelForImageClassification.from_pretrained("Abuzaid01/asl-sign-language-classifier")
processor = AutoImageProcessor.from_pretrained("Abuzaid01/asl-sign-language-classifier")
# Load an image
image = Image.open("asl_sample.jpg")
# Preprocess
inputs = processor(images=image, return_tensors="pt")
# Predict
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
predicted_class = logits.argmax(-1).item()
print("Predicted class:", model.config.id2label[predicted_class])
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