Sign Language Detection Model

Model Description

This model, achedguerra/resnet-50-signal_language, is a fine-tuned version of microsoft/resnet-50 for real-time sign language detection. It has been trained on a dataset of sign language images to provide accurate and efficient detection of sign language gestures.

Key Features

• Based on the powerful ResNet-50 architecture
• Fine-tuned specifically for sign language detection
• Capable of real-time processing
• Promotes accessibility and inclusion in technology

Use Cases

• Real-time sign language interpretation
• Assistive technology for the deaf and hard of hearing
• Educational tools for learning sign language
• Enhancing communication in diverse environments

How to Use the Model

Installation

First, ensure you have the Transformers library installed:

pip install transformers

Loading the Model

You can load the model using the Transformers library:

from transformers import AutoFeatureExtractor, AutoModelForImageClassification
import torch
from PIL import Image

model_name = "achedguerra/resnet-50-signal_language"

# Load the model and feature extractor
feature_extractor = AutoFeatureExtractor.from_pretrained(model_name)
model = AutoModelForImageClassification.from_pretrained(model_name)

Inference

To use the model for inference:

# Load and preprocess the image
image_path = "path/to/your/image.jpg"
image = Image.open(image_path)
inputs = feature_extractor(images=image, return_tensors="pt")

# Perform inference
with torch.no_grad():
    outputs = model(**inputs)

# Get the predicted class
predicted_class_idx = outputs.logits.argmax(-1).item()
predicted_class = model.config.id2label[predicted_class_idx]

print(f"Predicted sign: {predicted_class}")

Training Details

• Base model: microsoft/resnet-50
• Training data: Custom dataset of sign language images
• Fine-tuning process: The model was fine-tuned using transfer learning techniques to adapt it for sign language detection

Performance

[Include any relevant performance metrics, such as accuracy, precision, recall, or F1 score]

Limitations

• The model's performance may vary depending on the quality and lighting of input images
• It is trained on a specific set of sign language gestures and may not recognize all possible signs

Ethical Considerations

• This model should be used to assist and enhance communication, not to replace human interpreters
• Care should be taken to ensure the model performs equally well across different skin tones and hand shapes

Citation

If you use this model in your research or project, please cite it as follows:

@misc{SignLanguageDetectionModel,
  author = Hugo Alejandro Guerra Peralta,
  title = Sign Language Detection using Fine-tuned ResNet-50,
  year = 2024,
  howpublished = {\url{https://huggingface.co/achedguerra/resnet-50-signal_language}}
}

Contact

For any questions or feedback, please open an issue on the model's Hugging Face repository at https://huggingface.co/achedguerra/resnet-50-signal_language or contact the author through the Hugging Face platform.