---
license: apache-2.0
base_model:
- facebook/detr-resnet-50
pipeline_tag: image-classification
library_name: adapter-transformers
---
# Sign Language Detection Model

## Model Description
This model, `achedguerra/resnet-50-signal_language`, is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/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:

```bash
pip install transformers
```

### Loading the Model
You can load the model using the Transformers library:

```python
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:

```python
# 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.