Update README.md

README.md

@@ -1,3 +1,98 @@
----
-license: apache-2.0
----
+---
+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.