Model Details
Model Description
This model is designed to recognize sign language digits. It was trained on a dataset containing images of hand gestures representing digits, with approximately 200 images per digit, spread across 9-10 different folders. The images were organized, and the data was split into training (80%) and testing (20%) sets. A Convolutional Neural Network (CNN) architecture was used to train the model, achieving a high level of accuracy and reliability.
- Developed by: Jainam Sanghavi
- Model type: Convolutional Neural Network (CNN)
- Language(s) (NLP): Not applicable
- License: Open Database License (ODbL)
Model Sources [optional]
- Repository: GitHub Repository
Uses
This model can be used to recognize hand gestures representing digits in sign language. It is ideal for applications in accessibility, communication aids, or any system requiring digit recognition through hand gestures.
Direct Use
This model can be directly used in applications that require real-time recognition of sign language digits, such as educational tools or assistive technology for people with hearing impairments.
Out-of-Scope Use
The model is not designed for recognizing gestures beyond the digits it was trained on. It should not be used for broader gesture recognition tasks or in situations where accuracy is critical without further validation.
Bias, Risks, and Limitations
The model was trained on a dataset that may not be fully representative of all variations in hand gestures for sign language digits. It might not perform equally well across all demographic groups or in different lighting conditions.
Recommendations
Users should be aware of the limitations regarding the model's generalization to different populations and environments. Additional training or validation may be necessary for specific use cases.
How to Get Started with the Model
Use the code below to get started with the model:
Evaluation
Accuracy and Loss Curves
Below are the plots showing the model's training and validation accuracy as well as the training and validation loss over epochs.
Accuracy vs. Validation Accuracy
Loss vs. Validation Loss
The plots indicate that the model's accuracy improves steadily with minimal overfitting, as shown by the close alignment between the training and validation curves.
# Example code for loading and using the model
from tensorflow.keras.models import load_model
model = load_model('path_to_your_model')
# Add further instructions on how to use the model
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