license: mit
metrics:
- accuracy
library_name: keras
language:
- en
pipeline_tag: image-classification
tags:
- code
🤖 Model Card for Model ID: FaceAuthenticator.keras 📋 Model Details 📝 Model Description The FaceAuthenticator.keras model is a deep learning model designed for face authentication tasks. It utilizes a VGG16 convolutional neural network (CNN) architecture to extract features from facial images and make predictions about whether the face belongs to an authorized individual. This model is typically used in applications such as face recognition systems for security or access control. Developed by: Prathmesh Patil Model type: Convolutional Neural Network (CNN)
🛠️ Uses 👍 Direct Use The FaceAuthenticator.keras model can be directly used for face authentication tasks without the need for fine-tuning. 🔒 🔧 Downstream Use This model can be fine-tuned for specific face authentication tasks or integrated into larger systems for access control and security applications. 🛡️ ❌ Out-of-Scope Use The model may not work well for faces that significantly differ from those in the training data. It is not suitable for tasks outside of face authentication. 🚫
⚠️ Bias, Risks, and Limitations The model's performance may be affected by biases present in the training data, such as underrepresentation of certain demographics. Additionally, it may struggle with low-quality images or faces occluded by accessories like glasses or hats. ⚠️
💡 Recommendations Users should be aware that the model was trained with a specific dataset and may not generalize well to all populations. Consider additional authentication methods or human verification for critical decisions based on its predictions. 🤔
🚀 How to Get Started with the Model Use the code below to get started with the model: code.py
🧠 Training Details 📊 Training Data The model has been trained on a dataset containing facial images labelled for authentication purposes. ├── dataset-metadata.json ├── train │ ├── fake │ └── real ├── train.csv ├── valid │ ├── fake │ └── real └── valid.csv
🔍 Training Procedure 📊 Training Hyperparameters Training regime: VGG16 with 10 epochs Accuracy: Approximately 82%