---
license: mit
language:
- en
metrics:
- accuracy
pipeline_tag: image-classification
library_name: tf-keras
tags:
- legal
- code
- not-for-all-audiences
---

# Model Card for Real vs. Fake Image Classifier

This model is designed to classify images as either "real" or "fake" using a Convolutional Neural Network (CNN) built with TensorFlow Keras.

## Model Details

### Model Description

This CNN model has been developed to differentiate between real and fake images. It utilizes various convolutional layers, pooling layers, and dense layers to effectively learn features from the input images, enabling accurate classification.

- **Developed by:** [Abhishek Thakur, Mihir Vaid]
- **Model type:** Image Classification (CNN)
- **License:** MIT

## Uses

### Direct Use

This model can be used directly for image classification tasks without the need for further fine-tuning. Users can input images, and the model will output a classification of "real" or "fake."

### Out-of-Scope Use

This model is not intended for use in critical applications where misclassification could lead to significant consequences, such as security or legal decisions.

## Bias, Risks, and Limitations

While the model aims to provide accurate classifications, it may exhibit biases based on the training data. Users should be aware of potential limitations in performance across different types of images or contexts.

### Recommendations

Users should validate the model's predictions with additional methods when deploying it in sensitive applications.

## How to Get Started with the Model

To use this model, you can load it using TensorFlow Keras as shown below:

```python
import tensorflow as tf

# Load your trained model
model = tf.keras.models.load_model('path/to/your/model')

# Example prediction
image = tf.keras.preprocessing.image.load_img('path/to/image.jpg', target_size=(image_height, image_width))
image_array = tf.keras.preprocessing.image.img_to_array(image)
image_array = tf.expand_dims(image_array, axis=0)

predictions = model.predict(image_array)
print("Predicted class:", "Real" if predictions > 0.5 else "Fake")