https://github.com/alihassanml/horse-and-human-classification-deep-learning-tensorflow-keras
https://github.com/alihassanml/horse-and-human-classification-deep-learning-tensorflow-keras
Last synced: 3 months ago
JSON representation
- Host: GitHub
- URL: https://github.com/alihassanml/horse-and-human-classification-deep-learning-tensorflow-keras
- Owner: alihassanml
- Created: 2024-04-03T14:54:36.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-04-09T18:11:16.000Z (over 1 year ago)
- Last Synced: 2025-02-21T11:27:05.632Z (8 months ago)
- Language: Jupyter Notebook
- Size: 7.81 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Horse And Human Classification using Deep Learning with TensorFlow and Keras
This project is aimed at detecting whether an image contains a human or a horse using deep learning techniques implemented with TensorFlow and Keras.
## Overview
The goal of this project is to train a convolutional neural network (CNN) to classify images as either containing a human or a horse. We utilize the TensorFlow and Keras frameworks to implement the CNN architecture and train it on a dataset containing images of both humans and horses.
## Requirements
- Python 3.x
- TensorFlow
- Keras
- Matplotlib
- NumPy
## Dataset
The dataset used for training and testing consists of images of both horses and humans. It is essential to have a balanced dataset with a sufficient number of images for each class to ensure the model's robustness.
## Setup and Usage
1. Clone this repository:
```
git clone https://github.com/alihassanml/Horse-And-Human-Classification-Deep-Learning-Tensorflow-Keras.git
```
2. Install the required dependencies:
```
pip install -r requirements.txt
```
3. Download the dataset and place it in the appropriate directory within the project structure.
4. Train the model using the provided Python script:
```
python train.py
```
5. Once trained, you can use the trained model for inference on new images:
```
python predict.py path/to/image.jpg
```
## Model Evaluation
The model's performance can be evaluated using metrics such as accuracy, precision, recall, and F1-score. These metrics provide insights into how well the model is performing in terms of classifying images correctly.
## Results
The results of the classification can be visualized using various techniques such as confusion matrices, precision-recall curves, and ROC curves. These visualizations help in understanding the model's behavior and identifying areas for improvement.
## Future Improvements
Possible enhancements for this project include:
- Fine-tuning the model architecture for better performance.
- Data augmentation techniques to increase the diversity of the training dataset.
- Hyperparameter tuning to optimize the model's performance further.
## Contributors
- [Your Name](https://github.com/alihassanml)
- [Other contributors if any]
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
---
Feel free to modify and expand upon this README according to your project's specific requirements and features.