This project demonstrates a deep learning-based approach to solving a multi-class image classification problem. Using TensorFlow and related libraries, it covers the complete pipeline from data acquisition to model evaluation.
- Multi-class image classification using a custom deep learning model.
- Data preprocessing, including normalization and augmentation.
- Model training and evaluation with performance visualization.
- Hands-on use of TensorFlow, Scikit-learn, Matplotlib, and Seaborn.
- Deep Learning and Neural Network Design
- Image Data Preprocessing
- Model Training and Tuning
- Performance Evaluation and Visualization
- Programming Language: Python
- Libraries/Frameworks:
- TensorFlow (v2.14.0)
- Matplotlib
- Scikit-learn
- Seaborn
- Kaggle
- Getting Data: Load and explore the dataset for training.
- Processing Data: Preprocess images for model compatibility.
- Building Model: Create and compile a deep learning model.
- Training Model: Train the model and monitor metrics.
- Evaluate Model: Assess the model’s performance on test data.
To run this project, ensure you have the following installed:
- Python 3.7+
- TensorFlow 2.14.0
- Matplotlib
- Scikit-learn
- Seaborn
- Kaggle
To install the required dependencies, run:
pip install tensorflow==2.14.0 matplotlib scikit-learn seaborn kaggle- Clone this repository or download the Jupyter notebook file.
- Ensure all dependencies are installed.
- Open the notebook in your Jupyter environment.
- Run each cell sequentially to execute the project pipeline.
The project includes visualizations of the model’s training performance and evaluation metrics. Detailed results can be found in the evaluation section of the notebook.
This project is open-source and available under the MIT License.
[Yash Deepak Bambore]
Feel free to contribute or raise issues to enhance this project!