This project demonstrates the implementation of an image classification model using TensorFlow. It utilizes transfer learning with a pre-trained MobileNet model to classify images into multiple categories, showcasing the power of modern machine learning techniques.
- Dataset: The model is trained on the Oxford Flowers 102 dataset.
- Model: Transfer learning with MobileNet for feature extraction.
- Application: A command-line tool for image classification.
- Data preprocessing pipelines for resizing and normalizing images.
- Transfer learning for efficient model training.
- Command-line application for inference, including top-K predictions and class mapping.
- Python
- TensorFlow and TensorFlow Hub
- Matplotlib for data visualization
- Clone the repository:
git clone https://github.com/BaraSedih11/image_classifier.git
- Install the required libraries:
pip install -r requirements.txt- Train the model:
python train.py- Use the trained model to classify images:
python predict.py --image_path <path_to_image> --model_path <path_to_model>- Achieved high accuracy in classifying flower species using transfer learning.
- Provided a flexible and easy-to-use tool for image classification tasks.