This project implements a deep learning-based image classifier capable of identifying various fruits and vegetables. It utilizes neural networks to classify input images into predefined categories of fruits and vegetables.
Classifies input images into fruit or vegetable categories. Supports a variety of fruits and vegetables. Utilizes deep learning techniques for classification. Provides accuracy metrics to evaluate the performance of the classifier.
Python (>=3.6) Jupyter Notebook TensorFlow Keras OpenCV Numpy Matplotlib Pandas
Clone the repository: git clone https://github.com/Exstaa/fruit-vegetable-classifier.git Install dependencies: pip install -r requirements.txt
Open the Jupyter Notebook fruit_vegetable_classifier.ipynb. Follow the instructions provided in the notebook. Upload the image you want to classify. Run the notebook cells to preprocess the image and classify it. View the predicted category along with confidence scores.
The classifier model is trained on a dataset containing labeled images of fruits and vegetables. The notebook includes code for training the model. You can train the model on your own dataset or fine-tune the existing model.
The performance of the classifier is evaluated using accuracy metrics. You can evaluate the classifier on a separate test dataset to assess its performance.