You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window. Reload to refresh your session. You switched accounts on another tab or window. Reload to refresh your session.
Fruit Classifier using TensorFlow: A CNN model trained with data augmentation for accurate fruit image classification. Explore training history, model architecture, evaluation metrics, and sample predictions in this intuitive image recognition project.
Notifications You must be signed in to change notification settings
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
Go to fileThis project utilizes TensorFlow to build a Convolutional Neural Network (CNN) for classifying fruit images. The model is trained with data augmentation techniques to enhance its performance on diverse datasets. The repository includes scripts for data preprocessing, model training, evaluation, and random image predictions.
The CNN model comprises multiple convolutional and pooling layers, followed by dense layers. This architecture is optimized for accurate image classification tasks.
Explore the "results.png" for training history plots, evaluation metrics, and sample predictions to assess the model's performance.
The fruit images dataset used in this project is sourced from the official Kaggle website. The dataset, known as "Fruits 360," is available at Kaggle - Fruits 360 Dataset. It includes a diverse collection of fruit images for various machine learning and computer vision applications.
Ensure compliance with Kaggle's terms of use and licensing for the dataset.
Fruit Classifier using TensorFlow: A CNN model trained with data augmentation for accurate fruit image classification. Explore training history, model architecture, evaluation metrics, and sample predictions in this intuitive image recognition project.