https://www.kaggle.com/datasets/utkarshsaxenadn/fruits-classification?resource=download

This project is a deep learning-based image classification model built using TensorFlow. It classifies images of fruits into five categories: apple, banana, grape, mango, and strawberry. The model has been trained and evaluated using a dataset split into training, validation, and test sets.

The project is implemented in Google Colab for easy accessibility and reproducibility.

• Classifies uploaded fruit images into one of five classes.
• Displays the predicted fruit label.
• Returns the prediction confidence (accuracy score).
• Easy to use via Google Colab.

The dataset consists of images categorized into five fruit types:

• Apple
• Banana
• Grape
• Mango
• Strawberry

It is divided into:

• Training set
• Validation set
• Test set

Each subset is organized into separate folders per class.

Click the button below to open the notebook in Google Colab:

Open in Colab

Upload the .ipynb notebook manually if you haven’t pushed it to a public repo yet.

If you haven't hosted the notebook in a public repository:

1. Go to Google Colab
2. Click on File > Upload Notebook
3. Select the .ipynb file from your local machine

1. Run the Notebook
   Execute the cells in sequence to load the model, process the image, and make predictions.

2. Upload an Image
   You'll be prompted to upload an image (JPG or PNG) of a fruit.

3. View Prediction
   The model will display:

   • The predicted fruit class
   • The confidence score for the prediction

The model was trained on a custom dataset of fruit images using TensorFlow with:

• Convolutional Neural Network (CNN) architecture
• Image preprocessing (resizing, normalization, augmentation)
• Categorical cross-entropy loss
• Accuracy as the primary evaluation metric

Training was performed using GPU acceleration in Google Colab.

To run the notebook locally or in Colab, make sure the following are available:

• Python 3.x
• TensorFlow
• NumPy
• Matplotlib
• Google Colab (if using online)

These are typically pre-installed in Colab.

This project is open-source and free to use for educational or research purposes.