https://github.com/bhaveshbhakta/vegetable-classification-using-inceptionv3
Vegetable Classification
https://github.com/bhaveshbhakta/vegetable-classification-using-inceptionv3
cnn cnn-classification deep-learning inceptionv3 vegetable-classification
Last synced: 3 months ago
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Vegetable Classification
- Host: GitHub
- URL: https://github.com/bhaveshbhakta/vegetable-classification-using-inceptionv3
- Owner: BhaveshBhakta
- Created: 2025-01-10T14:48:27.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2025-02-10T16:05:46.000Z (8 months ago)
- Last Synced: 2025-03-04T16:26:43.461Z (8 months ago)
- Topics: cnn, cnn-classification, deep-learning, inceptionv3, vegetable-classification
- Language: Jupyter Notebook
- Homepage:
- Size: 1.78 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Vegetable Classification Using InceptionV3
## Overview
This project focuses on classifying different types of vegetables using the **InceptionV3** deep learning model. The dataset contains images of various vegetables, and the model is trained to recognize and categorize them accurately. The workflow includes data preprocessing, augmentation, model training, evaluation, and visualization of results.
## Technical Highlights
- **Dataset:** The dataset is sourced from Kaggle and contains labeled images of 15 vegetable categories.
- **Preprocessing:** Includes image resizing, augmentation (rotation, zoom, brightness adjustments), and normalization.
- **Model:** Uses **InceptionV3** as a pre-trained model with transfer learning, adding fully connected layers for classification.
- **Training:** The model is trained using categorical cross-entropy loss and the Adam optimizer with early stopping and checkpointing.
- **Evaluation:** Metrics like accuracy, loss curves, confusion matrix, and classification reports are used for performance analysis.
## Purpose
The primary goal of this project is to develop an efficient deep-learning model for vegetable classification. This helps in automating the process of identifying vegetables from images, reducing human effort and potential errors. The project also explores the effectiveness of transfer learning in image classification tasks.
## Applications
- **Smart Retail & Inventory Management:** Automating vegetable recognition in grocery stores for efficient checkout and stock monitoring.
- **Agriculture & Supply Chain:** Assisting farmers and suppliers in sorting and classifying vegetables.
- **Food Industry:** Enhancing food processing automation by categorizing vegetables accurately.
- **Educational Use:** Providing a practical deep learning example for image classification using TensorFlow and Keras.
**Installation**
To set up and run the project, follow these steps:
1. **Clone the repository**:
```bash
git clone https://github.com/BhaveshBhakta/Vegetable-Classification-Using-InceptionV3.git
```
2. **Navigate to the project directory**:
```bash
cd Vegetable-Classification-Using-InceptionV3
```
3. **Run the jupyter notebook**:
**Collaboration**
Contributions are welcome! Feel free to fork the repository, make improvements, and submit a pull request.