https://github.com/udacity-machinelearning-internship/image_classifier

Image classifier project for Udacity "Intro to machine learning with tensorflow" program.
https://github.com/udacity-machinelearning-internship/image_classifier

Last synced: 5 months ago
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Image classifier project for Udacity "Intro to machine learning with tensorflow" program.

• Host: GitHub
• URL: https://github.com/udacity-machinelearning-internship/image_classifier
• Owner: Udacity-MachineLearning-Internship
• Created: 2024-09-21T18:59:17.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2024-12-12T19:17:21.000Z (about 1 year ago)
• Last Synced: 2025-03-28T01:57:51.148Z (9 months ago)
• Language: Jupyter Notebook
• Size: 46.7 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          


  ![Image_Classifier](https://github.com/user-attachments/assets/4d36abb4-c14a-4fd9-9350-d3a7a1a89072)

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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.

## Project Overview

- **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.

## Features

- 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.

## Technologies Used

- **Python**

- **TensorFlow** and **TensorFlow Hub**

- **Matplotlib** for data visualization

## Installation and Usage

1. Clone the repository:

   ```bash

   git clone https://github.com/BaraSedih11/image_classifier.git

   ```

2. Install the required libraries:

  ```bash

  pip install -r requirements.txt

  ```

3. Train the model:

  ```bash

  python train.py

  ```

4. Use the trained model to classify images:

  ```bash

  python predict.py --image_path  --model_path 

  ```

## Results

* Achieved high accuracy in classifying flower species using transfer learning.

* Provided a flexible and easy-to-use tool for image classification tasks.