ObjectTorch: Image Classification GUI

ObjectTorch is a user-friendly desktop application for image classification built with Python and with their frameworks Kivy, and TensorFlow. It allows you to both train your own image classification models and use pre-trained models to make predictions on your own images.

Features

• Train Custom Models: Easily train a new image classification model on your own dataset.
• Use Pre-trained Models: Load and use existing Keras models for image classification.
• Image Prediction: Get predictions for your own images.
• Simple GUI: An intuitive graphical user interface built with Kivy.

Installation

1. Clone the repository:

   git clone https://github.com/DhruvSonavane/ObjectTorch.git
   cd ObjectTorch

2. Install dependencies: Make sure you have Python 3.12 or higher installed. Then, install the required packages using pip:

   pip install -r requirements.txt

Usage

To run the application, execute the following command from the root directory:

python "source/leading(main).py"

Using a Pre-trained Model

1. Unzip the Dhruv_Object_Torch_Test_Model.zip file.
2. Click the "Load Model" button.
3. Select the Dhruv_Object_Torch_Model directory that you unzipped.
4. Click the "Load Image" button to select an image for prediction.
5. The application will display the predicted class for the image.

Training a New Model

1. Click the "New Model" button.
2. Enter a name for your new model and the number of epochs to train for.
3. Click the "Confirm" button.
4. You will be prompted to select a dataset directory. The directory should contain subdirectories for each class, with the images for that class inside.
5. The application will train the model and display a "Save" button when training is complete.
6. Click the "Save" button to save the trained model.

Project Structure

.
├── Dhruv_Object_Torch_Test_Model.zip
├── Look.PNG
├── Object_Torch.zip
├── README.md
├── requirements.txt
└── source
    ├── __pycache__
    ├── assest
    │   ├── 1.png
    │   ├── 2.png
    │   ├── 3.png
    │   ├── 4.png
    │   ├── 5.png
    │   ├── 6.png
    │   ├── 7.png
    │   └── 8.png
    ├── associate.kv
    ├── leading(main).py
    └── reverse.py