https://github.com/sajalkmr/imageclassification

Build a CNN Image classifier model using your own images with Tensorflow
https://github.com/sajalkmr/imageclassification

cnn cnn-classification cnn-keras cnn-model deep-learning image-classification jupyter-notebook keras machine-learning opencv python tensorflow

Last synced: 3 months ago
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Build a CNN Image classifier model using your own images with Tensorflow

• Host: GitHub
• URL: https://github.com/sajalkmr/imageclassification
• Owner: sajalkmr
• License: mit
• Created: 2023-01-08T16:56:40.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2023-03-09T09:41:58.000Z (over 3 years ago)
• Last Synced: 2025-01-22T08:12:43.837Z (over 1 year ago)
• Topics: cnn, cnn-classification, cnn-keras, cnn-model, deep-learning, image-classification, jupyter-notebook, keras, machine-learning, opencv, python, tensorflow
• Language: Jupyter Notebook
• Homepage:
• Size: 101 MB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          


Image Classifier

This project provides a script to train a convolutional neural network (CNN) on a dataset of images. The script is written in Python and uses TensorFlow and OpenCV. The model can classify images into different classes based on the dataset provided.


Requirements



  
Tensorflow

  
Tensorflow-gpu

  
OpenCV-python

  
Matplotlib



Usage



  
Clone or download the repository.

  
Make sure that you have the required libraries installed.

  
Place your dataset in a directory called 'training_data'

  
Open the Jupyter notebook by running the command "jupyter notebook" in your terminal in the directory.

  
Locate and open the training_model.ipynb notebook


  
Make sure that the kernel for the notebook is set to Python 3.

  
Run the cells in the notebook to train the model on your dataset.

  
The script will create a unique folder with the current date and time in the format dd.mm.yyyy hhmmss-train_results for storing the model files and training results.

  
To use this script, you will need to place your dataset of images in a directory called 'training_data'. The script expects the images to be organized into different subfolders within the 'training_data' directory, one for each class. For example, if you have two classes, "class1" and "class2", you should create two subfolders within the 'training_data' directory named "class1" and "class2" and place the corresponding images in each folder. Make sure that the images are in jpeg, jpg, bmp, or png format.

  


  

  
Example file structure:

  



training_data/

    class1/

        image1.jpg

        image2.jpg

        ...

    class2/

        image1.jpg

        image2.jpg

        ...





Model architecture

The model architecture is a convolutional neural network (CNN) with the following layers:



  
Conv2D with 16 filters of size (3,3), stride of 1, and ReLU activation

  
MaxPooling2D

  
Conv2D with 32 filters of size (3,3), stride of 1, and ReLU activation

  
MaxPooling2D

  
Conv2D with 16 filters of size (3,3), stride of 1, and ReLU activation

  
MaxPooling2D

  
Flatten layer

  
Dense layer with 128 units and ReLU activation

  
Dense layer with the number of units equal to the number of classes in the dataset and softmax activation



You can change the architecture of the model, training parameters, and data set path accordingly.


  

Files saved after training

The script will save the following files in the unique folder created after training:



  
The trained model file in the format model.h5

  
A plot figure of a batch of the training images and their labels in the format batch_result.jpg

  
An Accuracy and a Loss chart as accuracy_chart.jpg and loss_chart.jpg

 

  
A file with the training and validation accuracy and loss values in the format histroy.json

   
Final summary and result as .txt



  

 
Training Results

The following images are examples of the results generated by the script after training.




    

    

    



  

  

  

  

Note



  
This script is set to work with images of size 256x256, you can change the image size accordingly.

  
The script automatically loads the images and splits them into train, validation, and test sets.

  




  

  

Contributing

If you want to contribute to this project, please feel free to submit a pull request.

  

  

  

Licence

This project is licensed under the MIT License - see the LICENSE.md file for details.


References


  
The code for this project is greatly inspired by Nicholas Renotte.


Examples

You can find an example of a pre-trained model in the model folder, which was trained to classify images as notes and non-notes from WhatsApp images. You can use this pre-trained model as a starting point for fine-tuning on your own dataset or as a reference for comparison to your own model's performance. The model is trained using two classes (notes and non-notes) and is available in this repository NotesSeparator.