Image Classification with TensorFlow

Summary

This project is built to teach you how to prepare your images and data for image classification task. There are three notebooks that can be referred in the root directory of this GitHub repository:

1. Image collection.ipynb -- collecting and organizing the images into folders
2. Training - Sign Language.ipynb -- preparing the dataset, building model from scratch, and training, on sign language recognition from gestures
3. Training - Transfer Learning - Skin Cancer.ipynb -- using pre-trained models for transfer learning, on skin cancer classification

These three notebooks contain very detailed explanations and comments to guide you through the code. Please refer to them for more details on all the steps.

This project also assumes that you already have some knowledge about deep learning with computer vision, especially the fundamental knowledge of convolutional neural networks. Otherwise, you would have a hard time understanding what is happening under the hood with the code in the notebooks.

You may also download the datasets used in this repository from the links below:

1. Sign Language (Gesture) Recognition: Google Drive Link
2. Skin Cancer Classification: Kaggle Link

Package Installation

NOTE: It is assumed that you have installed Anaconda in your machine and know how to create virtual environments on your own.

Run the following command in your Anaconda prompt (Anaconda's terminal or your terminal of choice) to create a virtual environment named tensorflow with Python version 3.8 installed:

conda create --name tensorflow python=3.8

Then remember to activate the environment with the command below every time you open up a new terminal before proceeding.

conda activate tensorflow

For the purpose of using TensorFlow with GPU on your local machine, please refer to this YouTube video for complete instructions on how to install the dependencies (CUDA, cuDNN for TensorFlow GPU support). Beware that this is a very tedious and error-prone process, if there is any error, please don't hesitate to ask for help.

After installing TensorFlow, you may proceed to install the rest of the packages with the command below (assuming that your terminal is already inside this repo's directory):

pip install --no-cache-dir -r requirements.txt

After installing the packages, create a Jupyter kernel to be selected in Jupyter Notebook/Lab with this command:

python -m ipykernel install --user --name tensorflow --display-name "tensorflow"

Also run this to update the ipykernel to avoid some errors.

conda install ipykernel --update-deps --force-reinstall

NOTE: But to use TensorFlow with GPU support on your local machine, you will need to refer to this YouTube video tutorial for further instructions as this is a very error-prone process.