Custom Object Detection Simplified

Google Colab Link

How to Run

Use TensorFlow 1.15
Requires Python 3.5+.

Fork and clone this repository to your local machine.

https://github.com/RifasM/Simplified-Custom-Object-Detection-Training

Install required libraries

pip3 install -r requirements.txt

Step 1: Annotate some images

• Delete the existing Pictures in the Folders ./data/raw, './data/images`
• Save some pictures with your custom object(s), ideally with jpg extension to ./data/raw directory.(>20 Suggested)
• Resize those pictures to uniformed size. e.g. (800, 600) using the command

python resize_images.py --raw-dir ./data/raw --save-dir ./data/images --ext jpg --target-size "(800, 600)"

Resized images can now be found in ./data/images/

• Split those files into two directories, ./data/images/train and ./data/images/test

• Annotate these resized images with labelImg to generate xml files inside ./data/images/train and ./data/images/test folders.

Tip: Use available shortcuts (w: draw box, d: next file, a: previous file, etc.) to accelerate the annotation.

• Commit and push your annotated images and xml files (./data/images/train and ./data/images/test) to your forked repository.

Step 2: Open tensorflow_object_detection.ipynb on Jupyter Notebook or Colab notebook

• Replace the repository's url to yours and run it.
• Follow the Instructions on the Notebook