Customizing Mask R-CNN for Object Detection: A Step-By-Step Guide

Introduction

Presenter: Sergio

• Helps companies, students, freelancers build visual recognition projects

Overview

• Training a Mask R-CNN detector to detect custom objects (e.g., screwdrivers) using Google Colab

Steps to Train a Custom Mask R-CNN

1. Data Collection and Preparation

• Use a smartphone to capture images of the objects (e.g., screwdrivers).
• Ensure diverse angles and backgrounds for robustness.
• Recommended starting with ~50 images.

2. Annotation of Images

• Use makesense.ai for annotations
• Annotation Types:
  • Rectangle for basic object detection (Not recommended here)
  • Polygon for precise segmentation
• Steps:
  1. Upload images to makesense.ai
  2. Select object detection -> create required labels -> annotate with polygons.
  3. Export annotations in COCO JSON format.

3. Training the Mask R-CNN Model

• Use Google Colab notebook (link in video description).
• Setup:
  1. Enable GPU: Edit > Notebook Settings > Hardware Accelerator > GPU
• Steps in Notebook:
  1. Installation: Run cells to install dependencies and setup environment.
  2. Dataset Loading: Upload images & annotation JSON file (in COCO format).
     • Zip images folder and upload to Colab session storage.
  3. Training Configuration: Execute cells to configure training parameters.
  4. Model Training: Start training (Estimated time: 2-3 hours).

4. Testing and Downloading the Model

• Test with a random validation image to see the results.
• Download the trained model (.h5 file) from Colab.

Running the Trained Mask R-CNN Detector

• Use second Colab notebook for inference.
• Setup:
  1. Upload the trained model (.h5 file).
  2. Load a test image and the model for inference.
• Execution: Run cells to display detection results.

Advanced Features in Pro Version

• Train on multiple classes of objects.
• Real-time detection from webcam or video files.
• Continue interrupted training using Google Drive.
• Advanced settings and more accuracy.
• Accompanied by a mini-course with detailed instructions.

Conclusion

• Encouragement to start simple with a few images and annotations.
• Potential to enhance and scale the project as needed.
• Call to action for viewer engagement and feedback.