How to Train Detectron2 on Custom Object Detection Data

This guide by Jacob Solawetz provides a comprehensive walkthrough on training Detectron2, a PyTorch-based modular computer vision library, for custom object detection tasks using a public blood cell detection dataset from Roboflow. It explains the process of setting up Detectron2 in a Google Colab environment, including installing dependencies, downloading and registering custom data, configuring the training setup, and visualizing training data to ensure correct import. The tutorial emphasizes the flexibility of Detectron2 in accommodating state-of-the-art computer vision models, with options available in the model zoo for various object detection architectures. It also guides users through evaluating model performance using metrics like mean average precision (mAP) and demonstrates how to run inference on unseen images to test the trained model's effectiveness. The post encourages experimentation with Detectron2's broad capabilities, suggesting additional resources and alternative object detection models, such as YOLOv5, for further exploration.