Object Detection: The Most Common CV Task
Object detection finds objects in an image and draws bounding boxes around each one, predicting the class and location in a single pass. It is the starting point for most computer vision projects: counting items on a shelf, spotting defects on a production line, tracking vehicles in traffic footage.
What This Tutorial Covers
Datature walks through the full pipeline for training a custom object detection model on Nexus:
- Creating a project and uploading your images
- Drawing bounding box annotations on the objects you want to detect
- Selecting a model architecture and configuring training parameters
- Running the training workflow and inspecting predictions
No code is required. The tutorial runs about five minutes and covers a complete cycle from raw images to working model.
Getting Good Detection Results
Detection accuracy depends on annotation quality more than model choice. Boxes that are too loose or too tight, inconsistent labeling across annotators, and insufficient examples of rare classes all hurt performance. Datature Nexus provides quality checks during annotation and lets you review predictions against ground truth after training.
For a deeper walkthrough with YOLOv8, read Get Started with Training a YOLOv8 Object Detection Model. To evaluate your model after training, see How to Evaluate Computer Vision Models with Confusion Matrix.
