Object detection identifies what objects are in an image and exactly where they sit. Unlike classification (which gives one label per image), detection draws bounding boxes around every object of interest and labels each one. This tutorial walks through training a YOLOv8 detection model on Datature Nexus in about eight minutes.
What This Tutorial Covers
- Uploading an image dataset to Datature Nexus
- Drawing bounding box annotations around objects of interest
- Selecting YOLOv8 and configuring detection training settings
- Launching a training run with no local GPU required
- Reviewing model predictions and evaluating accuracy
Where Object Detection Gets Used
Detection is the backbone of most production computer vision systems. Quality control on assembly lines (catching defects before packaging). Inventory counting in warehouses (scanning shelves with a camera instead of a barcode reader). Safety monitoring on construction sites (detecting workers without hard hats). License plate recognition in parking systems. Wildlife monitoring (counting and tracking animals from drone footage).
YOLOv8 delivers strong accuracy at real-time inference speeds, making it the default choice when you need fast, reliable bounding box predictions on new images or video streams.
Detection vs. Segmentation
If bounding boxes are precise enough for your use case, detection is faster to annotate and train. If you need pixel-level boundaries (cutting objects out of backgrounds, measuring irregular shapes), look at semantic segmentation instead.
