Image classification answers the question "what is this image?" by assigning a single label to each input. This tutorial walks through training a YOLOv8 classification model on Datature Nexus in about five minutes, from dataset upload to working predictions.
What This Tutorial Covers
- Uploading and organizing an image dataset on Nexus
- Labeling images into distinct categories
- Selecting YOLOv8 as the classification architecture
- Configuring training parameters (epochs, batch size, learning rate)
- Running the model and reviewing predictions
Where Image Classification Gets Used
Classification is the right tool when you need a single answer per image. Product quality sorting on manufacturing lines (pass/fail/rework). Plant disease identification in agriculture (healthy vs. blight vs. rust). Document type categorization in enterprise workflows. Content moderation for user-uploaded images. Visual search in e-commerce where customers photograph items to find matches.
If you need to know where something is in the image (not just what the image contains), you want object detection instead.
Why YOLOv8 for Classification
YOLOv8 is best known for detection, but its classification backbone is fast and accurate. It trains quickly, exports to multiple formats (ONNX, TFLite, CoreML), and handles real-time inference on edge devices. For most classification tasks, it provides a strong baseline without tuning.
