Train a detector

Once you have labeled images, fine-tuning a detector takes three nodes. The Train EdgeCrafter Detector on Images template is the starting point, and the same shape works with YOLO and DEIM.

Nodes: Dataset, Train/validation split, and a training node — Train EdgeCrafter, Train YOLO or Train DEIM.

Point the flow at labeled data

Set the Dataset node to a dataset with box annotations — for example the Accepted dataset of a labeling flow. Choose its @latest version if later retraining should pick up newly accepted images.

Split it

The split node writes a training and a validation dataset. If your images already carry split tags — Dataset Ninja imports do — you can keep the original split instead of drawing a new one.

Choose where training starts

The training node’s Model is a model in your registry. To start from pretrained weights, first copy one from Models → Catalog with Add to registry. To continue from an earlier run, pick the model it produced. The classes come from your datasets, and Hyperparameters holds the number of epochs, the batch size and the image size, with defaults that work for a first run.

Run and watch the report

Run the training node. It needs a GPU worker. Open its report while it runs: it’s live, and shows validation metrics epoch by epoch and predictions on example images.

What you get

A model version in the registry: the best checkpoint, its class list, metrics, training parameters, the datasets it was trained and validated on, and the training report. Run the node again and the same model gets a new version.
A training report that answers the question the headline number doesn’t: did the run converge, and where did it stop improving? The best checkpoint is often not the last one, and the curves show why.

See it running: live example · case study

Make it yours

Evaluate it properly. Validation metrics come from the split used during training. For a verdict you can defend, score the model on a held-out set with Evaluate object detection.
Train two and pick one. Add a second training branch with another architecture and compare them on the same data — see Evaluate and compare models.
Retrain on a schedule. Give the training node a schedule and point it at the latest version of your accepted dataset. Each run adds a model version, and prediction nodes that use the model’s @latest pick it up.
Export it. Export YOLO and Export DEIM convert trained models for deployment outside OvalBee.