Use cases

Try a pretrained detector

See what an off-the-shelf model finds on your own images before you spend anything on labels.

Before labeling thousands of images, it’s worth an hour to learn whether a ready-made detector already reacts to your material — and in whose words. The Run EdgeCrafter Detector on Images template runs a pretrained detector over a dataset and gives you its boxes to look at.

Dataset Predict EdgeCrafter

Nodes: Dataset and a prediction node — Predict EdgeCrafter, Predict YOLO or Predict DEIM.

1

Point the flow at your images

Set the Dataset node to your images. They don’t need any labels.

2

Choose a model

In the prediction node’s Model input, pick one of the pretrained models from the catalog. Smaller ones are faster, larger ones are more accurate — each node’s page in the Node Library compares them. Optionally tick only the Predict classes you care about.

3

Set the confidence threshold

The node’s Config holds the inference settings, including the confidence below which boxes are dropped. The default is a good start. Lower it to see what the model is unsure about, raise it to keep only confident boxes.

4

Run and look

Run the prediction node. It writes the images with the model’s boxes into a new predictions dataset, which the node previews on the canvas. Open the preview to page through the results.

The run needs a GPU worker, and the first run of a model can take a little longer while its image is pulled.

What to look for

Two questions, answered by paging through the predictions:

  • Does the model react to your objects at all? If it finds most of them, you may need far fewer labels than you planned — or none, if you only need what it already knows.
  • In whose vocabulary? Pretrained detectors predict their own classes, such as the 80 COCO classes. If yours are scratch and dent, the model can’t name them, and you’ll need to fine-tune. If yours are car and person, you may only need to map or filter its classes.

See it running: live example · case study

Next steps

  • Pre-label with it. Use the predictions dataset as the pool of a labeling flow, so labelers correct boxes instead of drawing them.
  • Score it. If you have a few labeled images, measure it properly with Evaluate and compare models.
  • Fine-tune it. When its vocabulary isn’t yours, train a detector on your own labels.