DetectionEngine.predict
DetectionEngine.predict(model_name=None, split='test')Boxes, scores and labels for every image in a split, in its own pixels.
A list and not an array, and in source pixels rather than the network’s frame - both for the same reason the segmentation side returns a list for space="source". Images differ in size, the number of boxes found differs per image, and a box in a letterboxed 416x416 frame cannot be drawn on the photograph it came from without undoing the letterbox, which is a step nobody should have to remember.
Parameters
| Name | Type | Description | Default |
|---|---|---|---|
| split | str | Which data to predict on. | 'test' |
| model_name | str | None | Which detector. Defaults to the first. | None |
Returns
| Name | Type | Description |
|---|---|---|
| list[dict[str, Any]] | A list, one entry per image, each holding boxes in that image’s own pixels | |
| list[dict[str, Any]] | with the inverse letterbox already applied. No array form: images differ in | |
| list[dict[str, Any]] | size, so does the number of boxes, and a box in a letterboxed frame cannot be | |
| list[dict[str, Any]] | drawn on the photograph it came from. |