turicreate.object_detector.ObjectDetector.predict

ObjectDetector.predict(dataset, confidence_threshold=0.25, iou_threshold=0.45, verbose=True)

Predict object instances in an SFrame of images.

Parameters:

dataset : SFrame | SArray | turicreate.Image

The images on which to perform object detection. If dataset is an SFrame, it must have a column with the same name as the feature column during training. Additional columns are ignored.

Returns:

out : SArray

An SArray with model predictions. Each element corresponds to an image and contains a list of dictionaries. Each dictionary describes an object instances that was found in the image. If dataset is a single image, the return value will be a single prediction.

See also

evaluate

Examples

# Make predictions
>>> pred = model.predict(data)

# Stack predictions, for a better overview
>>> turicreate.object_detector.util.stack_annotations(pred)
Data:
+--------+------------+-------+-------+-------+-------+--------+
| row_id | confidence | label |   x   |   y   | width | height |
+--------+------------+-------+-------+-------+-------+--------+
|   0    |    0.98    |  dog  | 123.0 | 128.0 |  80.0 | 182.0  |
|   0    |    0.67    |  cat  | 150.0 | 183.0 | 129.0 | 101.0  |
|   1    |    0.8     |  dog  |  50.0 | 432.0 |  65.0 |  98.0  |
+--------+------------+-------+-------+-------+-------+--------+
[3 rows x 7 columns]

# Visualize predictions by generating a new column of marked up images
>>> data['image_pred'] = turicreate.object_detector.util.draw_bounding_boxes(data['image'], data['predictions'])