Bases: ObjectDetectionBaseOnnxRoboflowInferenceModel
Roboflow ONNX Object detection model (Implements an object detection specific infer method).
This class is responsible for performing object detection using the YOLOv8 model
with ONNX runtime.
Attributes:
Name |
Type |
Description |
weights_file |
str
|
Path to the ONNX weights file.
|
Methods:
Name |
Description |
predict |
Performs object detection on the given image using the ONNX session.
|
Source code in inference/models/yolov8/yolov8_object_detection.py
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52 | class YOLOv8ObjectDetection(ObjectDetectionBaseOnnxRoboflowInferenceModel):
"""Roboflow ONNX Object detection model (Implements an object detection specific infer method).
This class is responsible for performing object detection using the YOLOv8 model
with ONNX runtime.
Attributes:
weights_file (str): Path to the ONNX weights file.
Methods:
predict: Performs object detection on the given image using the ONNX session.
"""
@property
def weights_file(self) -> str:
"""Gets the weights file for the YOLOv8 model.
Returns:
str: Path to the ONNX weights file.
"""
return "weights.onnx"
def predict(self, img_in: ImageMetaType, **kwargs) -> Tuple[np.ndarray]:
"""Performs object detection on the given image using the ONNX session.
Args:
img_in (np.ndarray): Input image as a NumPy array.
Returns:
Tuple[np.ndarray]: NumPy array representing the predictions, including boxes, confidence scores, and class confidence scores.
"""
predictions = run_session_via_iobinding(
self.onnx_session, self.input_name, img_in
)[0]
predictions = predictions.transpose(0, 2, 1)
boxes = predictions[:, :, :4]
class_confs = predictions[:, :, 4:]
confs = np.expand_dims(np.max(class_confs, axis=2), axis=2)
predictions = np.concatenate([boxes, confs, class_confs], axis=2)
return (predictions,)
|
weights_file
property
Gets the weights file for the YOLOv8 model.
Returns:
Name | Type |
Description |
str |
str
|
Path to the ONNX weights file.
|
predict(img_in, **kwargs)
Performs object detection on the given image using the ONNX session.
Parameters:
Name |
Type |
Description |
Default |
img_in
|
ndarray
|
Input image as a NumPy array.
|
required
|
Returns:
Type |
Description |
Tuple[ndarray]
|
Tuple[np.ndarray]: NumPy array representing the predictions, including boxes, confidence scores, and class confidence scores.
|
Source code in inference/models/yolov8/yolov8_object_detection.py
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52 | def predict(self, img_in: ImageMetaType, **kwargs) -> Tuple[np.ndarray]:
"""Performs object detection on the given image using the ONNX session.
Args:
img_in (np.ndarray): Input image as a NumPy array.
Returns:
Tuple[np.ndarray]: NumPy array representing the predictions, including boxes, confidence scores, and class confidence scores.
"""
predictions = run_session_via_iobinding(
self.onnx_session, self.input_name, img_in
)[0]
predictions = predictions.transpose(0, 2, 1)
boxes = predictions[:, :, :4]
class_confs = predictions[:, :, 4:]
confs = np.expand_dims(np.max(class_confs, axis=2), axis=2)
predictions = np.concatenate([boxes, confs, class_confs], axis=2)
return (predictions,)
|