smartlab-object-detection-0004#
Use Case and High-Level Description#
This is a smartlab object detector that is based on YoloX for 416x416 resolution.
Example#
Specification#
Accuracy metrics obtained on Smartlab validation dataset with yolox adapter for converted model.
Metric |
Value |
---|---|
[COCO mAP (0.5:0.05:0.95)] |
11.18% |
GFlops |
1.073 |
MParams |
0.8894 |
Source framework |
PyTorch* |
Average Precision (AP) is defined as an area under the precision/recall curve.
Inputs#
Image, name: images
, shape: 1, 3, 416, 416
in the format B, C, H, W
, where:
B
- batch sizeC
- number of channelsH
- image heightW
- image width
Expected color order is BGR
.
Outputs#
The array of detection summary info, name - output
, shape - 1, 3549, 8
, format is B, N, 8
, where:
B
- batch sizeN
- number of detection boxes
Detection box has format [x
, y
, h
, w
, box_score
, class_no_1
, …, class_no_3
], where:
(
x
,y
) - raw coordinates of box centerh
,w
- raw height and width of boxbox_score
- confidence of detection boxclass_no_1
, …,class_no_3
- probability distribution over the classes in logits format.
Legal Information#
[*] Other names and brands may be claimed as the property of others.