This is a smartlab object detector that is based on YoloX for 416x416 resolution.
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.
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
.
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 center h
,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.
[*] Other names and brands may be claimed as the property of others.