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smartlab-object-detection-0003

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)] 30.38%
GFlops 1.077
MParams 0.8908
Source framework PyTorch*

Inputs

Image, name: images, shape: 1, 3, 416, 416 in the format B, C, H, W, where:

  • B - batch size
  • C - number of channels
  • H - image height
  • W - image width

Expected color order is BGR.

Outputs

The array of detection summary info, name - output, shape - 1, 3549, 15, format is B, N, 15, where:

  • B - batch size
  • N - number of detection boxes

Detection box has format [x, y, h, w, box_score, class_no_1, ..., class_no_10], where:

  • (x, y) - raw coordinates of box center
  • h, w - raw height and width of box
  • box_score - confidence of detection box
  • class_no_1, ..., class_no_10 - probability distribution over the classes in logits format.

Legal Information

[*] Other names and brands may be claimed as the property of others.