README.md

smartlab-object-detection-0002

Use Case and High-Level Description

This is a smartlab object detector that is based on YoloX-nano 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)] | 6.06% | | GFlops | 1.073 | | MParams | 0.8894 | | 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, 8, format is B, N, 8, where:

• B - batch size
• N - 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 box
• box_score - confidence of detection box
• class_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.