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yolox-tiny

Use Case and High-Level Description

The yolox-tiny is a tiny version of YOLOX models family for object detection tasks. YOLOX is an anchor-free version of YOLO, with a simpler design but better performance.This model was pre-trained on Common Objects in Context (COCO) dataset with 80 classes.

More details provided in the paper and repository.

Specification

Metric Value
Type Object detection
GFLOPs 6.4813
MParams 5.0472
Source framework PyTorch*

Accuracy

Accuracy metrics obtained on Common Objects in Context (COCO) validation dataset for converted model.

Metric Value
mAP 47.85%
COCO mAP (0.5) 52.56%
COCO mAP (0.5:0.05:0.95) 31.82%

Input

Original model

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

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

Expected color order is RGB.

Mean values - [123.675, 116.28, 103.53]. Scale values - [58.395, 57.12, 57.375].

Converted model

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

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

Expected color order - BGR.

Output

Original model

The array of detection summary info, name - output, shape - 1, 3549, 85, format is B, N, 85, where:

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

Detection box has format [x, y, h, w, box_score, class_no_1, ..., class_no_80], 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_80 - probability distribution over the classes in logits format.

The model was trained on Common Objects in Context (COCO) dataset version with 80 categories of object. Mapping to class names provided in <omz_dir>/data/dataset_classes/coco_80cl.txt file.

Converted model

The array of detection summary info, name - output, shape - 1, 3549, 85, format is B, N, 85, where:

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

Detection box has format [x, y, h, w, box_score, class_no_1, ..., class_no_80], 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_80 - probability distribution over the classes in logits format.

The model was trained on Common Objects in Context (COCO) dataset version with 80 categories of object. Mapping to class names provided in <omz_dir>/data/dataset_classes/coco_80cl.txt file.

Download a Model and Convert it into Inference Engine Format

You can download models and if necessary convert them into Inference Engine format using the Model Downloader and other automation tools as shown in the examples below.

An example of using the Model Downloader:

omz_downloader --name <model_name>

An example of using the Model Converter:

omz_converter --name <model_name>

Legal Information

The original model is distributed under the Apache License, Version 2.0. A copy of the license is provided in <omz_dir>/models/public/licenses/APACHE-2.0-PyTorch-YOLOX.txt.