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yolo-v3-onnx

Use Case and High-Level Description

YOLO v3 is a real-time object detection model in ONNX* format from the repository which is converted from Keras* model repository using keras2onnx converter. This model was pre-trained on Common Objects in Context (COCO) dataset with 80 classes.

Specification

Metric

Value

Type

Detection

GFLOPs

65.998

MParams

61.930

Source framework

ONNX*

Accuracy

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

Metric

Value

mAP

48.30%

COCO mAP

47.07%

Input

Original model

  1. Image, name - input_1, shape - 1, 3, 416, 416, format is B, C, H, W, where:

    • B - batch size

    • C - channel

    • H - height

    • W - width

    Channel order is RGB. Scale value - 255.

  2. Information of input image size, name: image_shape, shape: 1, 2, format: B, C, where:

    • B - batch size

    • C - vector of 2 values in format H, W, where H is an image height, W is an image width.

Converted model

  1. Image, name - input_1, shape - 1, 3, 416, 416, format is B, C, H, W, where:

    • B - batch size

    • C - channel

    • H - height

    • W - width

    Channel order is BGR.

  2. Information of input image size, name: image_shape, shape: 1, 2, format: B, C, where:

    • B - batch size

    • C - vector of 2 values in format H, W, where H is an image height, W is an image width.

Output

Original model

  1. Boxes coordinates, name - yolonms_layer_1/ExpandDims_1:0, shape - 1, 10647, 4, format - B, N, 4, where:

    • B - batch size

    • N - number of candidates

  2. Scores of boxes per class, name - yolonms_layer_1/ExpandDims_3:0, shape - 1, 80, 10647, format - B, 80, N, where:

    • B - batch size

    • N - number of candidates

  3. Selected indices from the boxes tensor, name - yolonms_layer_1/concat_2:0, shape - 1, 1600, 3, format - B, N, 3, where:

    • B - batch size

    • N - number of detection boxes

Each index has format [b_idx, cls_idx, box_idx], where:

  • b_idx - batch index

  • cls_idx - class_index

  • box_idx- box_index

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

  1. Boxes coordinates, name - yolonms_layer_1/ExpandDims_1:0, shape - 1, 10647, 4, format - B, N, 4, where:

    • B - batch size

    • N - number of candidates

  2. Scores of boxes per class, name - yolonms_layer_1/ExpandDims_3:0, shape - 1, 80, 10647, format - B, 80, N, where:

    • B - batch size

    • N - number of candidates

  3. Selected indices from the boxes tensor, name - yolonms_layer_1/concat_2:0, shape - 1, 1600, 3, format - B, N, 3, where:

    • B - batch size

    • N - number of detection boxes

Each index has format [b_idx, cls_idx, box_idx], where:

  • b_idx - batch index

  • cls_idx - class_index

  • box_idx- box_index

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 OpenVINO™ IR Format

You can download models and if necessary convert them into OpenVINO™ IR 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>

Demo usage

The model can be used in the following demos provided by the Open Model Zoo to show its capabilities:

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