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ssd-resnet34-1200-onnx

Use Case and High-Level Description

The ssd-resnet-34-1200-onnx model is a multiscale SSD based on ResNet-34 backbone network intended to perform object detection. The model has been trained from the Common Objects in Context (COCO) image dataset. This model is pre-trained in PyTorch* framework and converted to ONNX* format. For additional information refer to repository.

Specification

Metric

Value

Type

Detection

GFLOPs

433.411

MParams

20.058

Source framework

PyTorch*

Accuracy

Metric

Value

coco_precision

20.7198%

mAP

39.2752%

Input

Note that original model expects image in RGB format, converted model - in BGR format.

Original model

Image, shape - 1, 3, 1200, 1200, format is B, C, H, W, where:

  • B - batch size

  • C - channel

  • H - height

  • W - width

Channel order is RGB.

Converted model

Image, shape - 1, 3, 1200, 1200, format is B, C, H, W, where:

  • B - batch size

  • C - channel

  • H - height

  • W - width

Channel order is BGR.

Output

NOTE output format changes after Model Optimizer conversion. To find detailed explanation of changes, go to Model Optimizer development guide

Original model

  1. Classifier, name - labels, shape - 1, N, contains predicted classes for each detected bounding box in [1, 81] range. The model was trained on Common Objects in Context (COCO) dataset version with 80 categories of object, 0 class is for background. Mapping to class names provided in <omz_dir>/data/dataset_classes/coco_80cl_bkgr.txt file

  2. Probability, name - scores, shape - 1, N, contains confidence of each detected bounding boxes.

  3. Detection boxes, name - bboxes, shape - 1, N, 4, contains detection boxes coordinates in format [y_min, x_min, y_max, x_max], where (x_min, y_min) are coordinates top left corner, (x_max, y_max) are coordinates right bottom corner. Coordinates are rescaled to input image size.

Converted model

  1. Classifier, shape - 1, 200, contains predicted class ID for each detected bounding box in [1, 81] range. The model was trained on Common Objects in Context (COCO) dataset version with 80 categories of object, 0 class is for background. Mapping to class names provided in <omz_dir>/data/dataset_classes/coco_80cl_bkgr.txt file

  2. Probability, shape - 1, 200, contains confidence of each detected bounding boxes.

  3. Detection boxes, shape - 1, 200, 4, contains detection boxes coordinates in format [y_min, x_min, y_max, x_max], where (x_min, y_min) are coordinates top left corner, (x_max, y_max) are coordinates right bottom corner. Coordinates are in normalized format, in range [0, 1].

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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