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

20.73%

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: