ssd-resnet-34-1200-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 pretrained in PyTorch* framework and converted to ONNX* format. For additional information refer to repository.

Example

Specification

Metric Value
Type Detection
GFLOPs 433.411
MParams 20.058
Source framework PyTorch*

Accuracy

Metric Value
coco_precision 20.7198%
mAP 39.2752%

Performance

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:

Channel order is RGB.

Converted model

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

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. The model was trained on Microsoft* COCO dataset version with 80 categories of object.
  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. The model was trained on Microsoft* COCO dataset version with 80 categories of object.
  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].

Legal Information

The original model is distributed under the Apache License, Version 2.0. A copy of the license is provided in APACHE-2.0-MLPerf.txt.