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ssd_resnet50_v1_fpn_coco

Use Case and High-Level Description

The ssd_resnet50_v1_fpn_coco model is a SSD FPN object detection architecture based on ResNet-50. The model has been trained from the Common Objects in Context (COCO) image dataset. For details see the repository and paper.

Specification

Metric

Value

Type

Detection

GFLOPs

178.6807

MParams

56.9326

Source framework

TensorFlow*

Accuracy

Metric

Value

coco_precision

38.4557%

Input

Original model

Image, name - image_tensor, shape - 1, 640, 640, 3, format - B, H, W, C, where:

  • B - batch size

  • H - height

  • W - width

  • C - channel

Expected color order - RGB.

Converted model

Image, name - image_tensor, shape - 1, 3, 640, 640, format is B, C, H, W, where:

  • B - batch size

  • C - channel

  • H - height

  • W - width

Expected color order - BGR.

Output

Note

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 - detection_classes, contains predicted bounding boxes classes in range [1, 91]. The model was trained on Common Objects in Context (COCO) dataset version with 91 categories of object, 0 class is for background. Mapping to class names provided in <omz_dir>/data/dataset_classes/coco_91cl_bkgr.txt file

  2. Probability, name - detection_scores, contains probability of detected bounding boxes.

  3. Detection box, name - detection_boxes, 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.

  4. Detections number, name - num_detections, contains the number of predicted detection boxes.

Converted model

The array of summary detection information, name - detection_out, shape - 1, 1, 100, 7 in the format 1, 1, N, 7, where N is the number of detected bounding boxes. For each detection, the description has the format: [image_id, label, conf, x_min, y_min, x_max, y_max], where:

  • image_id - ID of the image in the batch

  • label - predicted class ID in range [1, 91], mapping to class names provided in <omz_dir>/data/dataset_classes/coco_91cl_bkgr.txt file

  • conf - confidence for the predicted class

  • (x_min, y_min) - coordinates of the top left bounding box corner (coordinates are in normalized format, in range [0, 1])

  • (x_max, y_max) - coordinates of the bottom right bounding box corner (coordinates are in normalized format, in range [0, 1])

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:

python3 <omz_dir>/tools/downloader/downloader.py --name <model_name>

An example of using the Model Converter:

python3 <omz_dir>/tools/downloader/converter.py --name <model_name>
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