ssd300

Use Case and High-Level Description

The ssd300 model is the Caffe* framework implementation of Single-Shot multibox Detection (SSD) algorithm with 300x300 input resolution and VGG-16 backbone. The network intended to perform visual object detection. This model is pretrained on VOC2007 + VOC2012 + COCO dataset and is able to detect 20 PASCAL VOC2007 object classes:

  • Person: person

  • Animal: bird, cat, cow, dog, horse, sheep

  • Vehicle: aeroplane, bicycle, boat, bus, car, motorbike, train

  • Indoor: bottle, chair, dining table, potted plant, sofa, tv/monitor

Mapping model labels to class names provided in <omz_dir>/data/dataset_classes/voc_20cl_bkgr.txt file.

For details about this model, check out the repository.

Example

See here.

Specification

Metric

Value

Type

Detection

GFLOPs

62.815

MParams

26.285

Source framework

Caffe*

Accuracy

The accuracy results were obtained on test data from VOC2007 dataset.

Metric

Value

mAP

87.09%

Input

Original model

Image, name - data, shape - 1, 3, 300, 300, format is B, C, H, W, where:

  • B - batch size

  • C - channel

  • H - height

  • W - width

Channel order is BGR. Mean values - [104.0, 117.0, 123.0]

Converted model

Image, name - data, shape - 1, 3, 300, 300, format is B, C, H, W, where:

  • B - batch size

  • C - channel

  • H - height

  • W - width

Channel order is BGR.

Output

Original model

The array of detection summary info, name - detection_out, shape - 1, 1, 200, 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 (1..20 - PASCAL VOC defined class ids). Mapping to class names provided by <omz_dir>/data/dataset_classes/voc_20cl_bkgr.txt file.

  • conf - confidence for the predicted class, in [0, 1] range

  • (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])

Converted model

The array of detection summary info, name - detection_out, shape - 1, 1, 200, 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 (1..20 - PASCAL VOC defined class ids). Mapping to class names provided by <omz_dir>/data/dataset_classes/voc_20cl_bkgr.txt file.

  • conf - confidence for the predicted class in [0, 1] range

  • (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 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: