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ssd300

Use Case and High-Level Description

The "ssd300" model is a Single-Shot multibox Detection (SSD) network intended to perform detection. This model is implemented using the Caffe* framework. For details about this model, check out the repository.

The model input is a blob that consists of a single image of 1x3x300x300 in BGR order. The BGR mean values need to be subtracted as follows: [104.0,117.0,123.0] before passing the image blob into the network.

The model output is a typical vector containing the tracked object data, as previously described.

Example

See here.

Specification

Metric Value
Type Detection
GFLOPs 62.815
MParams 26.285
Source framework Caffe*

Accuracy

Metric Value
mAP 85.0791%

See here.

Performance

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, 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
  • 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])

Converted model

The array of detection summary info, name - detection_out, shape - 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
  • 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])

Legal Information

The original model is distributed under the following license:

COPYRIGHT

All new contributions compared to the original branch:
Copyright (c) 2015, 2016 Wei Liu (UNC Chapel Hill), Dragomir Anguelov (Zoox),
Dumitru Erhan (Google), Christian Szegedy (Google), Scott Reed (UMich Ann Arbor),
Cheng-Yang Fu (UNC Chapel Hill), Alexander C. Berg (UNC Chapel Hill).
All rights reserved.

All contributions by the University of California:
Copyright (c) 2014, 2015, The Regents of the University of California (Regents)
All rights reserved.

All other contributions:
Copyright (c) 2014, 2015, the respective contributors
All rights reserved.

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