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mobilenet-ssd

Use Case and High-Level Description

The mobilenet-ssd model is a Single-Shot multibox Detection (SSD) network intended to perform object 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, also like the densenet-121 model. The BGR mean values need to be subtracted as follows: [127.5, 127.5, 127.5] before passing the image blob into the network. In addition, values must be divided by 0.007843.

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

Example

Specification

Metric Value
Type Detection
GFLOPs 2.316
MParams 5.783
Source framework Caffe*

Accuracy

See https://github.com/chuanqi305/MobileNet-SSD.

Performance

Input

Original model

Image, name - prob, 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 - [127.5, 127.5, 127.5], scale value - 127.50223128904757.

Converted model

Image, name - prob, 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

https://raw.githubusercontent.com/tensorflow/models/master/LICENSE