Skip to content

Latest commit

 

History

History
104 lines (71 loc) · 3.96 KB

README.md

File metadata and controls

104 lines (71 loc) · 3.96 KB

pelee-coco

Use Case and High-Level Description

The Pelee is a Real-Time Object Detection System on Mobile Devices based on Single Shot Detection approach. The model is implemented using the Caffe* framework and trained on Common Objects in Context (COCO) dataset. For details about this model, check out the repository.

Specification

Metric Value
Type Detection
GFLOPs 1,290
MParams 5.98
Source framework Caffe*

Accuracy

Metric Value
coco_precision 21.9761%

See here.

Input

Original model

Image, name - data, shape - 1, 3, 304, 304, format is B, C, H, W, where:

  • B - batch size
  • C - channel
  • H - height
  • W - width

Channel order is BGR. Mean values - [103.94, 116.78, 123.68], Scale - 58.8235.

Converted model

Image, name - data, shape - 1, 3, 304, 304, 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 in range [1, 80], mapping to class names provided in <omz_dir>/data/dataset_classes/coco_80cl.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])

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 in range [1, 80], mapping to class names provided in <omz_dir>/data/dataset_classes/coco_80cl_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 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:

Legal Information

The original model is distributed under the Apache License, Version 2.0. A copy of the license is provided in <omz_dir>/models/public/licenses/APACHE-2.0.txt.

[*] Other names and brands may be claimed as the property of others.