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 MSCOCO* 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, 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 Apache License, Version 2.0. A copy of the license is provided in APACHE-2.0.txt.

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