ctdet_coco_dlav0_384

Use Case and High-Level Description

CenterNet object detection model ctdet_coco_dlav0_384 originally trained with PyTorch*. CenterNet models an object as a single point - the center point of its bounding box and uses keypoint estimation to find center points and regresses to object size. For details see paper, repository.

Specification

Metric

Value

Type

Detection

GFlops

34.994

MParams

17.911

Source framework

PyTorch*

Accuracy

Metric

Original model

Converted model

mAP

41.81%

41.61%

Input

Original Model

Image, name: input.1, shape: 1, 3, 384, 384, format: B, C, H, W, where:

  • B - batch size

  • C - number of channels

  • H - image height

  • W - image width

Expected color order: BGR. Mean values: [104.04, 113.985, 119.85], scale values: [73.695, 69.87, 70.89].

Converted Model

Image, name: input.1, shape: 1, 3, 384, 384, format: B, C, H, W, where:

  • B - batch size

  • C - number of channels

  • H - image height

  • W - image width

Expected color order: BGR.

Output

  1. Object center points heatmap, name: center_heatmap. Contains predicted objects center point, for each of the 80 categories, according to Common Objects in Context (COCO) dataset version with 80 categories of objects, without background label, mapping to class names provided in <omz_dir>/data/dataset_classes/coco_80cl.txt file.

  2. Object size output, name: width_height. Contains predicted width and height for each object.

  3. Regression output, name: regression. Contains offsets for each prediction.

Download a Model and Convert it into Inference Engine Format

You can download models and if necessary convert them into Inference Engine format using the Model Downloader and other automation tools as shown in the examples below.

An example of using the Model Downloader:

python3 <omz_dir>/tools/downloader/downloader.py --name <model_name>

An example of using the Model Converter:

python3 <omz_dir>/tools/downloader/converter.py --name <model_name>

Use Case and High-Level Description

CenterNet object detection model ctdet_coco_dlav0_384 originally trained with PyTorch*. CenterNet models an object as a single point - the center point of its bounding box and uses keypoint estimation to find center points and regresses to object size. For details see paper, repository.

Specification

Metric

Value

Type

Detection

GFlops

34.994

MParams

17.911

Source framework

PyTorch*

Accuracy

Metric

Original model

Converted model

mAP

41.81%

41.61%

Input

Original Model

Image, name: input.1, shape: 1, 3, 384, 384, format: B, C, H, W, where:

  • B - batch size

  • C - number of channels

  • H - image height

  • W - image width

Expected color order: BGR. Mean values: [104.04, 113.985, 119.85], scale values: [73.695, 69.87, 70.89].

Converted Model

Image, name: input.1, shape: 1, 3, 384, 384, format: B, C, H, W, where:

  • B - batch size

  • C - number of channels

  • H - image height

  • W - image width

Expected color order: BGR.

Output

  1. Object center points heatmap, name: center_heatmap. Contains predicted objects center point, for each of the 80 categories, according to Common Objects in Context (COCO) dataset version with 80 categories of objects, without background label, mapping to class names provided in <omz_dir>/data/dataset_classes/coco_80cl.txt file.

  2. Object size output, name: width_height. Contains predicted width and height for each object.

  3. Regression output, name: regression. Contains offsets for each prediction.

Download a Model and Convert it into Inference Engine Format

You can download models and if necessary convert them into Inference Engine format using the Model Downloader and other automation tools as shown in the examples below.

An example of using the Model Downloader:

python3 <omz_dir>/tools/downloader/downloader.py --name <model_name>

An example of using the Model Converter:

python3 <omz_dir>/tools/downloader/converter.py --name <model_name>

Legal Information

The original model is distributed under the following license

MIT License

Copyright (c) 2019 Xingyi Zhou
All rights reserved.

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.