ctpn¶

Use Case and High-Level Description¶

Detecting Text in Natural Image with Connectionist Text Proposal Network. For details see paper.

Metric

Value

Type

Object detection

GFlops

55.813

MParams

17.237

Source framework

TensorFlow*

Metric

Value

hmean

73.67%

Input¶

Original Model¶

Image, name: image_tensor, shape: 1, 600, 600, 3, format: B, H, W, C, where:

• B - batch size

• H - image height

• W - image width

• C - number of channels

Expected color order: BGR. Mean values: [102.9801, 115.9465, 122.7717].

Converted Model¶

Image, name: Placeholder, shape: 1, 3, 600, 600, format: B, C, H, W, where:

• B - batch size

• C - number of channels

• H - image height

• W - image width

Expected color order: BGR.

Output¶

Original Model¶

1. Detection boxes, name: rpn_bbox_pred/Reshape_1, contains predicted regions, in format B, H, W, A, where:

• B - batch size

• H - image height

• W - image width

• A - vector of 4*N coordinates, where N is the number of detected anchors.

2. Probability, name: Reshape_2, contains probabilities for predicted regions in a [0,1] range in format B, H, W, A, where:

• B - batch size

• H - image height

• W - image width

• A - vector of 4*N coordinates, where N is the number of detected anchors.

Converted Model¶

1. Detection boxes, name: rpn_bbox_pred/Reshape_1/Transpose, shape: 1, 40, 18, 18 contains predicted regions, format: B, A, H, W, where:

• B - batch size

• A - vector of 4*N coordinates, where N is the number of detected anchors.

• H - image height

• W - image width

2. Probability, name: Reshape_2/Transpose, shape: 1, 20, 18, 18, contains probabilities for predicted regions in a[0,1] range in format B, A, H, W, where:

• B - batch size

• A - vector of 2*N class probabilities (0 class for background, 1 class for text), where N is the number of detected anchors.

• H - image height

• W - image width

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.

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¶

Detecting Text in Natural Image with Connectionist Text Proposal Network. For details see paper.

Metric

Value

Type

Object detection

GFlops

55.813

MParams

17.237

Source framework

TensorFlow*

Metric

Value

hmean

73.67%

Input¶

Original Model¶

Image, name: image_tensor, shape: 1, 600, 600, 3, format: B, H, W, C, where:

• B - batch size

• H - image height

• W - image width

• C - number of channels

Expected color order: BGR. Mean values: [102.9801, 115.9465, 122.7717].

Converted Model¶

Image, name: Placeholder, shape: 1, 3, 600, 600, format: B, C, H, W, where:

• B - batch size

• C - number of channels

• H - image height

• W - image width

Expected color order: BGR.

Output¶

Original Model¶

1. Detection boxes, name: rpn_bbox_pred/Reshape_1, contains predicted regions, in format B, H, W, A, where:

• B - batch size

• H - image height

• W - image width

• A - vector of 4*N coordinates, where N is the number of detected anchors.

2. Probability, name: Reshape_2, contains probabilities for predicted regions in a [0,1] range in format B, H, W, A, where:

• B - batch size

• H - image height

• W - image width

• A - vector of 4*N coordinates, where N is the number of detected anchors.

Converted Model¶

1. Detection boxes, name: rpn_bbox_pred/Reshape_1/Transpose, shape: 1, 40, 18, 18 contains predicted regions, format: B, A, H, W, where:

• B - batch size

• A - vector of 4*N coordinates, where N is the number of detected anchors.

• H - image height

• W - image width

2. Probability, name: Reshape_2/Transpose, shape: 1, 20, 18, 18, contains probabilities for predicted regions in a[0,1] range in format B, A, H, W, where:

• B - batch size

• A - vector of 2*N class probabilities (0 class for background, 1 class for text), where N is the number of detected anchors.

• H - image height

• W - image width

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.

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¶

MIT License

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