person-vehicle-bike-detection-2004

Use Case and High-Level Description

This is a person, vehicle, bike detector that is based on MobileNetV2 backbone with ATSS head for 448x256 resolution.

Example

_images/person-vehicle-bike-detection-2004.png

Specification

Metric

Value

AP @ [ IoU=0.50:0.95 ]

0.274 (internal test set)

GFlops

1.811

MParams

2.327

Source framework

PyTorch*

Average Precision (AP) is defined as an area under the precision/recall curve.

Inputs

Image, name: input, shape: 1, 3, 256, 448 in the format B, C, H, W, where:

  • B - batch size

  • C - number of channels

  • H - image height

  • W - image width

Expected color order is BGR.

Outputs

  1. The boxes is a blob with the shape 100, 5 in the format N, 5, where N is the number of detected bounding boxes. For each detection, the description has the format: [x_min, y_min, x_max, y_max, conf], where:

    • (x_min, y_min) - coordinates of the top left bounding box corner

    • (x_max, y_max) - coordinates of the bottom right bounding box corner

    • conf - confidence for the predicted class

  2. The labels is a blob with the shape 100 in the format N, where N is the number of detected bounding boxes. The value of each label is equal to predicted class ID (0 - vehicle, 1 - person, 2 - non-vehicle).

Training Pipeline

The OpenVINO Training Extensions provide a training pipeline, allowing to fine-tune the model on custom dataset.

Use Case and High-Level Description

This is a person, vehicle, bike detector that is based on MobileNetV2 backbone with ATSS head for 448x256 resolution.

Example

_images/person-vehicle-bike-detection-2004.png

Specification

Metric

Value

AP @ [ IoU=0.50:0.95 ]

0.274 (internal test set)

GFlops

1.811

MParams

2.327

Source framework

PyTorch*

Average Precision (AP) is defined as an area under the precision/recall curve.

Inputs

Image, name: input, shape: 1, 3, 256, 448 in the format B, C, H, W, where:

  • B - batch size

  • C - number of channels

  • H - image height

  • W - image width

Expected color order is BGR.

Outputs

  1. The boxes is a blob with the shape 100, 5 in the format N, 5, where N is the number of detected bounding boxes. For each detection, the description has the format: [x_min, y_min, x_max, y_max, conf], where:

    • (x_min, y_min) - coordinates of the top left bounding box corner

    • (x_max, y_max) - coordinates of the bottom right bounding box corner

    • conf - confidence for the predicted class

  2. The labels is a blob with the shape 100 in the format N, where N is the number of detected bounding boxes. The value of each label is equal to predicted class ID (0 - vehicle, 1 - person, 2 - non-vehicle).

Training Pipeline

The OpenVINO Training Extensions provide a training pipeline, allowing to fine-tune the model on custom dataset.

Legal Information

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