README.md

vehicle-detection-0201

Use Case and High-Level Description

This is a vehicle detector that is based on MobileNetV2 backbone with two SSD heads from 1/16 and 1/8 scale feature maps and clustered prior boxes for 384x384 resolution.

Example

Specification

Metric	Value
AP @ [ IoU=0.50:0.95 ]	0.322 (internal test set)
GFlops	1.768
MParams	1.817
Source framework	PyTorch*

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

Inputs

Image, name: image, shape: 1, 3, 384, 384 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

The net outputs blob with shape: 1, 1, 200, 7 in the format 1, 1, N, 7, where N is the number of detected bounding boxes. Each detection 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 (0 - vehicle)
• conf - confidence for the predicted class
• (x_min, y_min) - coordinates of the top left bounding box corner
• (x_max, y_max) - coordinates of the bottom right bounding box corner

Training Pipeline

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

Demo usage

The model can be used in the following demos provided by the Open Model Zoo to show its capabilities:

• Object Detection C++ Demo
• Object Detection Python* Demo

Legal Information

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