Use Case and High-Level Description¶
This is a retrained version of the Faster R-CNN object detection network trained with the Common Objects in Context (COCO) training dataset. The actual implementation is based on Detectron, with additional network weight pruning applied to sparsify convolution layers (60% of network parameters are set to zeros).
The model input is a blob that consists of a single image of
1, 800, 1280, 3 in the
BGR order. The pixel values are integers in the [0, 255] range.
Mean Average Precision (mAP)
See Average Precision metric description at COCO: Common Objects in Context. The primary challenge metric is used. Tested on the COCO validation dataset.
1, 800, 1280, 3 in the format
B, H, W, C, where:
B- batch size
H- image height
W- image width
C- number of channels
Expected color order is
The net outputs a blob with the shape
1, 1, 100, 7, where each row consists of [
image_id- image ID in the batch
class_id- predicted class ID in range [1, 80], mapping to class names provided in
confidence- [0, 1] detection score; the higher the value, the more confident the detection is
y0) - normalized coordinates of the top left bounding box corner, in the [0, 1] range
y1) - normalized coordinates of the bottom right bounding box corner, in the [0, 1] range
The model can be used in the following demos provided by the Open Model Zoo to show its capabilities:
[*] Other names and brands may be claimed as the property of others.
[**] May be different from the original implementation due to different input configurations.