This is a retrained version of the Faster R-CNN object detection network trained with the 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 1x3x800x1280 in the BGR order. The pixel values are integers in the [0, 255] range.
| Metric | Value |
|---|---|
| Mean Average Precision (mAP) | 38.74%** |
| Flops | 364.21Bn |
| MParams | 52.79 |
| Source framework | TensorFlow* |
See Average Precision metric description at COCO: Common Objects in Context. The primary challenge metric is used. Tested on the COCO validation dataset.
Name: input, shape: [1x3x800x1280] - An input image in the format [BxCxHxW],
where:
- B - batch size
- C - number of channels
- H - image height
- W - image width Expected color order is BGR.
The net outputs a blob with the shape [300, 7], where each row consists of [image_id, class_id, confidence, x0, y0, x1, y1] respectively:
image_id- image ID in the batchclass_id- predicted class IDconfidence- [0, 1] detection score; the higher the value, the more confident the detection is- (
x0,y0) - normalized coordinates of the top left bounding box corner, in the [0, 1] range - (
x1,y1) - normalized coordinates of the bottom right bounding box corner, in the [0, 1] range
[*] Other names and brands may be claimed as the property of others.
[**] May be different from the original implementation due to different input configurations.