Use Case and High-Level Description

This is a reimplemented and retrained version of the YOLO v2 object detection network trained with the VOC2012 training dataset. Network weight pruning is applied to sparsify convolution layers (70% of network parameters are set to zeros).



Metric Value
Mean Average Precision (mAP) 62.9%
Flops 48.29Bn*
Source framework TensorFlow*

For Average Precision metric description, see The PASCAL Visual Object Classes (VOC) Challenge. Tested on the VOC 2012 validation dataset.



Name: input, shape: [1x3x416x416] - An input image in the format [BxCxHxW], where:


The net outputs a blob with the shape [1, 21125], which can be reshaped to [5, 25, 13, 13], where each number corresponds to [num_anchors, cls_reg_obj_params, y_loc, x_loc] respectively:

Legal Information

[*] Same as the original implementation.

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