henryliang105/yolov3-caffe

A YOLOv3 model in caffe

YOLOv3-caffe

usage:

python detect_one.py --image=images/dog-cycle-car.png

Acknowledgements

The major part is adapted from Ayoosh Kathuria's

amazing tutorial on YOLOv3 implementation in pytorch.

The weight files can be downloaded from YOLOv3-caffe. (ignore the prototxt file with interp layer, explanation below)

I also made a version where batchnorm computation is merged into convolution. The model can be downloaded from YOLOv3-caffe-mergebn

For people outside China, you can download from googledrive YOLOv3-caffe

Notes

A simplest YOLOv3 model in caffe for python3.

This is merely a practice project. Note that I implemented an interp layer in python for compatibility.

This is because interp layer is only viable in deeplab caffe, not in the official one.

Moreover, the behavior of interp layer in deeplab is different from torch.nn.UpsamplingBilinear2d,

in a sense that pytorch rescales (3,13,13) to (3,26,26) with a factor of 2, but deeplab caffe

rescales it to (3,25,25) with the same factor. This causes weird performance degradation.

To make the model dependent on C only, you need to customize the interp layer.

In deeplab version, note in interp_layer.cpp, you need to fix the logic (take an example):

height_out_ = height_in_eff_ + (height_in_eff_ - 1) * (zoom_factor - 1)

into

height_out_ = height_in_eff_ * zoom_factor