# YOLO in caffe
Update 12-05-2016: Currently, only yolo v1 (http://pjreddie.com/darknet/yolov1/) is supported. Yolo V2 (http://pjreddie.com/darknet/yolo/) is not supported. Batch norm layer is supported.
This is a caffe implementation of the YOLO:Real-Time Object Detection
Note, the caffe models are not trained in caffe, but converted from darknet's (.weight) files (http://pjreddie.com/darknet/yolov1/).
The converter is consisted of four steps:
first, you need to download the pretrained yolo weight files (http://pjreddie.com/darknet/yolov1/) and .cfg files (https://github.com/pjreddie/darknet/tree/master/cfg/yolov1)
run create_yolo_prototxt.py to create .prototxt files
after that, run create_yolo_caffemodel.py to create the caffemodel from yolo's (.weight) files
replace train_val_prototxt.filename with /your/path/to/yolo_train_val.prototxt (yolo_small, yolo_tiny), yoloweights_filename with /your/path/to/yolo.weights (yolo-small, yolo-tiny), and caffemodel_filename with your output caffemodel name,
e.g. "python create_yolo_caffemodel.py -m yolo_train_val.prototxt -w yolo.weights -o yolo.caffemodel"
run yolo_main.py to do yolo object detection for the input image
replace model_filename with /your/path/to/yolo_small_deploy.prototxt or yolo_tiny_deploy.prototxt, weight_filename with /your/path/to/yolo_tiny.caffemodel or yolo_small.caffemodel and image_filename with the target image file
Caffe, pycaffe
Opencv2
According to the LICENSE file of the original code,
Me and original author hold no liability for any damages
Do not use this on commercial!