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darknet yolo cfg 转换到 caffe model

darknect/caffe/convert_tool/readme.md

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darknet yolo cfg 转换到 caffe model

yolov1

转模型文件

python yolo_cfg_to_caffe_prototxt_v1.py yolov1_test.cfg yolov1_caffe_test.prototxt
python yolo_cfg_to_caffe_prototxt_v1.py yolov1_test.cfg yolov1_bn_caffe_test.prototxt

可视化

cd caffe 
python python/draw_net.py models/yolov1_caffe_test.prototxt yolov1_caffenet.png  
open yolov1_caffenet.png

转weight文件

   python yolo_weight_to_caffemodel_v1.py -m yolov1_caffe_test.prototxt -w yolov1.weights -o yolov1.caffemodel

Convert yolo2 model to caffe

convert tiny-yolo from darknet to caffe

1. download tiny-yolo-voc.weights : https://pjreddie.com/media/files/tiny-yolo-voc.weights
https://github.com/pjreddie/darknet/blob/master/cfg/tiny-yolo-voc.cfg
2. python darknet2caffe.py tiny-yolo-voc.cfg tiny-yolo-voc.weights tiny-yolo-voc.prototxt tiny-yolo-voc.caffemodel
3. download voc data and process according to https://github.com/marvis/pytorch-yolo2
python valid.py cfg/voc.data tiny-yolo-voc.prototxt tiny-yolo-voc.caffemodel
4. python scripts/voc_eval.py results/comp4_det_test_
VOC07 metric? Yes
AP for aeroplane = 0.6094
AP for bicycle = 0.6781
AP for bird = 0.4573
AP for boat = 0.3786
AP for bottle = 0.2081
AP for bus = 0.6645
AP for car = 0.6587
AP for cat = 0.6720
AP for chair = 0.3245
AP for cow = 0.4902
AP for diningtable = 0.5549
AP for dog = 0.5905
AP for horse = 0.6871
AP for motorbike = 0.6695
AP for person = 0.5833
AP for pottedplant = 0.2535
AP for sheep = 0.5374
AP for sofa = 0.4878
AP for train = 0.7004
AP for tvmonitor = 0.5754
Mean AP = 0.5391
5. python detect.py tiny-yolo-voc.prototxt tiny-yolo-voc.caffemodel data/dog.jpg

convert tiny-yolo from darknet to caffe without bn

1. python darknet.py tiny-yolo-voc.cfg tiny-yolo-voc.weights tiny-yolo-voc-nobn.cfg tiny-yolo-voc-nobn.weights
2. python darknet2caffe.py tiny-yolo-voc-nobn.cfg tiny-yolo-voc-nobn.weights tiny-yolo-voc-nobn.prototxt tiny-yolo-voc-nobn.caffemodel
3. python valid.py cfg/voc.data tiny-yolo-voc-nobn.prototxt tiny-yolo-voc-nobn.caffemodel
4. python scripts/voc_eval.py results/comp4_det_test_
VOC07 metric? Yes
AP for aeroplane = 0.6094
AP for bicycle = 0.6781
AP for bird = 0.4573
AP for boat = 0.3786
AP for bottle = 0.2081
AP for bus = 0.6645
AP for car = 0.6587
AP for cat = 0.6720
AP for chair = 0.3245
AP for cow = 0.4902
AP for diningtable = 0.5549
AP for dog = 0.5905
AP for horse = 0.6871
AP for motorbike = 0.6695
AP for person = 0.5833
AP for pottedplant = 0.2535
AP for sheep = 0.5374
AP for sofa = 0.4878
AP for train = 0.7004
AP for tvmonitor = 0.5754
Mean AP = 0.5391
5. python detect.py tiny-yolo-voc-nobn.prototxt tiny-yolo-voc-nobn.caffemodel data/dog.jpg

yolov2

参考 

对比 yolov2.cfg  yolov2_caffe.prototxt貌似少了一层卷积
在得到 conv13 卷积层结果之后 需要经过 一层 1*1卷积  64输出的卷积层(BN+SCALE+RELU)
26*26*512 ---->  26*26*64

在经过 passtrough层  变成 13*13*256
再和 conv 20圈基层之后的 13*13*1024  concat  结合 成  13*13*1280

在经过  3*3 1024 输出卷积  和 1*1 425输出((5+80)*5=425)

yolov3