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*添加基于ncnn的106关键点 C sample:https://github.com/dog-qiuqiu/MobileNet-Yolo/tree/master/sample/ncnn
Network | VOC mAP(0.5) | COCO mAP(0.5) | Resolution | Inference time (NCNN/Kirin 990) | Inference time (MNN arm82/Kirin 990) | FLOPS | Weight size |
---|---|---|---|---|---|---|---|
MobileNetV2-YOLOv3-Lite(our) | 73.26 | 37.44 | 320 | 28.42 ms | 18 ms | 1.8BFlops | 8.0MB |
MobileNetV2-YOLOv3-Nano(our) | 65.27 | 30.13 | 320 | 10.16 ms | 5 ms | 0.5BFlops | 3.0MB |
MobileNetV2-YOLOv3 | 70.7 | & | 352 | 32.15 ms | & ms | 2.44BFlops | 14.4MB |
MobileNet-SSD | 72.7 | & | 300 | 26.37 ms | & ms | & BFlops | 23.1MB |
YOLOv5s | & | 56.2 | 416 | 150.5 ms | & ms | 13.2BFlops | 28.1MB |
YOLOv3-Tiny-Prn | & | 33.1 | 416 | 36.6 ms | & ms | 3.5BFlops | 18.8MB |
YOLOv4-Tiny | & | 40.2 | 416 | 44.6 ms | & ms | 6.9BFlops | 23.1MB |
YOLO-Nano | 69.1 | & | 416 | & ms | & ms | 4.57BFlops | 4.0MB |
Network | Resolution | VOC mAP(0.5) | Inference time (DarkNet/i7-6700) | Inference time (NCNN/Kirin 990) | Inference time (MNN arm82/Kirin 990) | FLOPS | Weight size |
---|---|---|---|---|---|---|---|
MobileNetV2-YOLOv3-Fastest | 320 | 46.55 | 26 ms | 8.2 ms | 2.4 ms | 0.13BFlops | 700KB |
MobileNetV2-YOLOv3-Fastest-v2 | 320 | 50.13 | 27 ms | & ms | & ms | 0.14BFlops | 820KB |
Network | Resolution | Inference time (NCNN/Kirin 990) | Inference time (MNN arm82/Kirin 990) | FLOPS | Weight size |
---|---|---|---|---|---|
UltraFace-version-RFB | 320x240 | &ms | 3.36ms | 0.1BFlops | 1.3MB |
UltraFace-version-Slim | 320x240 | &ms | 3.06ms | 0.1BFlops | 1.2MB |
yoloface-500k | 320x256 | 5.5ms | 2.4ms | 0.1BFlops | 0.52MB |
yoloface-500k-v2 | 352x288 | 4.7ms | &ms | 0.1BFlops | 0.42MB |
Model | Easy Set | Medium Set | Hard Set |
---|---|---|---|
libfacedetection v1(caffe) | 0.65 | 0.5 | 0.233 |
libfacedetection v2(caffe) | 0.714 | 0.585 | 0.306 |
Retinaface-Mobilenet-0.25 (Mxnet) | 0.745 | 0.553 | 0.232 |
version-slim-320 | 0.77 | 0.671 | 0.395 |
version-RFB-320 | 0.787 | 0.698 | 0.438 |
yoloface-500k-320 | 0.728 | 0.682 | 0.431 |
yoloface-500k-352-v2 | 0.768 | 0.729 | 0.490 |
Network | Resolution | Inference time (NCNN/Kirin 990) | Inference time (MNN arm82/Kirin 990) | Inference time (DarkNet/R3-3100) | FLOPS | Weight size |
---|---|---|---|---|---|---|
yoloface-50k | 56x56 | 0.27ms | 0.31ms | 0.5 ms | 0.001BFlops | 46kb |
Network | Resolution | Inference time (NCNN/Kirin 990) | Inference time (MNN arm82/Kirin 990) | Weight size |
---|---|---|---|---|
landmark106 | 112x112 | 0.6ms | 0.5ms | 1.4MB |
cd darknet2caffe/
python darknet2caffe.py MobileNetV2-YOLOv3-Nano-voc.cfg MobileNetV2-YOLOv3-Nano-voc.weights MobileNetV2-YOLOv3-Nano-voc.prototxt MobileNetV2-YOLOv3-Nano-voc.caffemodel
cp MobileNetV2-YOLOv3-Nano-voc.prototxt sample
cp MobileNetV2-YOLOv3-Nano-voc.caffemodel sample
cd sample
python detector.py
#layer {
# bottom: "layer71-route"
# top: "layer72-upsample"
# name: "layer72-upsample"
# type: "Upsample"
# upsample_param {
# scale: 2
# }
#}
layer {
bottom: "layer71-route"
top: "layer72-upsample"
name: "layer72-upsample"
type: "Interp"
interp_param {
height:20 #upsample h size
width:20 #upsample w size
}
}
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