2021.2.6 此项目不再更新，新项目地址: Yolo-Fastest： Faster and stronger https://github.com/dog-qiuqiu/Yolo-Fastest
- Yolo-Fastest： Faster and stronger https://github.com/dog-qiuqiu/Yolo-Fastest
- 此表中NCNN基准未更新最新ARM82数据，最新版本NCNN理论上ARM82会有一倍速度提升，待更新...
- 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 |
- Support mobile inference frameworks such as NCNN&MNN
- The mnn benchmark only includes the forward inference time
- The ncnn benchmark is the forward inference time + post-processing time(NMS...) of the convolution feature map.
- Darknet Train Configuration: CUDA-version: 10010 (10020), cuDNN: 7.6.4,OpenCV version: 4 GPU:RTX2080ti
- A ultra-lightweight human body posture key point prediction model designed for mobile devices, which can cooperate with MobileNetV2-YOLOv3-Nano to complete the human body posture estimation task
- https://github.com/dog-qiuqiu/Ultralight-SimplePose
| 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 |
- 都500k了，要啥mAP:sunglasses:
- Inference time (DarkNet/i7-6700):13ms
- The mnn benchmark only includes the forward inference time
- The ncnn benchmark is the forward inference time + post-processing time(NMS...) of the convolution feature map.
| 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 |
- yoloface-500k-v2：The SE&CSP module is added
- V2 does not support MNN temporarily
- wider_face_val(ap05): yoloface-500k: 53.75 yoloface-500k-v2: 56.69
| 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 |
- For the close-range face detection model in a specific scene, the recommended detection distance is 1.5m
| Network | Resolution | Inference time (NCNN/Kirin 990) | Inference time (MNN arm82/Kirin 990) | Weight size |
|---|---|---|---|---|
| landmark106 | 112x112 | 0.6ms | 0.5ms | 1.4MB |
- Face detection: yoloface-50k Landmark: landmark106
- https://github.com/AlexeyAB/darknet
- You must use a pre-trained model to train your own data set. You can make a pre-trained model based on the weights of COCO training in this project to initialize the network parameters
- 交流qq群:1062122604
- MobileNetV2-YOLOv3-SPP: Nvidia Jeston, Intel Movidius, TensorRT，NPU，OPENVINO...High-performance embedded side
- MobileNetV2-YOLOv3-Lite: High Performance ARM-CPU，Qualcomm Adreno GPU， ARM82...High-performance mobile
- MobileNetV2-YOLOv3-NANO： ARM-CPU...Computing resources are limited
- MobileNetV2-YOLOv3-Fastest： ....... Can you do personal face detection???It’s better than nothing
- Benchmark:https://github.com/Tencent/ncnn/tree/master/benchmark
- NCNN supports direct conversion of darknet models
- darknet2ncnn: https://github.com/Tencent/ncnn/tree/master/tools/darknet
- https://github.com/dog-qiuqiu/Android_MobileNetV2-YOLOV3-Nano-NCNN
- APK:https://github.com/dog-qiuqiu/Android_MobileNetV2-YOLOV3-Nano-NCNN/blob/master/app/release/MobileNetv2-yolov3-nano.apk
- Python2.7
- python-opencv
- Caffe(add upsample layer https://github.com/dog-qiuqiu/caffe)
- You have to compile cpu version of caffe！！！
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
- Benchmark:https://www.yuque.com/mnn/cn/tool_benchmark
- Convert darknet model to caffemodel through darknet2caffe
- Manually replace the upsample layer in prototxt with the interp layer
- Take the modification of MobileNetV2-YOLOv3-Nano-voc.prototxt as an example
#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
}
}
- MNN conversion: https://www.yuque.com/mnn/cn/model_convert