Awesome
***Darknet Group convolution is not well supported on some GPUs such as NVIDIA PASCAL!!!
针对某些Pascal显卡例如1080ti在darknet上 训练失败/训练异常缓慢/推理速度异常 的可以采用Pytorch版yolo3框架 训练/推理
MobileNetV2-YOLOv3-Lite&Nano Darknet
Mobile inference frameworks benchmark (4*ARM_CPU)
- 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
MobileNetV2-YOLOv3-Lite-COCO Test results
Application
Ultralight-SimplePose
YoloFace-500k: 500kb yolo-Face-Detection
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.
Wider Face Val
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
YoloFace-500k Test results(thresh 0.7)
YoloFace-500k-v2 Test results(thresh 0.7)
YoloFace-50k: Sub-millisecond face detection model
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
YoloFace-50k Test results(thresh 0.7)
YoloFace50k-landmark106(Ultra lightweight 106 point face-landmark model)
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
YoloFace50k-landmark106 Test results
Reference&Framework instructions&How to Train
- 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
About model selection
- 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
NCNN conversion tutorial
NCNN C++ Sample
NCNN Android Sample
DarkNet2Caffe tutorial
Environmental requirements
MNN conversion tutorial
- 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
}
}
Thanks