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2022-10-25
MobileNet-YOLO 检测框架的一个caffe实现
MobileNet-YOLO Caffe
A caffe implementation of MobileNet-YOLO detection network , train on 07+12 , test on VOC2007
Network | mAP | Resolution | Download | NetScope | Inference time (GTX 1080) | Inference time (i5-7500) |
---|---|---|---|---|---|---|
MobileNetV2-YOLOv3 | 70.7 | 352 | caffemodel | graph | 6.65 ms | 217 ms |
inference time was log from script , does not include pre-processingthe benchmark of cpu performance on Tencent/ncnn frameworkthe deploy model was made by merge_bn.py, set eps = your prototxt batchnorm epsold models please see here
This project also support ssd framework , and here lists the difference from ssd caffe
Multi-scale training , you can select input resoluton when inferenceModified from last update caffe (2018)Support multi-task modelpelee + driverable map
Update
CODE UPDATED FOR OPENCV 3Channel pruning
CNN Analyzer
Use this tool to compare macc and param , train on 07+12 , test on VOC2007
network | mAP | resolution | macc | param | pruned | IOU_THRESH | GIOU |
---|---|---|---|---|---|---|---|
MobileNetV2-YOLOv3 | 0.707 | 352 | 1.22G | 4.05M | N | N | N |
MobileNetV2-YOLOv3 | 0.715 | 352 | 1.22G | 4.05M | N | Y | Y |
MobileNetV2-YOLOv3 | 0.702 | 352 | 1.01G | 2.88M | Y | N | N |
Pelee-SSD | 0.709 | 304 | 1.2G | 5.42M | N | N | N |
Mobilenet-SSD | 0.68 | 300 | 1.21G | 5.43M | N | N | N |
MobilenetV2-SSD-lite | 0.709 | 336 | 1.10G | 5.2M | N | N | N |
MobileNetV2-YOLOv3 and MobilenetV2-SSD-lite were not offcial model
Coverted TensorRT models
TensorRT-Yolov3-models
Pelee-Driverable_Maps, run 89 ms on jetson nano , running project
YOLO Segmentation
How to use
Windows Version
Caffe-YOLOv3-Windows
Oringinal darknet-yolov3
Converter
test on coco_minival_lmdb (IOU 0.5)
Network | mAP | Resolution | Download | NetScope |
---|---|---|---|---|
yolov3 | 54.2 | 416 | caffemodel | graph |
yolov3-spp | 59.8 | 608 | caffemodel | graph |
Model VisulizationTool
Supported on Netron , browser version
Build , Run and Training
See wiki
See docker
License and Citation
Please cite MobileNet-YOLO in your publications if it helps your research:
@article{MobileNet-YOLO, Author = {eric612 , Avisonic , ELAN}, Year = {2018}}
Reference
https://github.com/weiliu89/caffe/tree/ssd
https://pjreddie.com/darknet/yolo/
https://github.com/chuanqi305/MobileNet-SSD
https://github.com/gklz1982/caffe-yolov2
https://github.com/yonghenglh6/DepthwiseConvolution
https://github.com/alexgkendall/caffe-segnet
https://github.com/BVLC/caffe/pull/6384/commits/4d2400e7ae692b25f034f02ff8e8cd3621725f5c
https://cityscapes-dataset.com/
https://github.com/TuSimple/tusimple-benchmark/wiki
https://github.com/Robert-JunWang/Pelee
https://github.com/hujie-frank/SENet
https://github.com/lusenkong/Caffemodel_Compress
Cudnn convolution
https://github.com/chuanqi305/MobileNetv2-SSDLite/tree/master/src
Acknowledgements
https://github.com/AlexeyAB/darknet
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