Awesome
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 | 71.5 | 352 | caffemodel | graph | 6.65 ms | 217 ms |
- inference time was log from script , does not include pre-processing
- the benchmark of cpu performance on Tencent/ncnn framework
- the deploy model was made by merge_bn.py, set eps = your prototxt batchnorm eps
- old 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 inference
- Modified from last update caffe (2018)
- Support multi-task model
- pelee + driverable map
Update
- The pytorch version was released Mobilenet-YOLO-Pytorch
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
Pelee-Driverable_Maps, run 89 ms on jetson nano , running project
YOLO Segmentation
Windows Version
Oringinal darknet-yolov3
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/BVLC/caffe/pull/6384/commits/4d2400e7ae692b25f034f02ff8e8cd3621725f5c
Cudnn convolution
https://github.com/chuanqi305/MobileNetv2-SSDLite/tree/master/src