@article{picodet,
title={{PP-PicoDet}: A Better Real-Time Object Detector on Mobile Devices},
author={Guanghua Yu, Qinyao Chang, Wenyu Lv, Chang Xu, Cheng Cui, Wei Ji, Qingqing Dang, Kaipeng Deng, Guanzhong Wang, Yuning Du, Baohua Lai, Qiwen Liu, Xiaoguang Hu, Dianhai Yu, Yanjun Ma},
journal={arXiv preprint arXiv:2111.00902},
year={2021}
}| Bakcbone | size | box AP(ppdet) | Config | Download |
|---|---|---|---|---|
| picodet-s | 320 | 26.9(27.1) | config | model | log |
| picodet-s | 416 | 30.6(30.6) | config | model | log |
| picodet-m | 416 | 34.2(34.3) | config | model | log |
| picodet-l | 640 | 40.4(40.9) | config | model | log |
Our implementation is based on mmdetection. Install mmdetection according to INSTALL
Note: Make sure your mmcv-full version is consistency with mmdet version(we use mmcv==1.4.0)
-
Download pretrained backbone using the link above
-
training
bash tools/dist_train.sh ./configs/picodet/picodet_s_320_coco.py 4
bash tools/dist_test.sh $CONFIG_PATH $TRAINED_MODEL_PATH $GPU_NUMS --eval bbox
eg. use picodet-s 320 pretrianed model
bash tools/dist_test.sh ./configs/picodet/picodet_s_320_coco.py $MODEL_DIR/picodet_s_320.26.9.pth 8 --eval bbox
Evaluating bbox...
Loading and preparing results...
DONE (t=1.76s)
creating index...
index created!
Running per image evaluation...
Evaluate annotation type *bbox*
DONE (t=43.50s).
Accumulating evaluation results...
DONE (t=14.63s).
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.269
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.408
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.279
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.076
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.269
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.462
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.421
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.421
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.421
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.138
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.470
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.684
TODO：
- mnn deploy