ScaledYOLOv4 Download - ScaledYOLOv4 Quellcode Download

Yolov4-large

Dies ist die Implementierung von "scaled-yolov4: scaling cross boeth partiell netzwerk" unter Verwendung von Pytorch Framwork.

Yolov4-CSP
Yolov4-tiny
Yolov4-large

Modell	Testgröße	AP ^-Test	AP ₅₀ ^Test	AP ₇₅ ^Test	_APs ^Test	AP _M ^-Test	AP _L ^-Test	Batch1 -Durchsatz
Yolov4-p5	896	51,4%	69,9%	56,3%	33,1%	55,4%	62,4%	41 fps
Yolov4-p5	Tta	52,5%	70,3%	58,0%	36,0%	52,4%	62,3%	- -

Yolov4-p6	1280	54,3%	72,3%	59,5%	36,6%	58,2%	65,5%	30 fps
Yolov4-p6	Tta	54,9%	72,6%	60,2%	37,4%	58,8%	66,7%	- -

Yolov4-p7	1536	55,4%	73,3%	60,7%	38,1%	59,5%	67,4%	15 fps
Yolov4-p7	Tta	55,8%	73,2%	61,2%	38,8%	60,1%	68,2%	- -

Modell	Testgröße	AP ^val	AP ₅₀ ^Val	AP ₇₅ ^Val	AP _S ^Val	Ap _m ^val	Ap _l ^val	Gewichte
Yolov4-p5	896	51,2%	69,8%	56,2%	35,0%	56,2%	64,0%	`yolov4-p5.pt`
Yolov4-p5	Tta	52,5%	70,2%	57,8%	38,5%	57,2%	64,0%	- -
Yolov4-p5 (+bof)	896	51,7%	70,3%	56,7%	35,9%	56,7%	64,3%	`yolov4-p5_.pt`
Yolov4-p5 (+bof)	Tta	52,8%	70,6%	58,3%	38,8%	57,4%	64,4%	- -

Yolov4-p6	1280	53,9%	72,0%	59,0%	39,3%	58,3%	66,6%	`yolov4-p6.pt`
Yolov4-p6	Tta	54,4%	72,3%	59,6%	39,8%	58,9%	67,6%	- -
Yolov4-p6 (+bof)	1280	54,4%	72,7%	59,5%	39,5%	58,9%	67,3%	`yolov4-p6_.pt`
Yolov4-p6 (+bof)	Tta	54,8%	72,6%	60,0%	40,6%	59,1%	68,2%	- -
Yolov4-p6 (+bof*)	1280	54,7%	72,9%	60,0%	39,4%	59,2%	68,3%
Yolov4-p6 (+bof*)	Tta	55,3%	73,2%	60,8%	40,5%	59,9%	69,4%	- -

Yolov4-p7	1536	55,0%	72,9%	60,2%	39,8%	59,9%	68,4%	`yolov4-p7.pt`
Yolov4-p7	Tta	55,5%	72,9%	60,8%	41,1%	60,3%	68,9%	- -

Modell	Testgröße	AP ^val	AP ₅₀ ^Val	AP ₇₅ ^Val	AP _S ^Val	Ap _m ^val	Ap _l ^val
Yolov4-p6-Einhaltung	1280	54,3%	72,3%	59,6%	38,7%	58,9%	66,6%

Installation

 # create the docker container, you can change the share memory size if you have more.
nvidia-docker run --name yolov4_csp -it -v your_coco_path/:/coco/ -v your_code_path/:/yolo --shm-size=64g nvcr.io/nvidia/pytorch:20.06-py3

# install mish-cuda, if you use different pytorch version, you could try https://github.com/thomasbrandon/mish-cuda
cd /
git clone https://github.com/JunnYu/mish-cuda
cd mish-cuda
python setup.py build install

# go to code folder
cd /yolo

Testen

 # download {yolov4-p5.pt, yolov4-p6.pt, yolov4-p7.pt} and put them in /yolo/weights/ folder.
python test.py --img 896 --conf 0.001 --batch 8 --device 0 --data coco.yaml --weights weights/yolov4-p5.pt
python test.py --img 1280 --conf 0.001 --batch 8 --device 0 --data coco.yaml --weights weights/yolov4-p6.pt
python test.py --img 1536 --conf 0.001 --batch 8 --device 0 --data coco.yaml --weights weights/yolov4-p7.pt

Sie erhalten die folgenden Ergebnisse:

 # yolov4-p5
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.51244
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.69771
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.56180
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.35021
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.56247
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.63983
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.38530
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.64048
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.69801
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.55487
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.74368
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.82826

 # yolov4-p6
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.53857
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.72015
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.59025
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.39285
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.58283
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.66580
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.39552
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.66504
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.72141
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.59193
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.75844
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.83981

 # yolov4-p7
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.55046
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.72925
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.60224
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.39836
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.59854
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.68405
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.40256
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.66929
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.72943
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.59943
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.76873
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.84460

Ausbildung

Wir verwenden mehrere GPUs für das Training. {Yolov4-p5, yolov4-p6, yolov4-p7} Verwenden Sie die Eingabeauflösung {896, 1280, 1536} für das Training.

 # yolov4-p5
python -m torch.distributed.launch --nproc_per_node 4 train.py --batch-size 64 --img 896 896 --data coco.yaml --cfg yolov4-p5.yaml --weights '' --sync-bn --device 0,1,2,3 --name yolov4-p5
python -m torch.distributed.launch --nproc_per_node 4 train.py --batch-size 64 --img 896 896 --data coco.yaml --cfg yolov4-p5.yaml --weights 'runs/exp0_yolov4-p5/weights/last_298.pt' --sync-bn --device 0,1,2,3 --name yolov4-p5-tune --hyp 'data/hyp.finetune.yaml' --epochs 450 --resume

Wenn Ihr Trainingsprozess einlädt, ist dies aufgrund von Insekten des Python. Nur Ctrl+C um das Training zu stoppen und das Training wieder aufzunehmen von:

 # yolov4-p5
python -m torch.distributed.launch --nproc_per_node 4 train.py --batch-size 64 --img 896 896 --data coco.yaml --cfg yolov4-p5.yaml --weights 'runs/exp0_yolov4-p5/weights/last.pt' --sync-bn --device 0,1,2,3 --name yolov4-p5 --resume

Zitat

 @InProceedings{Wang_2021_CVPR,
    author    = {Wang, Chien-Yao and Bochkovskiy, Alexey and Liao, Hong-Yuan Mark},
    title     = {{Scaled-YOLOv4}: Scaling Cross Stage Partial Network},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    pages     = {13029-13038}
}