์ฝ˜ํ…์ธ ๋กœ ๊ฑด๋„ˆ๋›ฐ๊ธฐ

๋ชจ๋ธ ์•™์ƒ๋ธ”

๐Ÿ“š This guide explains how to use YOLOv5 ๐Ÿš€ model ensembling during testing and inference for improved mAP and Recall.

์ถœ์ฒ˜: https: //en.wikipedia.org/wiki/Ensemble_learning:

Ensemble modeling is a process where multiple diverse models are created to predict an outcome, either by using many different modeling algorithms or using different training data sets. The ensemble model then aggregates the prediction of each base model and results in once final prediction for the unseen data. The motivation for using ensemble models is to reduce the generalization error of the prediction. As long as the base models are diverse and independent, the prediction error of the model decreases when the ensemble approach is used. The approach seeks the wisdom of crowds in making a prediction. Even though the ensemble model has multiple base models within the model, it acts and performs as a single model.

์‹œ์ž‘ํ•˜๊ธฐ ์ „์—

๋ฆฌํฌ์ง€ํ† ๋ฆฌ๋ฅผ ๋ณต์ œํ•˜๊ณ  ์š”๊ตฌ์‚ฌํ•ญ.txt๋ฅผ ์„ค์น˜ํ•ฉ๋‹ˆ๋‹ค. Python>=3.8.0 ํ™˜๊ฒฝ์„ ํฌํ•จํ•˜์—ฌ PyTorch>=1.8. ๋ชจ๋ธ ๋ฐ ๋ฐ์ดํ„ฐ ์„ธํŠธ๋Š” ์ตœ์‹  YOLOv5 ๋ฆด๋ฆฌ์Šค์—์„œ ์ž๋™์œผ๋กœ ๋‹ค์šด๋กœ๋“œ๋ฉ๋‹ˆ๋‹ค.

git clone https://github.com/ultralytics/yolov5  # clone
cd yolov5
pip install -r requirements.txt  # install

์ •์ƒ์ ์œผ๋กœ ํ…Œ์ŠคํŠธ

์–ด์…ˆ๋ธ”ํ•˜๊ธฐ ์ „์— ๋‹จ์ผ ๋ชจ๋ธ์˜ ๊ธฐ์ค€ ์„ฑ๋Šฅ์„ ์„ค์ •ํ•˜๊ณ  ์‹ถ์Šต๋‹ˆ๋‹ค. ์ด ๋ช…๋ น์€ ์ด๋ฏธ์ง€ ํฌ๊ธฐ 640ํ”ฝ์…€์˜ COCO val2017์—์„œ YOLOv5x๋ฅผ ํ…Œ์ŠคํŠธํ•ฉ๋‹ˆ๋‹ค. yolov5x.pt ์€ ๊ฐ€์žฅ ํฌ๊ณ  ์ •ํ™•ํ•œ ๋ชจ๋ธ์ž…๋‹ˆ๋‹ค. ๋‹ค๋ฅธ ์˜ต์…˜์€ ๋‹ค์Œ๊ณผ ๊ฐ™์Šต๋‹ˆ๋‹ค. yolov5s.pt, yolov5m.pt ๊ทธ๋ฆฌ๊ณ  yolov5l.pt๋˜๋Š” ์‚ฌ์šฉ์ž ์ง€์ • ๋ฐ์ดํ„ฐ ์„ธํŠธ ํ•™์Šต์—์„œ ์–ป์€ ์ž์ฒด ์ฒดํฌํฌ์ธํŠธ ./weights/best.pt. ์‚ฌ์šฉ ๊ฐ€๋Šฅํ•œ ๋ชจ๋“  ๋ชจ๋ธ์— ๋Œ€ํ•œ ์ž์„ธํ•œ ๋‚ด์šฉ์€ README๋ฅผ ์ฐธ์กฐํ•˜์„ธ์š”. ํ…Œ์ด๋ธ”.

python val.py --weights yolov5x.pt --data coco.yaml --img 640 --half

์ถœ๋ ฅ:

val: data=./data/coco.yaml, weights=['yolov5x.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.65, task=val, device=, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=True, project=runs/val, name=exp, exist_ok=False, half=True
YOLOv5 ๐Ÿš€ v5.0-267-g6a3ee7c torch 1.9.0+cu102 CUDA:0 (Tesla P100-PCIE-16GB, 16280.875MB)

Fusing layers...
Model Summary: 476 layers, 87730285 parameters, 0 gradients

val: Scanning '../datasets/coco/val2017' images and labels...4952 found, 48 missing, 0 empty, 0 corrupted: 100% 5000/5000 [00:01<00:00, 2846.03it/s]
val: New cache created: ../datasets/coco/val2017.cache
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 157/157 [02:30<00:00,  1.05it/s]
                 all       5000      36335      0.746      0.626       0.68       0.49
Speed: 0.1ms pre-process, 22.4ms inference, 1.4ms NMS per image at shape (32, 3, 640, 640)  # <--- baseline speed

Evaluating pycocotools mAP... saving runs/val/exp/yolov5x_predictions.json...
...
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.504  # <--- baseline mAP
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.688
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.546
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.351
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.551
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.644
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.382
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.628
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.681  # <--- baseline mAR
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.524
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.735
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.826

์•™์ƒ๋ธ” ํ…Œ์ŠคํŠธ

์‚ฌ์ „ ํ•™์Šต๋œ ์—ฌ๋Ÿฌ ๋ชจ๋ธ์„ ํ…Œ์ŠคํŠธ ๋ฐ ์ถ”๋ก  ์‹œ์ ์— ์ถ”๊ฐ€ ๋ชจ๋ธ์„ ์ถ”๊ฐ€ํ•˜์—ฌ ํ•จ๊ป˜ ์กฐํ•ฉํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. --weights ์ธ์ˆ˜๋ฅผ ์ถ”๊ฐ€ํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. ์ด ์˜ˆ์—์„œ๋Š” ๋‘ ๋ชจ๋ธ์˜ ์•™์ƒ๋ธ”์„ ํ•จ๊ป˜ ํ…Œ์ŠคํŠธํ•ฉ๋‹ˆ๋‹ค:

  • YOLOv5x
  • YOLOv5l6
python val.py --weights yolov5x.pt yolov5l6.pt --data coco.yaml --img 640 --half

์ถœ๋ ฅ:

val: data=./data/coco.yaml, weights=['yolov5x.pt', 'yolov5l6.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6, task=val, device=, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=True, project=runs/val, name=exp, exist_ok=False, half=True
YOLOv5 ๐Ÿš€ v5.0-267-g6a3ee7c torch 1.9.0+cu102 CUDA:0 (Tesla P100-PCIE-16GB, 16280.875MB)

Fusing layers...
Model Summary: 476 layers, 87730285 parameters, 0 gradients  # Model 1
Fusing layers...
Model Summary: 501 layers, 77218620 parameters, 0 gradients  # Model 2
Ensemble created with ['yolov5x.pt', 'yolov5l6.pt']  # Ensemble notice

val: Scanning '../datasets/coco/val2017.cache' images and labels... 4952 found, 48 missing, 0 empty, 0 corrupted: 100% 5000/5000 [00:00<00:00, 49695545.02it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 157/157 [03:58<00:00,  1.52s/it]
                 all       5000      36335      0.747      0.637      0.692      0.502
Speed: 0.1ms pre-process, 39.5ms inference, 2.0ms NMS per image at shape (32, 3, 640, 640)  # <--- ensemble speed

Evaluating pycocotools mAP... saving runs/val/exp3/yolov5x_predictions.json...
...
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.515  # <--- ensemble mAP
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.699
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.557
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.356
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.563
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.668
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.387
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.638
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.689  # <--- ensemble mAR
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.526
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.743
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.844

์•™์ƒ๋ธ” ์ถ”๋ก 

์ถ”๊ฐ€ ๋ชจ๋ธ์„ ์ถ”๊ฐ€ํ•ฉ๋‹ˆ๋‹ค. --weights ์ธ์ˆ˜๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ์•™์ƒ๋ธ” ์ถ”๋ก ์„ ์‹คํ–‰ํ•ฉ๋‹ˆ๋‹ค:

python detect.py --weights yolov5x.pt yolov5l6.pt --img 640 --source data/images

์ถœ๋ ฅ:

YOLOv5 ๐Ÿš€ v5.0-267-g6a3ee7c torch 1.9.0+cu102 CUDA:0 (Tesla P100-PCIE-16GB, 16280.875MB)

Fusing layers...
Model Summary: 476 layers, 87730285 parameters, 0 gradients
Fusing layers...
Model Summary: 501 layers, 77218620 parameters, 0 gradients
Ensemble created with ['yolov5x.pt', 'yolov5l6.pt']

image 1/2 /content/yolov5/data/images/bus.jpg: 640x512 4 persons, 1 bus, 1 tie, Done. (0.063s)
image 2/2 /content/yolov5/data/images/zidane.jpg: 384x640 3 persons, 2 ties, Done. (0.056s)
Results saved to runs/detect/exp2
Done. (0.223s)

YOLO ์ถ”๋ก  ๊ฒฐ๊ณผ

์ง€์› ํ™˜๊ฒฝ

Ultralytics ๋Š” ๋ฐ”๋กœ ์‚ฌ์šฉํ•  ์ˆ˜ ์žˆ๋Š” ๋‹ค์–‘ํ•œ ํ™˜๊ฒฝ์„ ์ œ๊ณตํ•˜๋ฉฐ, ๊ฐ ํ™˜๊ฒฝ์—๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™์€ ํ•„์ˆ˜ ์ข…์†์„ฑ์ด ์‚ฌ์ „ ์„ค์น˜๋˜์–ด ์žˆ์Šต๋‹ˆ๋‹ค. CUDA, CUDNN, Python, ๋ฐ PyTorch์™€ ๊ฐ™์€ ํ•„์ˆ˜ ์ข…์† ์š”์†Œ๋ฅผ ์„ค์น˜ํ•˜์—ฌ ํ”„๋กœ์ ํŠธ๋ฅผ ์‹œ์ž‘ํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค.

ํ”„๋กœ์ ํŠธ ์ƒํƒœ

YOLOv5 CI

์ด ๋ฐฐ์ง€๋Š” ๋ชจ๋“  YOLOv5 GitHub Actions ์ง€์†์  ํ†ตํ•ฉ(CI) ํ…Œ์ŠคํŠธ๊ฐ€ ์„ฑ๊ณต์ ์œผ๋กœ ํ†ต๊ณผ๋˜์—ˆ์Œ์„ ๋‚˜ํƒ€๋ƒ…๋‹ˆ๋‹ค. ์ด๋Ÿฌํ•œ CI ํ…Œ์ŠคํŠธ๋Š” ๊ต์œก, ๊ฒ€์ฆ, ์ถ”๋ก , ๋‚ด๋ณด๋‚ด๊ธฐ ๋ฐ ๋ฒค์น˜๋งˆํฌ ๋“ฑ ๋‹ค์–‘ํ•œ ์ฃผ์š” ์ธก๋ฉด์—์„œ YOLOv5 ์˜ ๊ธฐ๋Šฅ๊ณผ ์„ฑ๋Šฅ์„ ์—„๊ฒฉํ•˜๊ฒŒ ํ™•์ธํ•ฉ๋‹ˆ๋‹ค. 24์‹œ๊ฐ„๋งˆ๋‹ค ๊ทธ๋ฆฌ๊ณ  ์ƒˆ๋กœ์šด ์ปค๋ฐ‹์ด ์žˆ์„ ๋•Œ๋งˆ๋‹ค ํ…Œ์ŠคํŠธ๋ฅผ ์ˆ˜ํ–‰ํ•˜์—ฌ macOS, Windows ๋ฐ Ubuntu์—์„œ ์ผ๊ด€๋˜๊ณ  ์•ˆ์ •์ ์ธ ์ž‘๋™์„ ๋ณด์žฅํ•ฉ๋‹ˆ๋‹ค.


๐Ÿ“… Created 11 months ago โœ๏ธ Updated 13 days ago

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