YOLOv4_pytorch

TODO List

• Dataset
• Model
• Loss
• Coder
• burn-in
• scheduler(Cosine Anneling)
• IT
• M(Mosaic augmentation)

scheduler

• we use step LR scheduler and learning rate warmup(burning) scheme

1833 x 218 = 399594

1833 x 246 = 450918

1833 x 273 = 500409

burn-in

• batch 64 & iteration 1000
• lr : 0 to 1e-3 (i/1000)

trining

• batch : 64
• scheduler : step LR
• loss : sse + bce
• dataset : coco
• epoch : 273
• gpu : nvidia geforce rtx 3090 * 2EA

Experiment

268 265

methods	Traning Dataset	Testing Dataset	Resolution	AP	AP50	AP75	Time	Fps
papers(YOLOv3)	COCOtrain2017	COCO test-dev	416 x 416	0.310	0.553	0.344	29	34.48
papers	COCOtrain2017	COCOval2017(minival)	416 x 416	-	-	-	-	-
yolov3 + CSP	COCOtrain2017	COCO test-dev	416 x 416	-	-	-	-	-
yolov3 + CSP	COCOtrain2017	COCOval2017(minival)	416 x 416	0.380	59.9	40.8
yolov3 + CSP + giou loss	COCOtrain2017	COCO test-dev	416 x 416	-	-	-
yolov3 + CSP + giou loss	COCOtrain2017	COCOval2017(minival)	416 x 416	0.398	0.602	0.426
YOLOv4	COCOtrain2017	COCO test-dev	416 x 416	0.412	0.628	0.448
YOLOv4	COCOtrain2017	COCO test-dev	512 x 512	0.430	0.649	0.465
OURs	COCOtrain2017	COCOval2017(minival).	416 x 416	0.410	0.611	0.439

• experiments1

  yolov3 + CSP

  lr = 1e-3
  epoch = 273 
  burn_in = 4000
  batch_size = 64
  optimizer = SGD
  lr decay = step LR [218, 246]
  best_epoch = 265
  

  experiments	Dataset	Resolution	base detector	AP	AP50	AP75
  exp1	minival	416 x 416	yolov3 + CSP	38.0	59.9	40.8

• experiments2 (~21.05.24-16:33)

  yolov3 + CSP + GIoULoss

  lr = 1e-3
  epoch = 273 
  burn_in = 4000
  batch_size = 64
  optimizer = SGD
  lr decay = step LR [218, 246]
  best_epoch = 272
  

  experiments	Dataset	Resolution	base detector	AP	AP50	AP75
  exp2	minival	416 x 416	yolov3 + CSP + GIoU	0.398	0.602	0.426

• experiments3

  yolov3 + CSP + GIoULoss + IT(Iou threshold) + cosine annealing lr scheduler

  lr = 1e-3
  epoch = 273 
  burn_in = 4000
  batch_size = 64
  optimizer = SGD
  lr decay = cosine annealing lr scheduler
  best_epoch = 264
  
  

  experiments	Dataset	Resolution	base detector	AP	AP50	AP75
  exp3	minival	416 x 416	yolov3 + CSP + GIoU + IT + M + CA	0.363	0.529	0.394
  YOLOv4	COCO test-dev	416 x 416	YOLOv4	0.412	0.628	0.448

• experiments4 Is the cosine-annealing-lr-scheduler better than step LR? loss.py 87 line set IT=None yolov3 + CSP + GIoULoss + CA(cosine annealing lr scheduler)

  lr = 1e-3
  epoch = 273 
  burn_in = 4000
  batch_size = 64
  optimizer = SGD
  lr decay = cosine annealing lr scheduler
  best_epoch = 266
  
  

  experiments	Dataset	Resolution	base detector	AP	AP50	AP75
  exp4	minival	416 x 416	yolov3 + CSP + GIoU + CA	0.403	0.603	0.432

• experiments5 Is the cosine-annealing-lr-scheduler better than step LR? loss.py 87 line set IT=None yolov3 + CSP + GIoULoss + CA(cosine annealing lr scheduler) + Mosaic

  lr = 1e-3
  epoch = 273 
  burn_in = 4000
  batch_size = 64
  optimizer = SGD
  lr decay = cosine annealing lr scheduler
  best_epoch = 264
  
  

  experiments	Dataset	Resolution	base detector	AP	AP50	AP75
  exp5	minival	416 x 416	yolov3 + CSP + GIoU + CA + Mosaic	0.408	0.612	0.439
  exp5	testdev	416 x 416	yolov3 + CSP + GIoU + CA + Mosaic	0.408	0.612	0.438

• experiments6 Is the cosine-annealing-lr-scheduler better than step LR? yeah yolov3 + CSP + GIoULoss + CA(cosine annealing lr scheduler) + Mosaic change our models

  lr = 1e-3
  epoch = 273 
  burn_in = 4000
  batch_size = 64
  optimizer = SGD
  lr decay = cosine annealing lr scheduler
  best_epoch = 271 
  
  

  experiments	Dataset	Resolution	base detector	AP	AP50	AP75
  exp6	minival	416 x 416	yolov3 + CSP + GIoU + CA + Mosaic	0.409	0.610	0.439

• experiments7 Is the cosine-annealing-lr-scheduler better than step LR? yeah yolov3 + CSP + GIoULoss + CA(cosine annealing lr scheduler) + Mosaic change scheduler.

  lr = 1e-3
  epoch = 273 
  burn_in = 4000
  batch_size = 64
  optimizer = SGD
  lr decay = cosine annealing lr scheduler to 280
  best_epoch = 266 
  
  

  experiments	Dataset	Resolution	base detector	AP	AP50	AP75
  exp7	minival	416 x 416	yolov3 + CSP + GIoU + CA + Mosaic	0.410	0.611	0.439