Default confidence and IoU threshold for training of yolov5

[rabiyaabbasi]

Hi I wanted to know what are the default values of confidence and IoU threshold while training the data . I have checked train.py file but I am unable to find it. Secondly, attached below is my F1 score. according to which recall and precision optimises at a confidence value of 0.352. Do I need to use this confidence value for detection (!python detect.py --weights runs/train/exp4/weights/best.pt --img 416 --conf 0.352 --source ../dataset)? Secondly, when I checked the detect.py file, the IoU is 0.45 and confidence threshold is 0.25. Why we using this confidence threshold and how to update it?

[glenn-jocher]

@rabiyaabbasi default settings are in detect.py argparser, you may modify these to whatever you want:

yolov5/detect.py

Lines 211 to 243 in 5f941a8

	def parse_opt():
	parser = argparse.ArgumentParser()
	parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'yolov5s.pt', help='model path(s)')
	parser.add_argument('--source', type=str, default=ROOT / 'data/images', help='file/dir/URL/glob, 0 for webcam')
	parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='(optional) dataset.yaml path')
	parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[640], help='inference size h,w')
	parser.add_argument('--conf-thres', type=float, default=0.25, help='confidence threshold')
	parser.add_argument('--iou-thres', type=float, default=0.45, help='NMS IoU threshold')
	parser.add_argument('--max-det', type=int, default=1000, help='maximum detections per image')
	parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
	parser.add_argument('--view-img', action='store_true', help='show results')
	parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
	parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')
	parser.add_argument('--save-crop', action='store_true', help='save cropped prediction boxes')
	parser.add_argument('--nosave', action='store_true', help='do not save images/videos')
	parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --classes 0, or --classes 0 2 3')
	parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS')
	parser.add_argument('--augment', action='store_true', help='augmented inference')
	parser.add_argument('--visualize', action='store_true', help='visualize features')
	parser.add_argument('--update', action='store_true', help='update all models')
	parser.add_argument('--project', default=ROOT / 'runs/detect', help='save results to project/name')
	parser.add_argument('--name', default='exp', help='save results to project/name')
	parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
	parser.add_argument('--line-thickness', default=3, type=int, help='bounding box thickness (pixels)')
	parser.add_argument('--hide-labels', default=False, action='store_true', help='hide labels')
	parser.add_argument('--hide-conf', default=False, action='store_true', help='hide confidences')
	parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference')
	parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference')
	opt = parser.parse_args()
	opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1 # expand
	print_args(vars(opt))
	return opt

[yesid-acm]

@glenn-jocher can you respond to the questions of @rabiyaabbasi ? Please! this is so important to understand the functionality.

[codevisioner]

@glenn-jocher can you respond to the questions of @rabiyaabbasi ? Thank you.