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deepghs/
yolos

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Official pretrained models from ultralytics.

Use this with dghs-realutils

pip install dghs-realutils

python

from realutils.detect import detect_by_yolo

print(detect_by_yolo('yolo/unsplash_aJafJ0sLo6o.jpg'))
# [((450, 317, 567, 599), 'person', 0.9004617929458618)]
print(detect_by_yolo('yolo/unsplash_n4qQGOBgI7U.jpg'))
# [((73, 101, 365, 409), 'vase', 0.9098997116088867), ((441, 215, 659, 428), 'vase', 0.622944176197052), ((5, 1, 428, 377), 'potted plant', 0.5178268551826477)]
print(detect_by_yolo('yolo/unsplash_vUNQaTtZeOo.jpg'))
# [((381, 103, 676, 448), 'bird', 0.9061452150344849)]
print(detect_by_yolo('yolo/unsplash_YZOqXWF_9pk.jpg'))
# [((315, 100, 690, 532), 'horse', 0.9453459978103638), ((198, 181, 291, 256), 'horse', 0.917123556137085), ((145, 173, 180, 249), 'horse', 0.7972317337989807), ((660, 138, 701, 170), 'horse', 0.4843617379665375)]

For more information, see documentation of realutils.

Models Available

Model	Type	FLOPS	Params	Labels	precision(B)	recall(B)	mAP50(B)	mAP50-95(B)
yolo12x	yolo	200G	59.2M	`person`, `bicycle`, `car`, `motorcycle`, `airplane` ... 80 labels in total	N/A	N/A	N/A	N/A
yolo12l	yolo	89.7G	26.5M	`person`, `bicycle`, `car`, `motorcycle`, `airplane` ... 80 labels in total	N/A	N/A	N/A	N/A
yolo12m	yolo	68.1G	20.2M	`person`, `bicycle`, `car`, `motorcycle`, `airplane` ... 80 labels in total	N/A	N/A	N/A	N/A
yolo12s	yolo	21.7G	9.29M	`person`, `bicycle`, `car`, `motorcycle`, `airplane` ... 80 labels in total	N/A	N/A	N/A	N/A
yolo12n	yolo	6.65G	2.6M	`person`, `bicycle`, `car`, `motorcycle`, `airplane` ... 80 labels in total	N/A	N/A	N/A	N/A
rtdetr-x	rtdetr	233G	67.5M	`person`, `bicycle`, `car`, `motorcycle`, `airplane` ... 80 labels in total	N/A	N/A	N/A	N/A
rtdetr-l	rtdetr	108G	33M	`person`, `bicycle`, `car`, `motorcycle`, `airplane` ... 80 labels in total	N/A	N/A	N/A	N/A
yolo11x	yolo	196G	57M	`person`, `bicycle`, `car`, `motorcycle`, `airplane` ... 80 labels in total	0.738	0.658	0.716	0.546
yolo11l	yolo	87.6G	25.4M	`person`, `bicycle`, `car`, `motorcycle`, `airplane` ... 80 labels in total	0.747	0.635	0.702	0.533
yolo11m	yolo	68.5G	20.1M	`person`, `bicycle`, `car`, `motorcycle`, `airplane` ... 80 labels in total	0.738	0.617	0.684	0.514
yolo11s	yolo	21.7G	9.46M	`person`, `bicycle`, `car`, `motorcycle`, `airplane` ... 80 labels in total	0.706	0.575	0.638	0.469
yolo11n	yolo	6.61G	2.62M	`person`, `bicycle`, `car`, `motorcycle`, `airplane` ... 80 labels in total	0.656	0.502	0.551	0.394
yolov10x	yolo	172G	31.8M	`person`, `bicycle`, `car`, `motorcycle`, `airplane` ... 80 labels in total	N/A	N/A	N/A	N/A
yolov10l	yolo	128G	25.9M	`person`, `bicycle`, `car`, `motorcycle`, `airplane` ... 80 labels in total	N/A	N/A	N/A	N/A
yolov10b	yolo	99.4G	20.6M	`person`, `bicycle`, `car`, `motorcycle`, `airplane` ... 80 labels in total	N/A	N/A	N/A	N/A
yolov10m	yolo	64.5G	16.6M	`person`, `bicycle`, `car`, `motorcycle`, `airplane` ... 80 labels in total	N/A	N/A	N/A	N/A
yolov10s	yolo	25.1G	8.13M	`person`, `bicycle`, `car`, `motorcycle`, `airplane` ... 80 labels in total	N/A	N/A	N/A	N/A
yolov10n	yolo	8.74G	2.78M	`person`, `bicycle`, `car`, `motorcycle`, `airplane` ... 80 labels in total	N/A	N/A	N/A	N/A
yolov9e	yolo	193G	58.2M	`person`, `bicycle`, `car`, `motorcycle`, `airplane` ... 80 labels in total	0.75	0.66	0.718	0.551
yolov9c	yolo	104G	25.6M	`person`, `bicycle`, `car`, `motorcycle`, `airplane` ... 80 labels in total	0.732	0.632	0.694	0.527
yolov9m	yolo	77.9G	20.2M	`person`, `bicycle`, `car`, `motorcycle`, `airplane` ... 80 labels in total	0.725	0.62	0.68	0.513
yolov9s	yolo	27.6G	7.32M	`person`, `bicycle`, `car`, `motorcycle`, `airplane` ... 80 labels in total	0.7	0.567	0.624	0.463
yolov9t	yolo	8.48G	2.13M	`person`, `bicycle`, `car`, `motorcycle`, `airplane` ... 80 labels in total	0.616	0.484	0.524	0.378
yolov8x	yolo	259G	68.2M	`person`, `bicycle`, `car`, `motorcycle`, `airplane` ... 80 labels in total	N/A	N/A	N/A	N/A
yolov8l	yolo	166G	43.7M	`person`, `bicycle`, `car`, `motorcycle`, `airplane` ... 80 labels in total	N/A	N/A	N/A	N/A
yolov8m	yolo	79.3G	25.9M	`person`, `bicycle`, `car`, `motorcycle`, `airplane` ... 80 labels in total	N/A	N/A	N/A	N/A
yolov8s	yolo	28.8G	11.2M	`person`, `bicycle`, `car`, `motorcycle`, `airplane` ... 80 labels in total	N/A	N/A	N/A	N/A
yolov8n	yolo	8.86G	3.16M	`person`, `bicycle`, `car`, `motorcycle`, `airplane` ... 80 labels in total	N/A	N/A	N/A	N/A
yolov5xu	yolo	247G	97.3M	`person`, `bicycle`, `car`, `motorcycle`, `airplane` ... 80 labels in total	N/A	N/A	N/A	N/A
yolov5lu	yolo	136G	53.2M	`person`, `bicycle`, `car`, `motorcycle`, `airplane` ... 80 labels in total	N/A	N/A	N/A	N/A
yolov5mu	yolo	64.6G	25.1M	`person`, `bicycle`, `car`, `motorcycle`, `airplane` ... 80 labels in total	N/A	N/A	N/A	N/A
yolov5su	yolo	24.2G	9.15M	`person`, `bicycle`, `car`, `motorcycle`, `airplane` ... 80 labels in total	N/A	N/A	N/A	N/A
yolov5nu	yolo	7.84G	2.65M	`person`, `bicycle`, `car`, `motorcycle`, `airplane` ... 80 labels in total	N/A	N/A	N/A	N/A

deepghs/yolos

Models Available

deepghs/
yolos