YOLOv5

v7.0

New Segmentation Checkpoints

We trained YOLOv5 segmentations models on COCO for 300 epochs at image size 640 using A100 GPUs. We exported all models to ONNX FP32 for CPU speed tests and to TensorRT FP16 for GPU speed tests. We ran all speed tests on Google Colab Pro notebooks for easy reproducibility.

Model

size (pixels)

mAPbox 50-95

mAPmask 50-95

Train time 300 epochs A100 (hours)

Speed ONNX CPU (ms)

Speed TRT A100 (ms)

params (M)

FLOPs @640 (B)

YOLOv5n-seg

640

27.6

23.4

80:17

62.7

1.2

2.0

7.1

YOLOv5s-seg

640

37.6

31.7

88:16

173.3

1.4

7.6

26.4

YOLOv5m-seg

640

45.0

37.1

108:36

427.0

2.2

22.0

70.8

YOLOv5l-seg

640

49.0

39.9

66:43 (2x)

857.4

2.9

47.9

147.7

YOLOv5x-seg

640

50.7

41.4

62:56 (3x)

1579.2

4.5

88.8

265.7

  • All checkpoints are trained to 300 epochs with SGD optimizer with lr0=0.01 and weight_decay=5e-5 at image size 640 and all default settings. Runs logged to https://wandb.ai/glenn-jocher/YOLOv5_v70_official

  • Accuracy values are for single-model single-scale on COCO dataset. Reproduce by python segment/val.py --data coco.yaml --weights yolov5s-seg.pt

  • Speed averaged over 100 inference images using a Colab Pro A100 High-RAM instance. Values indicate inference speed only (NMS adds about 1ms per image). Reproduce by python segment/val.py --data coco.yaml --weights yolov5s-seg.pt --batch 1

  • Export to ONNX at FP32 and TensorRT at FP16 done with export.py. Reproduce by python export.py --weights yolov5s-seg.pt --include engine --device 0 --half

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