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benchmark_results.md

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MindYOLO Benchmark and Baselines

Detection

performance tested on Ascend 910(8p) with graph mode
Name Scale BatchSize ImageSize Dataset Box mAP (%) Params Recipe Download
YOLOv8 N 16 * 8 640 MS COCO 2017 37.2 3.2M yaml weights
YOLOv8 S 16 * 8 640 MS COCO 2017 44.6 11.2M yaml weights
YOLOv8 M 16 * 8 640 MS COCO 2017 50.5 25.9M yaml weights
YOLOv8 L 16 * 8 640 MS COCO 2017 52.8 43.7M yaml weights
YOLOv8 X 16 * 8 640 MS COCO 2017 53.7 68.2M yaml weights
YOLOv7 Tiny 16 * 8 640 MS COCO 2017 37.5 6.2M yaml weights
YOLOv7 L 16 * 8 640 MS COCO 2017 50.8 36.9M yaml weights
YOLOv7 X 12 * 8 640 MS COCO 2017 52.4 71.3M yaml weights
YOLOv5 N 32 * 8 640 MS COCO 2017 27.3 1.9M yaml weights
YOLOv5 S 32 * 8 640 MS COCO 2017 37.6 7.2M yaml weights
YOLOv5 M 32 * 8 640 MS COCO 2017 44.9 21.2M yaml weights
YOLOv5 L 32 * 8 640 MS COCO 2017 48.5 46.5M yaml weights
YOLOv5 X 16 * 8 640 MS COCO 2017 50.5 86.7M yaml weights
YOLOv4 CSPDarknet53 16 * 8 608 MS COCO 2017 45.4 27.6M yaml weights
YOLOv4 CSPDarknet53(silu) 16 * 8 608 MS COCO 2017 45.8 27.6M yaml weights
YOLOv3 Darknet53 16 * 8 640 MS COCO 2017 45.5 61.9M yaml weights
YOLOX N 8 * 8 416 MS COCO 2017 24.1 0.9M yaml weights
YOLOX Tiny 8 * 8 416 MS COCO 2017 33.3 5.1M yaml weights
YOLOX S 8 * 8 640 MS COCO 2017 40.7 9.0M yaml weights
YOLOX M 8 * 8 640 MS COCO 2017 46.7 25.3M yaml weights
YOLOX L 8 * 8 640 MS COCO 2017 49.2 54.2M yaml weights
YOLOX X 8 * 8 640 MS COCO 2017 51.6 99.1M yaml weights
YOLOX Darknet53 8 * 8 640 MS COCO 2017 47.7 63.7M yaml weights
performance tested on Ascend 910*(8p)
Name Scale BatchSize ImageSize Dataset Box mAP (%) ms/step Params Recipe Download
YOLOv10 N 32 * 8 640 MS COCO 2017 38.3 513.63 2.8M yaml weights
YOLOv10 S 32 * 8 640 MS COCO 2017 45.7 503.38 8.2M yaml weights
YOLOv10 M 32 * 8 640 MS COCO 2017 50.7 560.81 16.6M yaml weights
YOLOv10 B 32 * 8 640 MS COCO 2017 52.0 695.69 20.6M yaml weights
YOLOv10 L 32 * 8 640 MS COCO 2017 52.6 782.61 25.9M yaml weights
YOLOv10 X 20 * 8 640 MS COCO 2017 53.7 650.63 31.8M yaml weights
YOLOv9 T 16 * 8 640 MS COCO 2017 37.3 350 2.0M yaml [ weights
YOLOv9 S 16 * 8 640 MS COCO 2017 46.3 377 7.1M yaml [ weights
YOLOv9 M 16 * 8 640 MS COCO 2017 51.4 499 20.0M yaml [ weights
YOLOv9 C 16 * 8 640 MS COCO 2017 52.6 627 25.3M yaml [ weights
YOLOv9 E 16 * 8 640 MS COCO 2017 55.1 826 57.3M yaml [ weights
YOLOv8 N 16 * 8 640 MS COCO 2017 37.3 373.55 3.2M yaml weights
YOLOv8 S 16 * 8 640 MS COCO 2017 44.7 365.53 11.2M yaml weights
YOLOv7 Tiny 16 * 8 640 MS COCO 2017 37.5 496.21 6.2M yaml weights
YOLOv5 N 32 * 8 640 MS COCO 2017 27.4 736.08 1.9M yaml weights
YOLOv5 S 32 * 8 640 MS COCO 2017 37.6 787.34 7.2M yaml weights
YOLOv5 N6 32 * 8 1280 MS COCO 2017 35.7 1543.35 3.5M yaml weights
YOLOv5 S6 32 * 8 1280 MS COCO 2017 44.4 1514.98 13.6M yaml weights
YOLOv5 M6 32 * 8 1280 MS COCO 2017 51.1 1769.17 38.5M yaml weights
YOLOv5 L6 16 * 8 1280 MS COCO 2017 53.6 894.65 82.9M yaml weights
YOLOv5 X6 8 * 8 1280 MS COCO 2017 54.4 864.43 140.9M yaml weights
YOLOv4 CSPDarknet53 16 * 8 608 MS COCO 2017 46.1 337.25 27.6M yaml weights
YOLOv3 Darknet53 16 * 8 640 MS COCO 2017 46.6 396.60 61.9M yaml weights
YOLOX S 8 * 8 640 MS COCO 2017 41.0 242.15 9.0M yaml weights

Segmentation

performance tested on Ascend 910(8p) with graph mode
Name Scale BatchSize ImageSize Dataset Box mAP (%) Mask mAP (%) Params Recipe Download
YOLOv8-seg X 16 * 8 640 MS COCO 2017 52.5 42.9 71.8M yaml weights

Depoly inference

Notes

  • Box mAP: Accuracy reported on the validation set.