OVHcloud GPU benchmark - YOLO Prediction FP16
Go back to listYOLO is a class of real-time object detection and image segmentation model. For this benchmark we used YOLOv8 built by the company Ultralytics. We evaluate computer vision speed via our tool yolo-benchmark
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# sample="https://upload.wikimedia.org/wikipedia/commons/thumb/f/f3/2018-10-12_Wrestling_Boys_Greco-Roman_45kg_at_2018_Summer_Youth_Olympics_%E2%80%93_Medal_Ceremony_%28Martin_Rulsch%29_16.jpg/640px-2018-10-12_Wrestling_Boys_Greco-Roman_45kg_at_2018_Summer_Youth_Olympics_%E2%80%93_Medal_Ceremony_%28Martin_Rulsch%29_16.jpg"
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# sample="https://upload.wikimedia.org/wikipedia/commons/thumb/f/f3/2018-10-12_Wrestling_Boys_Greco-Roman_45kg_at_2018_Summer_Youth_Olympics_%E2%80%93_Medal_Ceremony_%28Martin_Rulsch%29_16.jpg/1280px-2018-10-12_Wrestling_Boys_Greco-Roman_45kg_at_2018_Summer_Youth_Olympics_%E2%80%93_Medal_Ceremony_%28Martin_Rulsch%29_16.jpg"
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yolo-benchmark --model yolov8n.pt predict --conf 0.25 --iou 0.7 --device cuda --half $sample
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Cloud Mercato's observations:
- The image size doesn't influence the inference performance
- In FP16, H100 is not able to perform well
- Azure H100 and OVHcloud's L4/L40S have the best timing among the panel