RTX3090 vs RTX2080ti
前回から少し実行コマンドを変更しました.
- RTX3090(×4)
time python -m torch.distributed.launch --nproc_per_node 4 train.py --batch-size 64 --data coco.yaml --weights yolov5s.pt --epochs 1
Evaluating pycocotools mAP... saving runs/train/exp11/_predictions.json... loading annotations into memory... Done (t=0.38s) creating index... index created! Loading and preparing results... DONE (t=6.68s) creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=72.68s). Accumulating evaluation results... DONE (t=15.97s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.313 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.502 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.335 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.183 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.358 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.390 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.272 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.472 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.527 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.346 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.579 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.655 real 8m44.646s user 82m46.021s sys 4m21.447s
- RTX2080ti(×3)+ Volta 100(×1)
time python -m torch.distributed.launch --nproc_per_node 4 train.py --batch-size 64 --data coco.yaml --weights yolov5s.pt --epochs 1
Evaluating pycocotools mAP... saving runs/train/exp16/_predictions.json... loading annotations into memory... Done (t=0.41s) creating index... index created! Loading and preparing results... DONE (t=6.98s) creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=90.50s). Accumulating evaluation results... DONE (t=17.48s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.311 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.501 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.336 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.185 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.355 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.387 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.273 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.471 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.525 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.348 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.577 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.651 real 9m36.666s user 84m36.826s sys 8m9.428s
