# Benchmarks ### Config ``` board = NVIDIA Tesla V100 16GB (AWS: p3.2xlarge) batch-size = 1 eval = val2017 (COCO) sample = 1920x1080 video ``` **NOTE**: Used maintain-aspect-ratio=1 in config_infer file for Darknet (with letter_box=1) and PyTorch models. ### NMS config - Eval ``` nms-iou-threshold = 0.6 (Darknet) / 0.65 (YOLOv5, YOLOv6, YOLOv7, YOLOR and YOLOX) / 0.7 (Paddle, YOLO-NAS, DAMO-YOLO, YOLOv8 and YOLOv7-u6) pre-cluster-threshold = 0.001 topk = 300 ``` - Test ``` nms-iou-threshold = 0.45 pre-cluster-threshold = 0.25 topk = 300 ``` ### Results **NOTE**: * = PyTorch. **NOTE**: ** = The YOLOv4 is trained with the trainvalno5k set, so the mAP is high on val2017 test. **NOTE**: star = DAMO-YOLO model trained with distillation. **NOTE**: The V100 GPU decoder max out at 625-635 FPS on DeepStream even using lighter models. **NOTE**: The GPU bbox parser is a bit slower than CPU bbox parser on V100 GPU tests. | DeepStream | Precision | Resolution | IoU=0.5:0.95 | IoU=0.5 | IoU=0.75 | FPS
(without display) | |:------------------:|:---------:|:----------:|:------------:|:-------:|:--------:|:--------------------------:| | YOLO-NAS L | FP16 | 640 | 0.484 | 0.658 | 0.532 | 235.27 | | YOLO-NAS M | FP16 | 640 | 0.480 | 0.651 | 0.524 | 287.39 | | YOLO-NAS S | FP16 | 640 | 0.442 | 0.614 | 0.485 | 478.52 | | PP-YOLOE+_x | FP16 | 640 | 0.528 | 0.705 | 0.579 | 121.17 | | PP-YOLOE+_l | FP16 | 640 | 0.511 | 0.686 | 0.557 | 191.82 | | PP-YOLOE+_m | FP16 | 640 | 0.483 | 0.658 | 0.528 | 264.39 | | PP-YOLOE+_s | FP16 | 640 | 0.424 | 0.594 | 0.464 | 476.13 | | PP-YOLOE-s (400) | FP16 | 640 | 0.423 | 0.589 | 0.463 | 461.23 | | DAMO-YOLO-L star | FP16 | 640 | 0.502 | 0.674 | 0.551 | 176.93 | | DAMO-YOLO-M star | FP16 | 640 | 0.485 | 0.656 | 0.530 | 242.24 | | DAMO-YOLO-S star | FP16 | 640 | 0.460 | 0.631 | 0.502 | 385.09 | | DAMO-YOLO-S | FP16 | 640 | 0.445 | 0.611 | 0.486 | 378.68 | | DAMO-YOLO-T star | FP16 | 640 | 0.419 | 0.586 | 0.455 | 492.24 | | DAMO-YOLO-Nl | FP16 | 416 | 0.392 | 0.559 | 0.423 | 483.73 | | DAMO-YOLO-Nm | FP16 | 416 | 0.371 | 0.532 | 0.402 | 555.94 | | DAMO-YOLO-Ns | FP16 | 416 | 0.312 | 0.460 | 0.335 | 627.67 | | YOLOX-x | FP16 | 640 | 0.447 | 0.616 | 0.483 | 125.40 | | YOLOX-l | FP16 | 640 | 0.430 | 0.598 | 0.466 | 193.10 | | YOLOX-m | FP16 | 640 | 0.397 | 0.566 | 0.431 | 298.61 | | YOLOX-s | FP16 | 640 | 0.335 | 0.502 | 0.365 | 522.05 | | YOLOX-s legacy | FP16 | 640 | 0.375 | 0.569 | 0.407 | 518.52 | | YOLOX-Darknet | FP16 | 640 | 0.414 | 0.595 | 0.453 | 212.88 | | YOLOX-Tiny | FP16 | 640 | 0.274 | 0.427 | 0.292 | 633.95 | | YOLOX-Nano | FP16 | 640 | 0.212 | 0.342 | 0.222 | 633.04 | | YOLOv8x | FP16 | 640 | 0.499 | 0.669 | 0.545 | 130.49 | | YOLOv8l | FP16 | 640 | 0.491 | 0.660 | 0.535 | 180.75 | | YOLOv8m | FP16 | 640 | 0.468 | 0.637 | 0.510 | 278.08 | | YOLOv8s | FP16 | 640 | 0.415 | 0.578 | 0.453 | 493.45 | | YOLOv8n | FP16 | 640 | 0.343 | 0.492 | 0.373 | 627.43 | | YOLOv7-u6 | FP16 | 640 | 0.484 | 0.652 | 0.530 | 193.54 | | YOLOv7x* | FP16 | 640 | 0.496 | 0.679 | 0.536 | 155.07 | | YOLOv7* | FP16 | 640 | 0.476 | 0.660 | 0.518 | 226.01 | | YOLOv7-Tiny Leaky* | FP16 | 640 | 0.345 | 0.516 | 0.372 | 626.23 | | YOLOv7-Tiny Leaky* | FP16 | 416 | 0.328 | 0.493 | 0.349 | 633.90 | | YOLOv6-L 4.0 | FP16 | 640 | 0.490 | 0.671 | 0.535 | 178.41 | | YOLOv6-M 4.0 | FP16 | 640 | 0.460 | 0.635 | 0.502 | 293.39 | | YOLOv6-S 4.0 | FP16 | 640 | 0.416 | 0.585 | 0.453 | 513.90 | | YOLOv6-N 4.0 | FP16 | 640 | 0.349 | 0.503 | 0.378 | 633.37 | | YOLOv5x 7.0 | FP16 | 640 | 0.471 | 0.652 | 0.513 | 149.93 | | YOLOv5l 7.0 | FP16 | 640 | 0.455 | 0.637 | 0.497 | 235.55 | | YOLOv5m 7.0 | FP16 | 640 | 0.421 | 0.604 | 0.459 | 351.69 | | YOLOv5s 7.0 | FP16 | 640 | 0.344 | 0.529 | 0.372 | 618.13 | | YOLOv5n 7.0 | FP16 | 640 | 0.247 | 0.414 | 0.257 | 629.66 |