Add models benchmarks
Add models benchmarks Minor fixes
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81
readme.md
81
readme.md
@@ -7,7 +7,6 @@ NVIDIA DeepStream SDK 6.0 configuration for YOLO models
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* New documentation for multiple models
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* DeepStream tutorials
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* Native PP-YOLO support
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* Models benchmark
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* GPU NMS
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* Dynamic batch-size
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@@ -21,9 +20,10 @@ NVIDIA DeepStream SDK 6.0 configuration for YOLO models
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* Support for convolutional groups
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* Support for INT8 calibration
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* Support for non square models
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* **Support for implicit and channel layers (YOLOR)**
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* **YOLOv5 6.0 native support**
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* **Initial YOLOR native support**
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* Support for implicit and channel layers (YOLOR)
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* YOLOv5 6.0 native support
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* Initial YOLOR native support
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* **Models benchmarks**
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##
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@@ -31,6 +31,7 @@ NVIDIA DeepStream SDK 6.0 configuration for YOLO models
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* [Requirements](#requirements)
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* [Tested models](#tested-models)
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* [Benchmarks](#benchmarks)
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* [dGPU installation](#dgpu-installation)
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* [Basic usage](#basic-usage)
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* [YOLOv5 usage](#yolov5-usage)
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@@ -57,25 +58,59 @@ NVIDIA DeepStream SDK 6.0 configuration for YOLO models
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##
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### Tested models
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* [YOLOR-CSP](https://github.com/WongKinYiu/yolor) [[cfg]](https://raw.githubusercontent.com/WongKinYiu/yolor/main/cfg/yolor_csp.cfg) [[pt]](https://drive.google.com/file/d/1ZEqGy4kmZyD-Cj3tEFJcLSZenZBDGiyg/view?usp=sharing)
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* [YOLOR-CSP*](https://github.com/WongKinYiu/yolor) [[cfg]](https://raw.githubusercontent.com/WongKinYiu/yolor/main/cfg/yolor_csp.cfg) [[pt]](https://drive.google.com/file/d/1OJKgIasELZYxkIjFoiqyn555bcmixUP2/view?usp=sharing)
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* [YOLOR-CSP-X](https://github.com/WongKinYiu/yolor) [[cfg]](https://raw.githubusercontent.com/WongKinYiu/yolor/main/cfg/yolor_csp_x.cfg) [[pt]](https://drive.google.com/file/d/1L29rfIPNH1n910qQClGftknWpTBgAv6c/view?usp=sharing)
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* [YOLOR-CSO-X*](https://github.com/WongKinYiu/yolor) [[cfg]](https://raw.githubusercontent.com/WongKinYiu/yolor/main/cfg/yolor_csp_x.cfg) [[pt]](https://drive.google.com/file/d/1NbMG3ivuBQ4S8kEhFJ0FIqOQXevGje_w/view?usp=sharing)
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* [YOLOv5 6.0](https://github.com/ultralytics/yolov5) [[pt]](https://github.com/ultralytics/yolov5/releases/tag/v6.0)
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* [YOLOv4x-Mish](https://github.com/AlexeyAB/darknet) [[cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4x-mish.cfg)] [[weights](https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4x-mish.weights)]
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* [YOLOv4-CSP](https://github.com/WongKinYiu/ScaledYOLOv4/tree/yolov4-csp) [[cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4-csp.cfg)] [[weights](https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-csp.weights)]
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* [YOLOv4](https://github.com/AlexeyAB/darknet) [[cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4.cfg)] [[weights](https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v3_optimal/yolov4.weights)]
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* [YOLOv4-Tiny](https://github.com/AlexeyAB/darknet) [[cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4-tiny.cfg)] [[weights](https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-tiny.weights)]
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* [YOLOv3-SPP](https://github.com/pjreddie/darknet) [[cfg](https://raw.githubusercontent.com/pjreddie/darknet/master/cfg/yolov3-spp.cfg)] [[weights](https://pjreddie.com/media/files/yolov3-spp.weights)]
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* [YOLOv3](https://github.com/pjreddie/darknet) [[cfg](https://raw.githubusercontent.com/pjreddie/darknet/master/cfg/yolov3.cfg)] [[weights](https://pjreddie.com/media/files/yolov3.weights)]
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* [YOLOv3-Tiny-PRN](https://github.com/WongKinYiu/PartialResidualNetworks) [[cfg](https://raw.githubusercontent.com/WongKinYiu/PartialResidualNetworks/master/cfg/yolov3-tiny-prn.cfg)] [[weights](https://github.com/WongKinYiu/PartialResidualNetworks/raw/master/model/yolov3-tiny-prn.weights)]
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* [YOLOv3-Tiny](https://github.com/pjreddie/darknet) [[cfg](https://raw.githubusercontent.com/pjreddie/darknet/master/cfg/yolov3-tiny.cfg)] [[weights](https://pjreddie.com/media/files/yolov3-tiny.weights)]
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* [YOLOv3-Lite](https://github.com/dog-qiuqiu/MobileNet-Yolo) [[cfg](https://raw.githubusercontent.com/dog-qiuqiu/MobileNet-Yolo/master/MobileNetV2-YOLOv3-Lite/COCO/MobileNetV2-YOLOv3-Lite-coco.cfg)] [[weights](https://github.com/dog-qiuqiu/MobileNet-Yolo/raw/master/MobileNetV2-YOLOv3-Lite/COCO/MobileNetV2-YOLOv3-Lite-coco.weights)]
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* [YOLOv3-Nano](https://github.com/dog-qiuqiu/MobileNet-Yolo) [[cfg](https://raw.githubusercontent.com/dog-qiuqiu/MobileNet-Yolo/master/MobileNetV2-YOLOv3-Nano/COCO/MobileNetV2-YOLOv3-Nano-coco.cfg)] [[weights](https://github.com/dog-qiuqiu/MobileNet-Yolo/raw/master/MobileNetV2-YOLOv3-Nano/COCO/MobileNetV2-YOLOv3-Nano-coco.weights)]
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* [YOLO-Fastest 1.1](https://github.com/dog-qiuqiu/Yolo-Fastest) [[cfg](https://raw.githubusercontent.com/dog-qiuqiu/Yolo-Fastest/master/ModelZoo/yolo-fastest-1.1_coco/yolo-fastest-1.1-xl.cfg)] [[weights](https://github.com/dog-qiuqiu/Yolo-Fastest/raw/master/ModelZoo/yolo-fastest-1.1_coco/yolo-fastest-1.1-xl.weights)]
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* [YOLO-Fastest-XL 1.1](https://github.com/dog-qiuqiu/Yolo-Fastest) [[cfg](https://raw.githubusercontent.com/dog-qiuqiu/Yolo-Fastest/master/ModelZoo/yolo-fastest-1.1_coco/yolo-fastest-1.1.cfg)] [[weights](https://github.com/dog-qiuqiu/Yolo-Fastest/raw/master/ModelZoo/yolo-fastest-1.1_coco/yolo-fastest-1.1.weights)]
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* [YOLOv2](https://github.com/pjreddie/darknet) [[cfg](https://raw.githubusercontent.com/pjreddie/darknet/master/cfg/yolov2.cfg)] [[weights](https://pjreddie.com/media/files/yolov2.weights)]
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* [YOLOv2-Tiny](https://github.com/pjreddie/darknet) [[cfg](https://raw.githubusercontent.com/pjreddie/darknet/master/cfg/yolov2-tiny.cfg)] [[weights](https://pjreddie.com/media/files/yolov2-tiny.weights)]
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* [Darknet YOLO](https://github.com/AlexeyAB/darknet)
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* [YOLOv5 6.0](https://github.com/ultralytics/yolov5)
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* [YOLOR](https://github.com/WongKinYiu/yolor)
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* [MobileNet-YOLO](https://github.com/dog-qiuqiu/MobileNet-Yolo)
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* [YOLO-Fastest](https://github.com/dog-qiuqiu/Yolo-Fastest)
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##
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### Benchmarks
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```
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nms = 0.45 (changed to beta_nms when used in Darknet cfg file) / 0.6 (YOLOv5 and YOLOR models)
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pre-cluster-threshold = 0.001 (mAP eval) / 0.25 (FPS measurement)
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batch-size = 1
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valid = val2017 (COCO) - 1000 random images for INT8 calibration
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NOTE: Used maintain-aspect-ratio=1 in config_infer file for YOLOv4 (with letter_box=1), YOLOv5 and YOLOR models.
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```
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<details><summary>NVIDIA GTX 1050 (4GB Mobile)</summary>
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| DeepStream | Precision | Resolution | IoU=0.5:0.95 | IoU=0.5 | IoU=0.75 | FPS<br />(without display) |
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|:------------------:|:---------:|:----------:|:------------:|:-------:|:--------:|:--------------------------:|
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| YOLOR-CSP-X* | FP32 | 640 | 0.473 | 0.664 | 0.513 | 7.59 |
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| YOLOR-CSP-X | FP32 | 640 | 0.470 | 0.661 | 0.507 | 7.52 |
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| YOLOR-CSP* | FP32 | 640 | 0.459 | 0.652 | 0.496 | 13.28 |
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| YOLOR-CSP | FP32 | 640 | 0.449 | 0.639 | 0.483 | 13.32 |
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| YOLOv5x6 6.0 | FP32 | 1280 | 0.504 | 0.681 | 0.547 | 2.22 |
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| YOLOv5l6 6.0 | FP32 | 1280 | 0.492 | 0.670 | 0.535 | 4.05 |
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| YOLOv5m6 6.0 | FP32 | 1280 | 0.463 | 0.642 | 0.504 | 7.54 |
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| YOLOv5s6 6.0 | FP32 | 1280 | 0.394 | 0.572 | 0.424 | 18.64 |
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| YOLOv5n6 6.0 | FP32 | 1280 | 0.294 | 0.452 | 0.314 | 26.94 |
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| YOLOv5x 6.0 | FP32 | 640 | 0.469 | 0.654 | 0.509 | 8.24 |
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| YOLOv5l 6.0 | FP32 | 640 | 0.450 | 0.634 | 0.487 | 14.96 |
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| YOLOv5m 6.0 | FP32 | 640 | 0.415 | 0.601 | 0.448 | 28.30 |
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| YOLOv5s 6.0 | FP32 | 640 | 0.334 | 0.516 | 0.355 | 63.55 |
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| YOLOv5n 6.0 | FP32 | 640 | 0.250 | 0.417 | 0.260 | 110.25 |
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| YOLOv4-P6 | FP32 | 1280 | 0.499 | 0.685 | 0.542 | 2.57 |
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| YOLOv4-P5 | FP32 | 896 | 0.472 | 0.659 | 0.513 | 5.48 |
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| YOLOv4-CSP-X-SWISH | FP32 | 640 | 0.473 | 0.664 | 0.513 | 7.51 |
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| YOLOv4-CSP-SWISH | FP32 | 640 | 0.459 | 0.652 | 0.496 | 13.13 |
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| YOLOv4x-MISH | FP32 | 640 | 0.459 | 0.650 | 0.495 | 7.53 |
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| YOLOv4-CSP | FP32 | 640 | 0.440 | 0.632 | 0.474 | 13.19 |
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| YOLOv4 | FP32 | 608 | 0.498 | 0.740 | 0.549 | 12.18 |
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| YOLOv4-Tiny | FP32 | 416 | 0.215 | 0.403 | 0.206 | 201.20 |
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| YOLOv3-SPP | FP32 | 608 | 0.411 | 0.686 | 0.433 | 12.22 |
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| YOLOv3-Tiny-PRN | FP32 | 416 | 0.167 | 0.382 | 0.125 | 277.14 |
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| YOLOv3 | FP32 | 608 | 0.377 | 0.672 | 0.385 | 12.51 |
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| YOLOv3-Tiny | FP32 | 416 | 0.095 | 0.203 | 0.079 | 218.42 |
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| YOLOv2 | FP32 | 608 | 0.286 | 0.541 | 0.273 | 25.28 |
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| YOLOv2-Tiny | FP32 | 416 | 0.102 | 0.258 | 0.061 | 231.36 |
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</details>
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##
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