v6.1

版本发布时间: 2022-02-22 19:35:57

ultralytics/yolov5最新发布版本:v7.0(2022-11-22 23:29:09)

This release incorporates many new features and bug fixes (271 PRs from 48 contributors) since our last release in October 2021. It adds TensorRT, Edge TPU and OpenVINO support, and provides retrained models at --batch-size 128 with new default one-cycle linear LR scheduler. YOLOv5 now officially supports 11 different formats, not just for export but for inference (both detect.py and PyTorch Hub), and validation to profile mAP and speed results after export.

Format	`export.py --include`	Model
PyTorch	-	`yolov5s.pt`
TorchScript	`torchscript`	`yolov5s.torchscript`
ONNX	`onnx`	`yolov5s.onnx`
OpenVINO	`openvino`	`yolov5s_openvino_model/`
TensorRT	`engine`	`yolov5s.engine`
CoreML	`coreml`	`yolov5s.mlmodel`
TensorFlow SavedModel	`saved_model`	`yolov5s_saved_model/`
TensorFlow GraphDef	`pb`	`yolov5s.pb`
TensorFlow Lite	`tflite`	`yolov5s.tflite`
TensorFlow Edge TPU	`edgetpu`	`yolov5s_edgetpu.tflite`
TensorFlow.js	`tfjs`	`yolov5s_web_model/`

Usage examples (ONNX shown):

Export:          python export.py --weights yolov5s.pt --include onnx
Detect:          python detect.py --weights yolov5s.onnx
PyTorch Hub:     model = torch.hub.load('ultralytics/yolov5', 'custom', 'yolov5s.onnx')
Validate:        python val.py --weights yolov5s.onnx
Visualize:       https://netron.app

Important Updates

TensorRT support: TensorFlow, Keras, TFLite, TF.js model export now fully integrated using python export.py --include saved_model pb tflite tfjs (https://github.com/ultralytics/yolov5/pull/5699 by @imyhxy)
Tensorflow Edge TPU support ⭐ NEW: New smaller YOLOv5n (1.9M params) model below YOLOv5s (7.5M params), exports to 2.1 MB INT8 size, ideal for ultralight mobile solutions. (https://github.com/ultralytics/yolov5/pull/3630 by @zldrobit)
OpenVINO support: YOLOv5 ONNX models are now compatible with both OpenCV DNN and ONNX Runtime (https://github.com/ultralytics/yolov5/pull/6057 by @glenn-jocher).
Export Benchmarks: Benchmark (mAP and speed) all YOLOv5 export formats with python utils/benchmarks.py --weights yolov5s.pt. Currently operates on CPU, future updates will implement GPU support. (https://github.com/ultralytics/yolov5/pull/6613 by @glenn-jocher).
Architecture: no changes
Hyperparameters: minor change
- hyp-scratch-large.yaml lrf reduced from 0.2 to 0.1 (https://github.com/ultralytics/yolov5/pull/6525 by @glenn-jocher).
Training: Default Learning Rate (LR) scheduler updated
- One-cycle with cosine replace with one-cycle linear for improved results (https://github.com/ultralytics/yolov5/pull/6729 by @glenn-jocher).

New Results

All model trainings logged to https://wandb.ai/glenn-jocher/YOLOv5_v61_official

YOLOv5-P5 640 Figure (click to expand)

Figure Notes (click to expand)

COCO AP val denotes mAP@0.5:0.95 metric measured on the 5000-image COCO val2017 dataset over various inference sizes from 256 to 1536.
GPU Speed measures average inference time per image on COCO val2017 dataset using a AWS p3.2xlarge V100 instance at batch-size 32.
EfficientDet data from google/automl at batch size 8.
Reproduce by python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5n6.pt yolov5s6.pt yolov5m6.pt yolov5l6.pt yolov5x6.pt

Example YOLOv5l before and after metrics:

YOLOv5l ^Large	size ^(pixels)	mAP^val 0.5:0.95	mAP^val 0.5	Speed ^{CPU b1 (ms)}	Speed ^{V100 b1 (ms)}	Speed ^{V100 b32 (ms)}	params ^(M)	FLOPs ^{@640 (B)}
v5.0	640	48.2	66.9	457.9	11.6	2.8	47.0	115.4
v6.0 (previous)	640	48.8	67.2	424.5	10.9	2.7	46.5	109.1
v6.1 (this release)	640	49.0	67.3	430.0	10.1	2.7	46.5	109.1

Pretrained Checkpoints

Model	size ^(pixels)	mAP^val 0.5:0.95	mAP^val 0.5	Speed ^{CPU b1 (ms)}	Speed ^{V100 b1 (ms)}	Speed ^{V100 b32 (ms)}	params ^(M)	FLOPs ^{@640 (B)}
YOLOv5n	640	28.0	45.7	45	6.3	0.6	1.9	4.5
YOLOv5s	640	37.4	56.8	98	6.4	0.9	7.2	16.5
YOLOv5m	640	45.4	64.1	224	8.2	1.7	21.2	49.0
YOLOv5l	640	49.0	67.3	430	10.1	2.7	46.5	109.1
YOLOv5x	640	50.7	68.9	766	12.1	4.8	86.7	205.7

YOLOv5n6	1280	36.0	54.4	153	8.1	2.1	3.2	4.6
YOLOv5s6	1280	44.8	63.7	385	8.2	3.6	12.6	16.8
YOLOv5m6	1280	51.3	69.3	887	11.1	6.8	35.7	50.0
YOLOv5l6	1280	53.7	71.3	1784	15.8	10.5	76.8	111.4
YOLOv5x6 + TTA	1280 1536	55.0 55.8	72.7 72.7	3136 -	26.2 -	19.4 -	140.7 -	209.8 -

Table Notes (click to expand)

All checkpoints are trained to 300 epochs with default settings. Nano and Small models use hyp.scratch-low.yaml hyps, all others use hyp.scratch-high.yaml.
mAP^val values are for single-model single-scale on COCO val2017 dataset.
Reproduce by python val.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65
Speed averaged over COCO val images using a AWS p3.2xlarge instance. NMS times (~1 ms/img) not included.
Reproduce by python val.py --data coco.yaml --img 640 --task speed --batch 1
TTA Test Time Augmentation includes reflection and scale augmentations.
Reproduce by python val.py --data coco.yaml --img 1536 --iou 0.7 --augment