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YOLOv5 v6.0 compatibility update #1857

Merged
merged 9 commits into from
Nov 14, 2021
Merged

YOLOv5 v6.0 compatibility update #1857

merged 9 commits into from
Nov 14, 2021

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@glenn-jocher glenn-jocher commented Nov 14, 2021 •
edited by UltralyticsAssistant
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This PR merges the most recent updates to YOLOv5 🚀 from the October 12th, 2021 YOLOv5 v6.0 release into this Ultralytics YOLOv3 repository. This is part of Ultralytics YOLOv3 maintenance and takes place on every major YOLOv5 release. Full details on the YOLOv5 v6.0 release are below.

https://github.com/ultralytics/yolov5/releases/tag/v6.0

This release incorporates many new features and bug fixes (465 PRs from 73 contributors) since our last release v5.0 in April, brings architecture tweaks, and also introduces new P5 and P6 'Nano' models: YOLOv5n and YOLOv5n6. Nano models maintain the YOLOv5s depth multiple of 0.33 but reduce the YOLOv5s width multiple from 0.50 to 0.25, resulting in ~75% fewer parameters, from 7.5M to 1.9M, ideal for mobile and CPU solutions.

Example usage:

python detect.py --weights yolov5n.pt --img 640    # Nano P5 model trained at --img 640 (28.4 mAP@0.5:0.95)
python detect.py --weights yolov5n6.pt --img 1280  # Nano P6 model trained at --img 1280 (34.0 mAP0.5:0.95)

Important Updates

New Results

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

mAP improves from +0.3% to +1.1% across all models, and ~5% FLOPs reduction produces slight speed improvements and a reduced CUDA memory footprint. Example YOLOv5l before and after metrics:

YOLOv5l
Large		 size
(pixels)		 mAPval
0.5:0.95		 mAPval
0.5		 Speed
CPU b1
(ms)		 Speed
V100 b1
(ms)		 Speed
V100 b32
(ms)		 params
(M)		 FLOPs
@640 (B)
v5.0 (previous) 640 48.2 66.9 457.9 11.6 2.8 47.0 115.4
v6.0 (this release) 640 48.8 67.2 424.5 10.9 2.7 46.5 109.1

Pretrained Checkpoints

Model size
(pixels)		 mAPval
0.5:0.95		 mAPval
0.5		 Speed
CPU b1
(ms)		 Speed
V100 b1
(ms)		 Speed
V100 b32
(ms)		 params
(M)		 FLOPs
@640 (B)
YOLOv5n 640 28.4 46.0 45 6.3 0.6 1.9 4.5
YOLOv5s 640 37.2 56.0 98 6.4 0.9 7.2 16.5
YOLOv5m 640 45.2 63.9 224 8.2 1.7 21.2 49.0
YOLOv5l 640 48.8 67.2 430 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 34.0 50.7 153 8.1 2.1 3.2 4.6
YOLOv5s6 1280 44.5 63.0 385 8.2 3.6 16.8 12.6
YOLOv5m6 1280 51.0 69.0 887 11.1 6.8 35.7 50.0
YOLOv5l6 1280 53.6 71.6 1784 15.8 10.5 76.8 111.4
YOLOv5x6
+ TTA		 1280
1536		 54.7
55.4		 72.4
72.3		 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 models use hyp.scratch-low.yaml hyperparameters, all others use hyp.scratch-high.yaml.
  • mAPval 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 --conf 0.25 --iou 0.45
  • TTA Test Time Augmentation includes reflection and scale augmentations.
    Reproduce by python val.py --data coco.yaml --img 1536 --iou 0.7 --augment

Changelog

Changes between previous release and this release: ultralytics/yolov5@v5.0...v6.0
Changes since this release: ultralytics/yolov5@v6.0...HEAD

New Features and Bug Fixes (465)
New Contributors (73)

🛠️ PR Summary

Made with ❤️ by Ultralytics Actions

WARNING ⚠️ this PR is very large, summary may not cover all changes.

🌟 Summary

Export a YOLOv3 model to various formats for deployment.

📊 Key Changes

  • New export script supports exporting YOLOv3 to TorchScript, ONNX, CoreML, TensorFlow, and other formats.
  • Added automation for tracing the model, simplifying, and converting for different export formats.
  • Implemented export functionality for TensorFlow Lite, TensorFlow.js, and CoreML for optimized mobile deployment.

🎯 Purpose & Impact

  • Enables the use of YOLOv3 models in various production environments.
  • Facilitates cross-platform deployment, including edge devices and web applications.
  • Enhancement in user experience by providing a single script to handle multiple export formats.

@glenn-jocher glenn-jocher added the documentation Improvements or additions to documentation label Nov 14, 2021
@glenn-jocher glenn-jocher merged commit 7eb23e3 into master Nov 14, 2021
@glenn-jocher glenn-jocher deleted the update/yolov5_v6.0_release branch November 14, 2021 21:23
@Forever518 Forever518 mentioned this pull request Dec 31, 2021
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rsundar96 pushed a commit to kit-ar/yolov3 that referenced this pull request Feb 18, 2022
@glenn-jocher
Merge YOLOv5 v6.0 compatibility update (ultralytics#1857) into kitar-…
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