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v9.6.0 - YOLOv5 v6.0 release compatibility update for YOLOv3

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@glenn-jocher glenn-jocher released this 14 Nov 21:26
· 245 commits to master since this release
v9.6.0
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This release 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 YOLOv5 v6.0 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 [email protected]: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

  • Roboflow Integration ⭐ NEW: Train YOLOv5 models directly on any Roboflow dataset with our new integration! (ultralytics/yolov5#4975 by @Jacobsolawetz)

  • YOLOv5n 'Nano' models ⭐ 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. (ultralytics/yolov5#5027 by @glenn-jocher)

  • TensorFlow and Keras Export: TensorFlow, Keras, TFLite, TF.js model export now fully integrated using python export.py --include saved_model pb tflite tfjs (ultralytics/yolov5#1127 by @zldrobit)

  • OpenCV DNN: YOLOv5 ONNX models are now compatible with both OpenCV DNN and ONNX Runtime (ultralytics/yolov5#4833 by @SamFC10).

  • Model Architecture: Updated backbones are slightly smaller, faster and more accurate.

    • Replacement of Focus() with an equivalent Conv(k=6, s=2, p=2) layer (ultralytics/yolov5#4825 by @thomasbi1) for improved exportability
    • New SPPF() replacement for SPP() layer for reduced ops (ultralytics/yolov5#4420 by @glenn-jocher)
    • Reduction in P3 backbone layer C3() repeats from 9 to 6 for improved speeds
    • Reorder places SPPF() at end of backbone
    • Reintroduction of shortcut in the last C3() backbone layer
    • Updated hyperparameters with increased mixup and copy-paste augmentation

New Results

YOLOv5-P5 640 Figure (click to expand)

Figure Notes (click to expand)
  • COCO AP val denotes [email protected]: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)

Contributors

timstokman, cgerum, and 80 other contributors
Assets 6
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