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

• Roboflow Integration ⭐ NEW: Train YOLOv5 models directly on any Roboflow dataset with our new integration! (https://github.com/ultralytics/yolov5/issues/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. (https://github.com/ultralytics/yolov5/discussions/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 (https://github.com/ultralytics/yolov5/pull/1127 by @zldrobit)

• OpenCV DNN: YOLOv5 ONNX models are now compatible with both OpenCV DNN and ONNX Runtime (https://github.com/ultralytics/yolov5/pull/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 (https://github.com/ultralytics/yolov5/issues/4825 by @thomasbi1) for improved exportability
  • New SPPF() replacement for SPP() layer for reduced ops (https://github.com/ultralytics/yolov5/pull/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

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 | |1280 |51.0 |69.0 |887 |11.1 |6.8 |35.7 |50.0 | |1280 |53.6 |71.6 |1784 |15.8 |10.5 |76.8 |111.4 |+ |12801536 |54.7 |72.3 |3136- |26.2- |19.4- |140.7- |209.8-}

Changelog

Changes between previous release and this release: https://github.com/ultralytics/yolov5/compare/v5.0...v6.0 Changes since this release: https://github.com/ultralytics/yolov5/compare/v6.0...HEAD