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
<sup>(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
-</sup>

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