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

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 |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
<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 |28.0 |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
-</sup>

Changelog

Changes between previous release and this release: https://github.com/ultralytics/yolov5/compare/v6.0...v6.1 Changes since this release: https://github.com/ultralytics/yolov5/compare/v6.1...HEAD

Full Changelog: https://github.com/ultralytics/yolov5/compare/v6.0...v6.1