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TorchScript, ONNX, CoreML Export

📚 This guide explains how to export a trained YOLOv5 🚀 model from PyTorch to ONNX and TorchScript formats. UPDATED 25 September 2022.

Before You Start

Clone repo and install requirements.txt in a Python>=3.7.0 environment, including PyTorch>=1.7. Models and datasets download automatically from the latest YOLOv5 release.

git clone https://github.com/ultralytics/yolov5  # clone
cd yolov5
pip install -r requirements.txt  # install

For TensorRT export example (requires GPU) see our Colab notebook appendix section. Open In Colab

Formats

YOLOv5 inference is officially supported in 11 formats:

💡 ProTip: Export to ONNX or OpenVINO for up to 3x CPU speedup. See CPU Benchmarks. 💡 ProTip: Export to TensorRT for up to 5x GPU speedup. See GPU Benchmarks.

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/
PaddlePaddle paddle yolov5s_paddle_model/

Benchmarks

Benchmarks below run on a Colab Pro with the YOLOv5 tutorial notebook Open In Colab. To reproduce:

python benchmarks.py --weights yolov5s.pt --imgsz 640 --device 0

Colab Pro V100 GPU

benchmarks: weights=/content/yolov5/yolov5s.pt, imgsz=640, batch_size=1, data=/content/yolov5/data/coco128.yaml, device=0, half=False, test=False
Checking setup...
YOLOv5 🚀 v6.1-135-g7926afc torch 1.10.0+cu111 CUDA:0 (Tesla V100-SXM2-16GB, 16160MiB)
Setup complete ✅ (8 CPUs, 51.0 GB RAM, 46.7/166.8 GB disk)

Benchmarks complete (458.07s)
                   Format  mAP@0.5:0.95  Inference time (ms)
0                 PyTorch        0.4623                10.19
1             TorchScript        0.4623                 6.85
2                    ONNX        0.4623                14.63
3                OpenVINO           NaN                  NaN
4                TensorRT        0.4617                 1.89
5                  CoreML           NaN                  NaN
6   TensorFlow SavedModel        0.4623                21.28
7     TensorFlow GraphDef        0.4623                21.22
8         TensorFlow Lite           NaN                  NaN
9     TensorFlow Edge TPU           NaN                  NaN
10          TensorFlow.js           NaN                  NaN

Colab Pro CPU

benchmarks: weights=/content/yolov5/yolov5s.pt, imgsz=640, batch_size=1, data=/content/yolov5/data/coco128.yaml, device=cpu, half=False, test=False
Checking setup...
YOLOv5 🚀 v6.1-135-g7926afc torch 1.10.0+cu111 CPU
Setup complete ✅ (8 CPUs, 51.0 GB RAM, 41.5/166.8 GB disk)

Benchmarks complete (241.20s)
                   Format  mAP@0.5:0.95  Inference time (ms)
0                 PyTorch        0.4623               127.61
1             TorchScript        0.4623               131.23
2                    ONNX        0.4623                69.34
3                OpenVINO        0.4623                66.52
4                TensorRT           NaN                  NaN
5                  CoreML           NaN                  NaN
6   TensorFlow SavedModel        0.4623               123.79
7     TensorFlow GraphDef        0.4623               121.57
8         TensorFlow Lite        0.4623               316.61
9     TensorFlow Edge TPU           NaN                  NaN
10          TensorFlow.js           NaN                  NaN

Export a Trained YOLOv5 Model

This command exports a pretrained YOLOv5s model to TorchScript and ONNX formats. yolov5s.pt is the 'small' model, the second smallest model available. Other options are yolov5n.pt, yolov5m.pt, yolov5l.pt and yolov5x.pt, along with their P6 counterparts i.e. yolov5s6.pt or you own custom training checkpoint i.e. runs/exp/weights/best.pt. For details on all available models please see our README table.

python export.py --weights yolov5s.pt --include torchscript onnx

💡 ProTip: Add --half to export models at FP16 half precision for smaller file sizes

Output:

export: data=data/coco128.yaml, weights=['yolov5s.pt'], imgsz=[640, 640], batch_size=1, device=cpu, half=False, inplace=False, train=False, keras=False, optimize=False, int8=False, dynamic=False, simplify=False, opset=12, verbose=False, workspace=4, nms=False, agnostic_nms=False, topk_per_class=100, topk_all=100, iou_thres=0.45, conf_thres=0.25, include=['torchscript', 'onnx']
YOLOv5 🚀 v6.2-104-ge3e5122 Python-3.7.13 torch-1.12.1+cu113 CPU

Downloading https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5s.pt to yolov5s.pt...
100% 14.1M/14.1M [00:00<00:00, 274MB/s]

Fusing layers... 
YOLOv5s summary: 213 layers, 7225885 parameters, 0 gradients

PyTorch: starting from yolov5s.pt with output shape (1, 25200, 85) (14.1 MB)

TorchScript: starting export with torch 1.12.1+cu113...
TorchScript: export success ✅ 1.7s, saved as yolov5s.torchscript (28.1 MB)

ONNX: starting export with onnx 1.12.0...
ONNX: export success ✅ 2.3s, saved as yolov5s.onnx (28.0 MB)

Export complete (5.5s)
Results saved to /content/yolov5
Detect:          python detect.py --weights yolov5s.onnx 
Validate:        python val.py --weights yolov5s.onnx 
PyTorch Hub:     model = torch.hub.load('ultralytics/yolov5', 'custom', 'yolov5s.onnx')
Visualize:       https://netron.app/

The 3 exported models will be saved alongside the original PyTorch model:

Netron Viewer is recommended for visualizing exported models:

Example Usage of exported models

export.py will show Usage examples for the last export format indicated. For example for ONNX:

detect.py runs inference on exported models:

python path/to/detect.py --weights yolov5s.pt                 # PyTorch
                                   yolov5s.torchscript        # TorchScript
                                   yolov5s.onnx               # ONNX Runtime or OpenCV DNN with --dnn
                                   yolov5s.xml                # OpenVINO
                                   yolov5s.engine             # TensorRT
                                   yolov5s.mlmodel            # CoreML (macOS only)
                                   yolov5s_saved_model        # TensorFlow SavedModel
                                   yolov5s.pb                 # TensorFlow GraphDef
                                   yolov5s.tflite             # TensorFlow Lite
                                   yolov5s_edgetpu.tflite     # TensorFlow Edge TPU
                                   yolov5s_paddle_model       # PaddlePaddle

val.py runs validation on exported models:

python path/to/val.py --weights yolov5s.pt                 # PyTorch
                                yolov5s.torchscript        # TorchScript
                                yolov5s.onnx               # ONNX Runtime or OpenCV DNN with --dnn
                                yolov5s.xml                # OpenVINO
                                yolov5s.engine             # TensorRT
                                yolov5s.mlmodel            # CoreML (macOS Only)
                                yolov5s_saved_model        # TensorFlow SavedModel
                                yolov5s.pb                 # TensorFlow GraphDef
                                yolov5s.tflite             # TensorFlow Lite
                                yolov5s_edgetpu.tflite     # TensorFlow Edge TPU
                                yolov5s_paddle_model       # PaddlePaddle

OpenCV DNN inference

OpenCV inference with ONNX models:

python export.py --weights yolov5s.pt --include onnx

python detect.py --weights yolov5s.onnx --dnn  # detect
python val.py --weights yolov5s.onnx --dnn  # validate

C++ Inference

YOLOv5 OpenCV DNN C++ inference on exported ONNX model examples: - https://github.com/Hexmagic/ONNX-yolov5/blob/master/src/test.cpp - https://github.com/doleron/yolov5-opencv-cpp-python

YOLOv5 OpenVINO C++ inference examples: - https://github.com/dacquaviva/yolov5-openvino-cpp-python - https://github.com/UNeedCryDear/yolov5-seg-opencv-dnn-cpp

Environments

YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):

Status

YOLOv5 CI

If this badge is green, all YOLOv5 GitHub Actions Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training, validation, inference, export and benchmarks on MacOS, Windows, and Ubuntu every 24 hours and on every commit.