--- comments: true description: Learn to export YOLOv5 models to various formats like TFLite, ONNX, CoreML and TensorRT. Increase model efficiency and deployment flexibility with our step-by-step guide. keywords: YOLOv5 export, TFLite, ONNX, CoreML, TensorRT, model conversion, YOLOv5 tutorial, PyTorch export --- # TFLite, ONNX, CoreML, TensorRT Export 📚 This guide explains how to export a trained YOLOv5 🚀 model from PyTorch to ONNX and TorchScript formats. ## Before You Start Clone repo and install [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) in a [**Python>=3.8.0**](https://www.python.org/) environment, including [**PyTorch>=1.8**](https://pytorch.org/get-started/locally/). [Models](https://github.com/ultralytics/yolov5/tree/master/models) and [datasets](https://github.com/ultralytics/yolov5/tree/master/data) download automatically from the latest YOLOv5 [release](https://github.com/ultralytics/yolov5/releases). ```bash git clone https://github.com/ultralytics/yolov5 # clone cd yolov5 pip install -r requirements.txt # install ``` For [TensorRT](https://developer.nvidia.com/tensorrt) export example (requires GPU) see our Colab [notebook](https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb#scrollTo=VTRwsvA9u7ln&line=2&uniqifier=1) appendix section.

. To reproduce: ```bash 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](https://github.com/ultralytics/yolov5#pretrained-checkpoints). ```bash python export.py --weights yolov5s.pt --include torchscript onnx ``` 💡 ProTip: Add `--half` to export models at FP16 half precision for smaller file sizes Output: ```bash 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-3.8.0 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:

YOLO export locations

[Netron Viewer](https://github.com/lutzroeder/netron) is recommended for visualizing exported models:

YOLO model visualization

## Exported Model Usage Examples `detect.py` runs inference on exported models: ```bash python detect.py --weights yolov5s.pt # PyTorch yolov5s.torchscript # TorchScript yolov5s.onnx # ONNX Runtime or OpenCV DNN with dnn=True yolov5s_openvino_model # 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: ```bash python val.py --weights yolov5s.pt # PyTorch yolov5s.torchscript # TorchScript yolov5s.onnx # ONNX Runtime or OpenCV DNN with dnn=True yolov5s_openvino_model # 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 ``` Use PyTorch Hub with exported YOLOv5 models: ```python import torch # Model model = torch.hub.load("ultralytics/yolov5", "custom", "yolov5s.pt") model = torch.hub.load("ultralytics/yolov5", "custom", "yolov5s.torchscript ") # TorchScript model = torch.hub.load("ultralytics/yolov5", "custom", "yolov5s.onnx") # ONNX Runtime model = torch.hub.load("ultralytics/yolov5", "custom", "yolov5s_openvino_model") # OpenVINO model = torch.hub.load("ultralytics/yolov5", "custom", "yolov5s.engine") # TensorRT model = torch.hub.load("ultralytics/yolov5", "custom", "yolov5s.mlmodel") # CoreML (macOS Only) model = torch.hub.load("ultralytics/yolov5", "custom", "yolov5s_saved_model") # TensorFlow SavedModel model = torch.hub.load("ultralytics/yolov5", "custom", "yolov5s.pb") # TensorFlow GraphDef model = torch.hub.load("ultralytics/yolov5", "custom", "yolov5s.tflite") # TensorFlow Lite model = torch.hub.load("ultralytics/yolov5", "custom", "yolov5s_edgetpu.tflite") # TensorFlow Edge TPU model = torch.hub.load("ultralytics/yolov5", "custom", "yolov5s_paddle_model") # PaddlePaddle # Images img = "https://ultralytics.com/images/zidane.jpg" # or file, Path, PIL, OpenCV, numpy, list # Inference results = model(img) # Results results.print() # or .show(), .save(), .crop(), .pandas(), etc. ``` ## OpenCV DNN inference OpenCV inference with ONNX models: ```bash 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/Hexmagic/ONNX-yolov5/blob/master/src/test.cpp) - [https://github.com/doleron/yolov5-opencv-cpp-python](https://github.com/doleron/yolov5-opencv-cpp-python) YOLOv5 OpenVINO C++ inference examples: - [https://github.com/dacquaviva/yolov5-openvino-cpp-python](https://github.com/dacquaviva/yolov5-openvino-cpp-python) - [https://github.com/UNeedCryDear/yolov5-seg-opencv-dnn-cpp](https://github.com/UNeedCryDear/yolov5-seg-opencv-onnxruntime-cpp) ## TensorFlow.js Web Browser Inference - [https://aukerul-shuvo.github.io/YOLOv5_TensorFlow-JS/](https://aukerul-shuvo.github.io/YOLOv5_TensorFlow-JS/) ## Supported Environments Ultralytics provides a range of ready-to-use environments, each pre-installed with essential dependencies such as [CUDA](https://developer.nvidia.com/cuda-zone), [CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/), and [PyTorch](https://pytorch.org/), to kickstart your projects. - **Free GPU Notebooks**:

- **Google Cloud**: [GCP Quickstart Guide](../environments/google_cloud_quickstart_tutorial.md) - **Amazon**: [AWS Quickstart Guide](../environments/aws_quickstart_tutorial.md) - **Azure**: [AzureML Quickstart Guide](../environments/azureml_quickstart_tutorial.md) - **Docker**: [Docker Quickstart Guide](../environments/docker_image_quickstart_tutorial.md)

## Project Status

This badge indicates that all [YOLOv5 GitHub Actions](https://github.com/ultralytics/yolov5/actions) Continuous Integration (CI) tests are successfully passing. These CI tests rigorously check the functionality and performance of YOLOv5 across various key aspects: [training](https://github.com/ultralytics/yolov5/blob/master/train.py), [validation](https://github.com/ultralytics/yolov5/blob/master/val.py), [inference](https://github.com/ultralytics/yolov5/blob/master/detect.py), [export](https://github.com/ultralytics/yolov5/blob/master/export.py), and [benchmarks](https://github.com/ultralytics/yolov5/blob/master/benchmarks.py). They ensure consistent and reliable operation on macOS, Windows, and Ubuntu, with tests conducted every 24 hours and upon each new commit.