TFLite, ONNX, CoreML, TensorRT Export

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TFLite, ONNX, CoreML, TensorRT Export

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📚 This guide explains how to export a trained YOLOv5 🚀 model from PyTorch to ONNX and TorchScript formats. UPDATED 8 December 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.

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 . 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 [email protected]: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 [email protected]: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

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TFLite, ONNX, CoreML, TensorRT Export

TFLite, ONNX, CoreML, TensorRT Export

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