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Run YOLOv5 benchmarks on a PyTorch model for all supported export formats. Currently operates on CPU, future updates will implement GPU inference. For GPU benchmarks see #6963.
export.py --include
yolov5s.pt
torchscript
yolov5s.torchscript
onnx
yolov5s.onnx
openvino
yolov5s_openvino_model/
engine
yolov5s.engine
coreml
yolov5s.mlmodel
saved_model
yolov5s_saved_model/
pb
yolov5s.pb
tflite
yolov5s.tflite
edgetpu
yolov5s_edgetpu.tflite
tfjs
yolov5s_web_model/
Colab Pro+ V100 High-RAM CPU Results
MacOS Intel CPU Results (CoreML-capable)
iMac (Retina 5K, 27-inch, 2020) - 3.8 GHz 8-Core Intel Core i7
iMac (Retina 5K, 27-inch, 2014)
Ultralytics Hyperplane EPYC Milan AMD CPU Results
Resolves #6586
🛠️ PR Summary
Made with ❤️ by Ultralytics Actions
🌟 Summary
This PR introduces an enhanced export functionality, streamlines model type handling within the codebase, and adds a new benchmarking utility.
📊 Key Changes
pandas
library toexport.py
for dataframe support.export_formats
function to list supported export formats in a dataframe.models/common.py
by utilizing themodel_type
method, which relies on the new export formats dataframe..xml
files.benchmarks.py
, a new utility script to benchmark YOLOv5 across various supported export formats.val.py
to support a 'benchmark' task, which affects image padding and inference shape handling.🎯 Purpose & Impact
export_formats
function and dataframe usage aims to centralize and organize the different supported export formats, making it easier to manage them.Overall, these changes enhance the usability of the YOLOv5 export options and provide valuable insights into model performance, potentially benefiting developers and users focused on optimization and deployment of models in different environments. 🚀