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| #!/usr/bin/env bash | ||
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| DATA_DIR=/code_execution/data | ||
| SUBMISSION_PATH=/code_execution/submission/submission.csv | ||
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| # call our script (main.py in this case) and tell it where the data and submission live | ||
| cd yolov9 | ||
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| #Compile the project | ||
| g++ -std=c++17 -lstdc++fs -o yolo_ort \ | ||
| -Iinclude/ \ | ||
| -I ../onnxruntime-linux-x64-1.12.0/include \ | ||
| -I ../opencv-4.9.0/include \ | ||
| -I ../opencv-4.9.0/build \ | ||
| -I ../opencv-4.9.0/modules/calib3d/include \ | ||
| -I ../opencv-4.9.0/modules/dnn/include \ | ||
| -I ../opencv-4.9.0/modules/core/include \ | ||
| -I ../opencv-4.9.0/modules/features2d/include \ | ||
| -I ../opencv-4.9.0/modules/flann/include \ | ||
| -I ../opencv-4.9.0/modules/highgui/include \ | ||
| -I ../opencv-4.9.0/modules/gapi/include \ | ||
| -I ../opencv-4.9.0/modules/imgcodecs/include \ | ||
| -I ../opencv-4.9.0/modules/imgproc/include \ | ||
| -I ../opencv-4.9.0/modules/ml/include \ | ||
| -I ../opencv-4.9.0/modules/objdetect/include \ | ||
| -I ../opencv-4.9.0/modules/photo/include \ | ||
| -I ../opencv-4.9.0/modules/stitching/include \ | ||
| -I ../opencv-4.9.0/modules/video/include \ | ||
| -I ../opencv-4.9.0/modules/ts/include \ | ||
| -I ../opencv-4.9.0/modules/videoio/include \ | ||
| src/detector.cpp \ | ||
| src/utils.cpp \ | ||
| src/main.cpp \ | ||
| -L ../onnxruntime-linux-x64-1.12.0/lib \ | ||
| -L ../opencv-4.9.0/build/lib \ | ||
| -Wl,-rpath,../onnxruntime-linux-x64-1.12.0/lib \ | ||
| -Wl,-rpath,../opencv-4.9.0/build/lib \ | ||
| -l onnxruntime \ | ||
| -l opencv_calib3d \ | ||
| -l opencv_dnn \ | ||
| -l opencv_core \ | ||
| -l opencv_features2d \ | ||
| -l opencv_flann \ | ||
| -l opencv_highgui \ | ||
| -l opencv_gapi \ | ||
| -l opencv_imgcodecs \ | ||
| -l opencv_imgproc \ | ||
| -l opencv_ml \ | ||
| -l opencv_objdetect \ | ||
| -l opencv_photo \ | ||
| -l opencv_stitching \ | ||
| -l opencv_video \ | ||
| -l opencv_ts \ | ||
| -l opencv_videoio \ | ||
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| #Run the project | ||
| ./yolo_ort -i $DATA_DIR/images -o $SUBMISSION_PATH |
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| MIT License | ||
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| Copyright (c) Microsoft Corporation | ||
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| Permission is hereby granted, free of charge, to any person obtaining a copy | ||
| of this software and associated documentation files (the "Software"), to deal | ||
| in the Software without restriction, including without limitation the rights | ||
| to use, copy, modify, merge, publish, distribute, sublicense, and/or sell | ||
| copies of the Software, and to permit persons to whom the Software is | ||
| furnished to do so, subject to the following conditions: | ||
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| The above copyright notice and this permission notice shall be included in all | ||
| copies or substantial portions of the Software. | ||
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| THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR | ||
| IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, | ||
| FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE | ||
| AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER | ||
| LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, | ||
| OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE | ||
| SOFTWARE. |
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| # Privacy | ||
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| ## Data Collection | ||
| The software may collect information about you and your use of the software and send it to Microsoft. Microsoft may use this information to provide services and improve our products and services. You may turn off the telemetry as described in the repository. There are also some features in the software that may enable you and Microsoft to collect data from users of your applications. If you use these features, you must comply with applicable law, including providing appropriate notices to users of your applications together with a copy of Microsoft's privacy statement. Our privacy statement is located at https://go.microsoft.com/fwlink/?LinkID=824704. You can learn more about data collection and use in the help documentation and our privacy statement. Your use of the software operates as your consent to these practices. | ||
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| *** | ||
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| ### Private Builds | ||
| No data collection is performed when using your private builds built from source code. | ||
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| ### Official Builds | ||
| ONNX Runtime does not maintain any independent telemetry collection mechanisms outside of what is provided by the platforms it supports. However, where applicable, ONNX Runtime will take advantage of platform-supported telemetry systems to collect trace events with the goal of improving product quality. | ||
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| Currently telemetry is only implemented for Windows builds and is turned **ON** by default in the official builds distributed in their respective package management repositories ([see here](../README.md#binaries)). This may be expanded to cover other platforms in the future. Data collection is implemented via 'Platform Telemetry' per vendor platform providers (see [telemetry.h](../onnxruntime/core/platform/telemetry.h)). | ||
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| #### Technical Details | ||
| The Windows provider uses the [TraceLogging](https://docs.microsoft.com/en-us/windows/win32/tracelogging/trace-logging-about) API for its implementation. This enables ONNX Runtime trace events to be collected by the operating system, and based on user consent, this data may be periodically sent to Microsoft servers following GDPR and privacy regulations for anonymity and data access controls. | ||
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| Windows ML and onnxruntime C APIs allow Trace Logging to be turned on/off (see [API pages](../README.md#api-documentation) for details). | ||
| For information on how to enable and disable telemetry, see [C API: Telemetry](./C_API.md#telemetry). | ||
| There are equivalent APIs in the C#, Python, and Java language bindings as well. |
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| <p align="center"><img width="50%" src="docs/images/ONNX_Runtime_logo_dark.png" /></p> | ||
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| **ONNX Runtime is a cross-platform inference and training machine-learning accelerator**. | ||
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| **ONNX Runtime inference** can enable faster customer experiences and lower costs, supporting models from deep learning frameworks such as PyTorch and TensorFlow/Keras as well as classical machine learning libraries such as scikit-learn, LightGBM, XGBoost, etc. ONNX Runtime is compatible with different hardware, drivers, and operating systems, and provides optimal performance by leveraging hardware accelerators where applicable alongside graph optimizations and transforms. [Learn more →](https://www.onnxruntime.ai/docs/#onnx-runtime-for-inferencing) | ||
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| **ONNX Runtime training** can accelerate the model training time on multi-node NVIDIA GPUs for transformer models with a one-line addition for existing PyTorch training scripts. [Learn more →](https://www.onnxruntime.ai/docs/#onnx-runtime-for-training) | ||
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| ## Get Started | ||
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| **General Information**: [onnxruntime.ai](https://onnxruntime.ai) | ||
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| **Usage documention and tutorials**: [onnxruntime.ai/docs](https://onnxruntime.ai/docs) | ||
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| **Companion sample repositories**: | ||
| - ONNX Runtime Inferencing: [microsoft/onnxruntime-inference-examples](https://github.com/microsoft/onnxruntime-inference-examples) | ||
| - ONNX Runtime Training: [microsoft/onnxruntime-training-examples](https://github.com/microsoft/onnxruntime-training-examples) | ||
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| ## Build Pipeline Status | ||
| |System|CPU|GPU|EPs| | ||
| |---|---|---|---| | ||
| |Windows|[](https://dev.azure.com/onnxruntime/onnxruntime/_build/latest?definitionId=9)|[](https://dev.azure.com/onnxruntime/onnxruntime/_build/latest?definitionId=10)|[](https://dev.azure.com/onnxruntime/onnxruntime/_build/latest?definitionId=47)| | ||
| |Linux|[](https://dev.azure.com/onnxruntime/onnxruntime/_build/latest?definitionId=11)<br>[](https://dev.azure.com/onnxruntime/onnxruntime/_build/latest?definitionId=64)<br>[](https://dev.azure.com/onnxruntime/onnxruntime/_build/latest?definitionId=110)<br>[](https://dev.azure.com/onnxruntime/onnxruntime/_build/latest?definitionId=78)<br>[](https://dev.azure.com/onnxruntime/onnxruntime/_build/latest?definitionId=86)|[](https://dev.azure.com/onnxruntime/onnxruntime/_build/latest?definitionId=12)<br>[](https://dev.azure.com/onnxruntime/onnxruntime/_build/latest?definitionId=45)<br>[](https://dev.azure.com/onnxruntime/onnxruntime/_build/latest?definitionId=140)<br>[](https://dev.azure.com/onnxruntime/onnxruntime/_build/latest?definitionId=84)|[](https://dev.azure.com/onnxruntime/onnxruntime/_build/latest?definitionId=110)<br>[](https://dev.azure.com/onnxruntime/onnxruntime/_build/latest?definitionId=55)| | ||
| |Mac|[](https://dev.azure.com/onnxruntime/onnxruntime/_build/latest?definitionId=13)<br>[](https://dev.azure.com/onnxruntime/onnxruntime/_build/latest?definitionId=65)||| | ||
| |Android|||[](https://dev.azure.com/onnxruntime/onnxruntime/_build/latest?definitionId=53)| | ||
| |iOS|||[](https://dev.azure.com/onnxruntime/onnxruntime/_build/latest?definitionId=134)| | ||
| |WebAssembly|||[](https://dev.azure.com/onnxruntime/onnxruntime/_build/latest?definitionId=161)| | ||
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| ## Data/Telemetry | ||
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| Windows distributions of this project may collect usage data and send it to Microsoft to help improve our products and services. See the [privacy statement](docs/Privacy.md) for more details. | ||
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| ## Contributions and Feedback | ||
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| We welcome contributions! Please see the [contribution guidelines](CONTRIBUTING.md). | ||
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| For feature requests or bug reports, please file a [GitHub Issue](https://github.com/Microsoft/onnxruntime/issues). | ||
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| For general discussion or questions, please use [GitHub Discussions](https://github.com/microsoft/onnxruntime/discussions). | ||
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| ## Code of Conduct | ||
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| This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/). | ||
| For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) | ||
| or contact [[email protected]](mailto:[email protected]) with any additional questions or comments. | ||
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| ## License | ||
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| This project is licensed under the [MIT License](LICENSE). |
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