| Name | angelcv JSON |
| Version |
0.2.0
JSON |
| download |
| home_page | None |
| Summary | Train and inference for Computer Vision models made easy. |
| upload_time | 2025-08-29 11:22:45 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | <3.13,>=3.9 |
| license | Copyright 2025- Angel Protection Systems Inc. Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. "License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. "Licensor" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License. "Legal Entity" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. For the purposes of this definition, "control" means (i) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (ii) ownership of fifty percent (50%) or more of the outstanding shares, or (iii) beneficial ownership of such entity. "You" (or "Your") shall mean an individual or Legal Entity exercising permissions granted by this License. "Source" form shall mean the preferred form for making modifications, including but not limited to software source code, documentation source, and configuration files. "Object" form shall mean any form resulting from mechanical transformation or translation of a Source form, including but not limited to compiled object code, generated documentation, and conversions to other media types. "Work" shall mean the work of authorship, whether in Source or Object form, made available under the License, as indicated by a copyright notice that is included in or attached to the work (an example is provided in the Appendix below). "Derivative Works" shall mean any work, whether in Source or Object form, that is based on (or derived from) the Work and for which the editorial revisions, annotations, elaborations, or other modifications represent, as a whole, an original work of authorship. For the purposes of this License, Derivative Works shall not include works that remain separable from, or merely link (or bind by name) to the interfaces of, the Work and Derivative Works thereof. "Contribution" shall mean any work of authorship, including the original version of the Work and any modifications or additions to that Work or Derivative Works thereof, that is intentionally submitted to Licensor for inclusion in the Work by the copyright owner or by an individual or Legal Entity authorized to submit on behalf of the copyright owner. For the purposes of this definition, "submitted" means any form of electronic, verbal, or written communication sent to the Licensor or its representatives, including but not limited to communication on electronic mailing lists, source code control systems, and issue tracking systems that are managed by, or on behalf of, the Licensor for the purpose of discussing and improving the Work, but excluding communication that is conspicuously marked or otherwise designated in writing by the copyright owner as "Not a Contribution." "Contributor" shall mean Licensor and any individual or Legal Entity on behalf of whom a Contribution has been received by Licensor and subsequently incorporated within the Work. 2. Grant of Copyright License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license to reproduce, prepare Derivative Works of, publicly display, publicly perform, sublicense, and distribute the Work and such Derivative Works in Source or Object form. 3. Grant of Patent License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable (except as stated in this section) patent license to make, have made, use, offer to sell, sell, import, and otherwise transfer the Work, where such license applies only to those patent claims licensable by such Contributor that are necessarily infringed by their Contribution(s) alone or by combination of their Contribution(s) with the Work to which such Contribution(s) was submitted. If You institute patent litigation against any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Work or a Contribution incorporated within the Work constitutes direct or contributory patent infringement, then any patent licenses granted to You under this License for that Work shall terminate as of the date such litigation is filed. 4. Redistribution. You may reproduce and distribute copies of the Work or Derivative Works thereof in any medium, with or without modifications, and in Source or Object form, provided that You meet the following conditions: (a) You must give any other recipients of the Work or Derivative Works a copy of this License; and (b) You must cause any modified files to carry prominent notices stating that You changed the files; and (c) You must retain, in the Source form of any Derivative Works that You distribute, all copyright, patent, trademark, and attribution notices from the Source form of the Work, excluding those notices that do not pertain to any part of the Derivative Works; and (d) If the Work includes a "NOTICE" text file as part of its distribution, then any Derivative Works that You distribute must include a readable copy of the attribution notices contained within such NOTICE file, excluding those notices that do not pertain to any part of the Derivative Works, in at least one of the following places: within a NOTICE text file distributed as part of the Derivative Works; within the Source form or documentation, if provided along with the Derivative Works; or, within a display generated by the Derivative Works, if and wherever such third-party notices normally appear. The contents of the NOTICE file are for informational purposes only and do not modify the License. You may add Your own attribution notices within Derivative Works that You distribute, alongside or as an addendum to the NOTICE text from the Work, provided that such additional attribution notices cannot be construed as modifying the License. You may add Your own copyright statement to Your modifications and may provide additional or different license terms and conditions for use, reproduction, or distribution of Your modifications, or for any such Derivative Works as a whole, provided Your use, reproduction, and distribution of the Work otherwise complies with the conditions stated in this License. 5. Submission of Contributions. Unless You explicitly state otherwise, any Contribution intentionally submitted for inclusion in the Work by You to the Licensor shall be under the terms and conditions of this License, without any additional terms or conditions. Notwithstanding the above, nothing herein shall supersede or modify the terms of any separate license agreement you may have executed with Licensor regarding such Contributions. 6. Trademarks. This License does not grant permission to use the trade names, trademarks, service marks, or product names of the Licensor, except as required for reasonable and customary use in describing the origin of the Work and reproducing the content of the NOTICE file. 7. Disclaimer of Warranty. Unless required by applicable law or agreed to in writing, Licensor provides the Work (and each Contributor provides its Contributions) on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied, including, without limitation, any warranties or conditions of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A PARTICULAR PURPOSE. You are solely responsible for determining the appropriateness of using or redistributing the Work and assume any risks associated with Your exercise of permissions under this License. 8. Limitation of Liability. In no event and under no legal theory, whether in tort (including negligence), contract, or otherwise, unless required by applicable law (such as deliberate and grossly negligent acts) or agreed to in writing, shall any Contributor be liable to You for damages, including any direct, indirect, special, incidental, or consequential damages of any character arising as a result of this License or out of the use or inability to use the Work (including but not limited to damages for loss of goodwill, work stoppage, computer failure or malfunction, or any and all other commercial damages or losses), even if such Contributor has been advised of the possibility of such damages. 9. Accepting Warranty or Additional Liability. While redistributing the Work or Derivative Works thereof, You may choose to offer, and charge a fee for, acceptance of support, warranty, indemnity, or other liability obligations and/or rights consistent with this License. However, in accepting such obligations, You may act only on Your own behalf and on Your sole responsibility, not on behalf of any other Contributor, and only if You agree to indemnify, defend, and hold each Contributor harmless for any liability incurred by, or claims asserted against, such Contributor by reason of your accepting any such warranty or additional liability. END OF TERMS AND CONDITIONS APPENDIX: How to apply the Apache License to your work. To apply the Apache License to your work, attach the following boilerplate notice, with the fields enclosed by brackets "[]" replaced with your own identifying information. (Don't include the brackets!) The text should be enclosed in the appropriate comment syntax for the file format. We also recommend that a file or class name and description of purpose be included on the same "printed page" as the copyright notice for easier identification within third-party archives. Copyright [yyyy] [name of copyright owner] Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. -------------------------------------------------------------------------------- Code in angelcv/tool/tal.py is adapted from: - https://github.com/fcjian/TOOD/blob/master/mmdet/core/bbox/assigners/task_aligned_assigner.py - https://github.com/Nioolek/PPYOLOE_pytorch/blob/master/ppyoloe/assigner/tal_assigner.py Copyright (c) 2021 fcjian (TOOD) Copyright (c) 2022 Nioolek (PPYOLOE_pytorch) Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at https://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. |
| keywords |
computer-vision
deep-learning
machine-learning
ai
ml
dl
yolo
yolov10
angelcv
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# AngelCV
**AngelCV is an open-source, commercially-friendly computer vision library designed for ease of use, power, and extensibility.**
AngelCV is a project by [**Angel Protection System**](https://angelprotection.com/), a company at the forefront of safeguarding schools, hospitals, and other vital community spaces. They specialize in intelligent security and surveillance systems, including cutting-edge firearm detection technology that provides critical, real-time information to 911 and first responders, playing a vital role in saving lives.
Our mission is to provide cutting-edge deep learning models and tools that you can seamlessly integrate into your projects, whether for research, personal use, or commercial applications. All our code and pre-trained models are under the **Apache 2.0 License**, giving you the freedom to innovate without restrictive licensing.
_A note on our open-source commitment: Angel Protection System initially developed AngelCV to enhance its advanced computer vision capabilities for security applications. We are excited to share it with the open-source community to foster innovation and allow everyone to benefit from and contribute to its development._
## ✨ Why AngelCV?
- **Open & Free for Commercial Use**: Build your next big thing without worrying about licensing fees or restrictions. Our Apache 2.0 license covers both the library and our provided pre-trained models.
- **State-of-the-Art Models**: We start with robust implementations like YOLOv10 for object detection and plan to expand to other vision tasks (classification, segmentation, oriented bounding boxes) and model architectures.
- **Developer-Friendly Interface**: A clean, intuitive API (see `ObjectDetectionModel` and `InferenceResult`) makes common tasks like training, inference, and evaluation straightforward.
- **Flexible Configuration**: Easily customize model architectures, training parameters, and datasets using YAML-based configuration files.
- **Community Driven (Future)**: We aim to build a community around AngelCV.
## 🚀 Getting Started
### Installation
AngelCV will be available on PyPI. You can install it using pip:
```bash
pip install angelcv
```
Make sure you have PyTorch installed, as it's a primary dependency. You can find PyTorch installation instructions at [pytorch.org](https://pytorch.org/).
### Quick Start: Object Detection
Here's a simple example of how to load a pre-trained YOLOv10 model and perform inference on an image:
```python
from angelcv import ObjectDetectionModel
# Load a pre-trained YOLOv10n model (will download if not found locally)
# You can also specify a path to a local .ckpt or .pt file,
# or a .yaml configuration file to initialize a new model.
model = ObjectDetectionModel("yolov10n.ckpt")
# Perform inference on an image
# Source can be a file path, URL, PIL image, torch.Tensor, or numpy array.
results = model.predict("path/to/your/image.jpg")
# Process and display results
for result in results:
print(f"Found {len(result.boxes.xyxy)} objects.")
# Access bounding boxes (various formats available, e.g., result.boxes.xyxy_norm)
# Access confidences: result.boxes.confidences
# Access class IDs: result.boxes.class_label_ids
# Access class labels (if available): result.boxes.labels
# Show the annotated image
result.show()
# Save the annotated image
result.save("output_image.jpg")
```
## 🚧 Development Status
> **⚠️ Repository Under Heavy Development**
>
> AngelCV is actively being developed. While core functionality is stable, we're continuously improving and expanding features.
### ✅ **Stable & Ready to Use**
- **Object Detection**: Training, validation, testing, and inference are fully stable
- **YOLOv10 Integration**: Robust implementation with pre-trained models
- **Core API**: `ObjectDetectionModel` and `InferenceResult` interfaces
- **Configuration System**: YAML-based model and training configuration
- **Model Export**: ONNX, TensorRT, and other deployment formats
### 🔄 **Worning On**
- **Data Augmentation**: Expanding augmentation techniques to improve training performance on large datasets
- **Performance Optimization**: Addressing slightly below-expected performance on big datasets
- **Documentation**: Comprehensive guides and examples
### 📋 **Coming Soon (TODO)**
- **Image Segmentation**: Semantic and instance segmentation models
- **Oriented Bounding Boxes**: Support for rotated object detection
- **Classification Models**: Standalone image classification capabilities
- **Additional Architectures**: Beyond YOLOv10 (YOLOv9, DETR, etc.)
- **Advanced Metrics**: Comprehensive evaluation and benchmarking tools
## 📚 Dive Deeper
For more detailed information, check out our documentation:
- **[Getting Started](https://angelprotection.github.io/angelcv/getting_started/)**: Your first stop for installation and a quick tour.
- **[Object Detection](https://angelprotection.github.io/angelcv/object_detection/)**: Learn about our object detection capabilities, focusing on YOLOv10.
- **[Configuration](https://angelprotection.github.io/angelcv/configuration/)**: Understand how to use and customize model, training, and dataset configurations.
- **[API Interfaces](https://angelprotection.github.io/angelcv/interfaces/)**: Explore the main Python classes you'll interact with.
## 🤝 Contributing
Interested in contributing? We welcome contributions of all kinds, from bug fixes to new features. (TODO: Link to contribution guidelines when ready).
## 🛠️ Development and Support
The primary developer and maintainer of AngelCV is [Iu Ayala](https://github.com/IuAyala) from **Gradient Insight**. Gradient Insight partners with businesses to design and build custom AI-powered computer vision systems, turning complex visual data into actionable insights. You can learn more about their work at [gradientinsight.com](https://gradientinsight.com).
## 📄 License
AngelCV is licensed under the **Apache 2.0 License**. See the `LICENSE` file for more details.
Raw data
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"maintainer_email": "Iu Ayala <iu.ayala@gradientinsight.com>",
"keywords": "computer-vision, deep-learning, machine-learning, AI, ML, DL, YOLO, YOLOv10, AngelCV",
"author": null,
"author_email": "Iu Ayala <iu.ayala@gradientinsight.com>",
"download_url": "https://files.pythonhosted.org/packages/32/10/3910573f2a08dca834d26c69842ec1f03ca785e74b9fca2253245fa7f923/angelcv-0.2.0.tar.gz",
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"description": "# AngelCV\n\n**AngelCV is an open-source, commercially-friendly computer vision library designed for ease of use, power, and extensibility.**\n\nAngelCV is a project by [**Angel Protection System**](https://angelprotection.com/), a company at the forefront of safeguarding schools, hospitals, and other vital community spaces. They specialize in intelligent security and surveillance systems, including cutting-edge firearm detection technology that provides critical, real-time information to 911 and first responders, playing a vital role in saving lives.\n\nOur mission is to provide cutting-edge deep learning models and tools that you can seamlessly integrate into your projects, whether for research, personal use, or commercial applications. All our code and pre-trained models are under the **Apache 2.0 License**, giving you the freedom to innovate without restrictive licensing.\n\n_A note on our open-source commitment: Angel Protection System initially developed AngelCV to enhance its advanced computer vision capabilities for security applications. We are excited to share it with the open-source community to foster innovation and allow everyone to benefit from and contribute to its development._\n\n## \u2728 Why AngelCV?\n\n- **Open & Free for Commercial Use**: Build your next big thing without worrying about licensing fees or restrictions. Our Apache 2.0 license covers both the library and our provided pre-trained models.\n- **State-of-the-Art Models**: We start with robust implementations like YOLOv10 for object detection and plan to expand to other vision tasks (classification, segmentation, oriented bounding boxes) and model architectures.\n- **Developer-Friendly Interface**: A clean, intuitive API (see `ObjectDetectionModel` and `InferenceResult`) makes common tasks like training, inference, and evaluation straightforward.\n- **Flexible Configuration**: Easily customize model architectures, training parameters, and datasets using YAML-based configuration files.\n- **Community Driven (Future)**: We aim to build a community around AngelCV.\n\n## \ud83d\ude80 Getting Started\n\n### Installation\n\nAngelCV will be available on PyPI. You can install it using pip:\n\n```bash\npip install angelcv\n```\n\nMake sure you have PyTorch installed, as it's a primary dependency. You can find PyTorch installation instructions at [pytorch.org](https://pytorch.org/).\n\n### Quick Start: Object Detection\n\nHere's a simple example of how to load a pre-trained YOLOv10 model and perform inference on an image:\n\n```python\nfrom angelcv import ObjectDetectionModel\n\n# Load a pre-trained YOLOv10n model (will download if not found locally)\n# You can also specify a path to a local .ckpt or .pt file,\n# or a .yaml configuration file to initialize a new model.\nmodel = ObjectDetectionModel(\"yolov10n.ckpt\")\n\n# Perform inference on an image\n# Source can be a file path, URL, PIL image, torch.Tensor, or numpy array.\nresults = model.predict(\"path/to/your/image.jpg\")\n\n# Process and display results\nfor result in results:\n print(f\"Found {len(result.boxes.xyxy)} objects.\")\n # Access bounding boxes (various formats available, e.g., result.boxes.xyxy_norm)\n # Access confidences: result.boxes.confidences\n # Access class IDs: result.boxes.class_label_ids\n # Access class labels (if available): result.boxes.labels\n\n # Show the annotated image\n result.show()\n\n # Save the annotated image\n result.save(\"output_image.jpg\")\n```\n\n## \ud83d\udea7 Development Status\n\n> **\u26a0\ufe0f Repository Under Heavy Development**\n>\n> AngelCV is actively being developed. While core functionality is stable, we're continuously improving and expanding features.\n\n### \u2705 **Stable & Ready to Use**\n\n- **Object Detection**: Training, validation, testing, and inference are fully stable\n- **YOLOv10 Integration**: Robust implementation with pre-trained models\n- **Core API**: `ObjectDetectionModel` and `InferenceResult` interfaces\n- **Configuration System**: YAML-based model and training configuration\n- **Model Export**: ONNX, TensorRT, and other deployment formats\n\n### \ud83d\udd04 **Worning On**\n\n- **Data Augmentation**: Expanding augmentation techniques to improve training performance on large datasets\n- **Performance Optimization**: Addressing slightly below-expected performance on big datasets\n- **Documentation**: Comprehensive guides and examples\n\n### \ud83d\udccb **Coming Soon (TODO)**\n\n- **Image Segmentation**: Semantic and instance segmentation models\n- **Oriented Bounding Boxes**: Support for rotated object detection\n- **Classification Models**: Standalone image classification capabilities\n- **Additional Architectures**: Beyond YOLOv10 (YOLOv9, DETR, etc.)\n- **Advanced Metrics**: Comprehensive evaluation and benchmarking tools\n\n## \ud83d\udcda Dive Deeper\n\nFor more detailed information, check out our documentation:\n\n- **[Getting Started](https://angelprotection.github.io/angelcv/getting_started/)**: Your first stop for installation and a quick tour.\n- **[Object Detection](https://angelprotection.github.io/angelcv/object_detection/)**: Learn about our object detection capabilities, focusing on YOLOv10.\n- **[Configuration](https://angelprotection.github.io/angelcv/configuration/)**: Understand how to use and customize model, training, and dataset configurations.\n- **[API Interfaces](https://angelprotection.github.io/angelcv/interfaces/)**: Explore the main Python classes you'll interact with.\n\n## \ud83e\udd1d Contributing\n\nInterested in contributing? We welcome contributions of all kinds, from bug fixes to new features. (TODO: Link to contribution guidelines when ready).\n\n## \ud83d\udee0\ufe0f Development and Support\n\nThe primary developer and maintainer of AngelCV is [Iu Ayala](https://github.com/IuAyala) from **Gradient Insight**. Gradient Insight partners with businesses to design and build custom AI-powered computer vision systems, turning complex visual data into actionable insights. You can learn more about their work at [gradientinsight.com](https://gradientinsight.com).\n\n## \ud83d\udcc4 License\n\nAngelCV is licensed under the **Apache 2.0 License**. See the `LICENSE` file for more details.\n",
"bugtrack_url": null,
"license": "Copyright 2025- Angel Protection Systems Inc. Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. \"License\" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. \"Licensor\" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License. \"Legal Entity\" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. For the purposes of this definition, \"control\" means (i) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (ii) ownership of fifty percent (50%) or more of the outstanding shares, or (iii) beneficial ownership of such entity. \"You\" (or \"Your\") shall mean an individual or Legal Entity exercising permissions granted by this License. \"Source\" form shall mean the preferred form for making modifications, including but not limited to software source code, documentation source, and configuration files. \"Object\" form shall mean any form resulting from mechanical transformation or translation of a Source form, including but not limited to compiled object code, generated documentation, and conversions to other media types. \"Work\" shall mean the work of authorship, whether in Source or Object form, made available under the License, as indicated by a copyright notice that is included in or attached to the work (an example is provided in the Appendix below). \"Derivative Works\" shall mean any work, whether in Source or Object form, that is based on (or derived from) the Work and for which the editorial revisions, annotations, elaborations, or other modifications represent, as a whole, an original work of authorship. For the purposes of this License, Derivative Works shall not include works that remain separable from, or merely link (or bind by name) to the interfaces of, the Work and Derivative Works thereof. \"Contribution\" shall mean any work of authorship, including the original version of the Work and any modifications or additions to that Work or Derivative Works thereof, that is intentionally submitted to Licensor for inclusion in the Work by the copyright owner or by an individual or Legal Entity authorized to submit on behalf of the copyright owner. For the purposes of this definition, \"submitted\" means any form of electronic, verbal, or written communication sent to the Licensor or its representatives, including but not limited to communication on electronic mailing lists, source code control systems, and issue tracking systems that are managed by, or on behalf of, the Licensor for the purpose of discussing and improving the Work, but excluding communication that is conspicuously marked or otherwise designated in writing by the copyright owner as \"Not a Contribution.\" \"Contributor\" shall mean Licensor and any individual or Legal Entity on behalf of whom a Contribution has been received by Licensor and subsequently incorporated within the Work. 2. Grant of Copyright License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license to reproduce, prepare Derivative Works of, publicly display, publicly perform, sublicense, and distribute the Work and such Derivative Works in Source or Object form. 3. Grant of Patent License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable (except as stated in this section) patent license to make, have made, use, offer to sell, sell, import, and otherwise transfer the Work, where such license applies only to those patent claims licensable by such Contributor that are necessarily infringed by their Contribution(s) alone or by combination of their Contribution(s) with the Work to which such Contribution(s) was submitted. If You institute patent litigation against any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Work or a Contribution incorporated within the Work constitutes direct or contributory patent infringement, then any patent licenses granted to You under this License for that Work shall terminate as of the date such litigation is filed. 4. Redistribution. You may reproduce and distribute copies of the Work or Derivative Works thereof in any medium, with or without modifications, and in Source or Object form, provided that You meet the following conditions: (a) You must give any other recipients of the Work or Derivative Works a copy of this License; and (b) You must cause any modified files to carry prominent notices stating that You changed the files; and (c) You must retain, in the Source form of any Derivative Works that You distribute, all copyright, patent, trademark, and attribution notices from the Source form of the Work, excluding those notices that do not pertain to any part of the Derivative Works; and (d) If the Work includes a \"NOTICE\" text file as part of its distribution, then any Derivative Works that You distribute must include a readable copy of the attribution notices contained within such NOTICE file, excluding those notices that do not pertain to any part of the Derivative Works, in at least one of the following places: within a NOTICE text file distributed as part of the Derivative Works; within the Source form or documentation, if provided along with the Derivative Works; or, within a display generated by the Derivative Works, if and wherever such third-party notices normally appear. The contents of the NOTICE file are for informational purposes only and do not modify the License. You may add Your own attribution notices within Derivative Works that You distribute, alongside or as an addendum to the NOTICE text from the Work, provided that such additional attribution notices cannot be construed as modifying the License. You may add Your own copyright statement to Your modifications and may provide additional or different license terms and conditions for use, reproduction, or distribution of Your modifications, or for any such Derivative Works as a whole, provided Your use, reproduction, and distribution of the Work otherwise complies with the conditions stated in this License. 5. Submission of Contributions. Unless You explicitly state otherwise, any Contribution intentionally submitted for inclusion in the Work by You to the Licensor shall be under the terms and conditions of this License, without any additional terms or conditions. Notwithstanding the above, nothing herein shall supersede or modify the terms of any separate license agreement you may have executed with Licensor regarding such Contributions. 6. Trademarks. This License does not grant permission to use the trade names, trademarks, service marks, or product names of the Licensor, except as required for reasonable and customary use in describing the origin of the Work and reproducing the content of the NOTICE file. 7. Disclaimer of Warranty. Unless required by applicable law or agreed to in writing, Licensor provides the Work (and each Contributor provides its Contributions) on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied, including, without limitation, any warranties or conditions of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A PARTICULAR PURPOSE. You are solely responsible for determining the appropriateness of using or redistributing the Work and assume any risks associated with Your exercise of permissions under this License. 8. Limitation of Liability. In no event and under no legal theory, whether in tort (including negligence), contract, or otherwise, unless required by applicable law (such as deliberate and grossly negligent acts) or agreed to in writing, shall any Contributor be liable to You for damages, including any direct, indirect, special, incidental, or consequential damages of any character arising as a result of this License or out of the use or inability to use the Work (including but not limited to damages for loss of goodwill, work stoppage, computer failure or malfunction, or any and all other commercial damages or losses), even if such Contributor has been advised of the possibility of such damages. 9. Accepting Warranty or Additional Liability. 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