Merge branch 'main' of hf.co:nvidia/nemoretriever-page-elements-v3 into main

README.md

@@ -23,17 +23,17 @@ tags:
 
 ### Description
 
-The **NeMo Retriever Page Elements v3** model is a specialized object detection model designed to identify and extract key elements from charts and graphs. While the underlying technology builds upon work from [Megvii Technology](https://github.com/Megvii-BaseDetection/YOLOX), we developed our own base model through complete retraining rather than using pre-trained weights. YOLOX is an anchor-free version of YOLO (You Only Look Once), this model combines a simpler architecture with enhanced performance. The model is trained to detect **tables**, **charts**, **infographics**, **titles**, **header/footers** and **texts** in documents.
+The **NeMo Retriever Page Elements v3** model is a specialized object detection model designed to identify and extract elements from document pages. While the underlying technology builds upon work from [Megvii Technology](https://github.com/Megvii-BaseDetection/YOLOX), we developed our own base model through complete retraining rather than using pre-trained weights. YOLOX is an anchor-free version of YOLO (You Only Look Once), this model combines a simpler architecture with enhanced performance. The model is trained to detect **tables**, **charts**, **infographics**, **titles**, **header/footers** and **texts** in documents.
 
 This model supersedes the [nemoretriever-page-elements](https://build.nvidia.com/nvidia/nemoretriever-page-elements-v2) model and is a part of the NVIDIA NeMo Retriever family of NIM microservices specifically for object detection and multimodal extraction of enterprise documents.
 
-This model is ready for commercial/non-commercial use.
+This model is ready for commercial/non-commercial use. 
 
-### License/Terms of use
+We are excited to announce the open sourcing of this commercial model. For users interested in deploying this model in production environments, it is also available via the model API in NVIDIA Inference Microservices (NIM) at [nemoretriever-page-elements-v2](https://build.nvidia.com/nvidia/nemoretriever-page-elements-v2).
 
-The use of this model is governed by the [NVIDIA Open Model License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/).
+### License/Terms of use
 
-**You are responsible for ensuring that your use of NVIDIA provided models complies with all applicable laws.**
+The use of this model is governed by the [NVIDIA Open Model License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/) and the use of the post-processing scripts are licensed under [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0.txt).
 
 ### Team
 
@@ -231,10 +231,6 @@ The primary evaluation set is a cut of the Azure labels and digital corpora imag
 
 NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.
 
-**For more detailed information on ethical considerations for this model**, please see the Model Card++ [Explainability](explainability.md), [Bias](bias.md), [Safety & Security](safety-security.md), and [Privacy](privacy.md) Subcards.
-
-Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).
-
 
 ## Bias
 
@@ -257,7 +253,7 @@ Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.
 | Verified to have met prescribed NVIDIA quality standards: | Yes |
 | Performance Metrics: | Mean Average Precision, detectionr recall and visual inspection |
 | Potential Known Risks: | This model may not always detect all elements in a document. |
-| Licensing & Terms of Use: | Use of this model is governed by the [NVIDIA Open Model License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/) and the [Apache 2.0 License](https://github.com/Megvii-BaseDetection/YOLOX/blob/main/LICENSE). |
+| Licensing & Terms of Use: | Use of this model is governed by the [NVIDIA Open Model License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/) and the [Apache 2.0 License](https://www.apache.org/licenses/LICENSE-2.0.txt). |
 
 ## Privacy
 
@@ -279,4 +275,4 @@ Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.
 | Model Application Field(s): | Object Detection for Retrieval, focused on Enterprise |
 | Describe the life critical impact (if present). | Not Applicable |
 | Use Case Restrictions: | Abide by [NVIDIA Open Model License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/).   |
-| Model and dataset restrictions: | The Principle of least privilege (PoLP) is applied limiting access for dataset generation and model development. Restrictions enforce dataset access during training, and dataset license constraints adhered to. |
+| Model and dataset restrictions: | The Principle of least privilege (PoLP) is applied limiting access for dataset generation and model development. Restrictions enforce dataset access during training, and dataset license constraints adhered to. |

THIRD_PARTY_NOTICES.md

@@ -0,0 +1,426 @@
+-----
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+Copyright (c) 2012-2014 DeepMind Technologies    (Koray Kavukcuoglu)
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+License Text([https://github.com/python-pillow/Pillow/blob/main/LICENSE](https://www.google.com/search?q=https://github.com/python-pillow/Pillow/blob/main/LICENSE))
+
+This notice applies to **PIL (Pillow)**.
+
+The Python Imaging Library (PIL) is
+Copyright (c) 1997-2011 by Secret Labs AB
+Copyright (c) 1995-2011 by Fredrik Lundh
+Copyright (c) 2010-2023 by Alex Clark and contributors
+
+Like PIL, Pillow is licensed under the open source HPND License:
+
+By obtaining, using, and/or copying this software and/or its
+associated documentation, you agree that you have read, understood,
+and will comply with the following terms and conditions:
+
+Permission to use, copy, modify, and distribute this software and
+its associated documentation for any purpose and without fee is
+hereby granted, provided that the above copyright notice appears in
+all copies, and that both that copyright notice and this permission
+notice appear in supporting documentation, and that the name of
+Secret Labs AB or the author not be used in advertising or publicity
+pertaining to distribution of the software without specific, written
+prior permission.
+
+SECRET LABS AB AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH REGARD
+TO THIS SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANT-
+ABILITY AND FITNESS. IN NO EVENT SHALL SECRET LABS AB OR THE AUTHOR
+BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY
+DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
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+OF THIS SOFTWARE.
+
+-----
+
+Copyright "ZFTurbo" - MIT License
+License Text([https://opensource.org/licenses/MIT](https://opensource.org/licenses/MIT))
+
+This notice applies to the vendored code from **Weighted-Boxes-Fusion** ([https://github.com/ZFTurbo/Weighted-Boxes-Fusion](https://github.com/ZFTurbo/Weighted-Boxes-Fusion)).
+
+Copyright (c) 2020 ZFTurbo
+
+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:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+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.
+
+-----
+
+Copyright "Megvii Inc." - Apache License 2.0
+License Text([https://www.apache.org/licenses/LICENSE-2.0](https://www.apache.org/licenses/LICENSE-2.0))
+
+This notice applies to the vendored code from **YOLOX** ([https://github.com/Megvii-BaseDetection/YOLOX](https://github.com/Megvii-BaseDetection/YOLOX)).
+
+Copyright (c) 2021-2022 Megvii Inc.
+
+```
+                             Apache License
+                       Version 2.0, January 2004
+                    [http://www.apache.org/licenses/](http://www.apache.org/licenses/)
+```
+
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model.py

@@ -1,3 +1,5 @@
+# SPDX-FileCopyrightText: Copyright (c) 2024, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
 import os
 import sys
 import torch

page_element_v3.py

@@ -1,3 +1,5 @@
+# SPDX-FileCopyrightText: Copyright (c) 2024, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
 import torch
 import torch.nn as nn
 from typing import Dict, List, Tuple

post_processing/page_elt_pp.py

@@ -1,3 +1,5 @@
+# SPDX-FileCopyrightText: Copyright (c) 2024, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
 import numpy as np
 import numpy.typing as npt
 from typing import List, Tuple, Optional

post_processing/text_pp.py

@@ -1,3 +1,5 @@
+# SPDX-FileCopyrightText: Copyright (c) 2024, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
 import numpy as np
 import numpy.typing as npt
 from typing import Dict, List, Tuple, Literal

post_processing/wbf.py

@@ -1,3 +1,6 @@
+# SPDX-FileCopyrightText: Copyright (c) 2024, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
 # Adapted from:
 # https://github.com/ZFTurbo/Weighted-Boxes-Fusion/blob/master/ensemble_boxes/ensemble_boxes_wbf.py
 

utils.py

@@ -1,3 +1,5 @@
+# SPDX-FileCopyrightText: Copyright (c) 2024, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
 import numpy as np
 import numpy.typing as npt
 import matplotlib.pyplot as plt

yolox/__init__.py

@@ -1,5 +1,9 @@
 #!/usr/bin/env python3
 # -*- coding:utf-8 -*-
+
+# SPDX-FileCopyrightText: Copyright (c) 2024, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
 # Copyright (c) Megvii Inc. All rights reserved.
 
 from .yolo_head import YOLOXHead

yolox/boxes.py

@@ -1,4 +1,8 @@
 #!/usr/bin/env python3
+
+# SPDX-FileCopyrightText: Copyright (c) 2024, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
 # Copyright (c) Megvii Inc. All rights reserved.
 
 import torch

yolox/darknet.py

@@ -1,5 +1,9 @@
 #!/usr/bin/env python
 # -*- encoding: utf-8 -*-
+
+# SPDX-FileCopyrightText: Copyright (c) 2024, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
 # Copyright (c) Megvii Inc. All rights reserved.
 
 from torch import nn

yolox/network_blocks.py

@@ -1,5 +1,9 @@
 #!/usr/bin/env python
 # -*- encoding: utf-8 -*-
+
+# SPDX-FileCopyrightText: Copyright (c) 2024, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
 # Copyright (c) Megvii Inc. All rights reserved.
 
 import torch

yolox/yolo_fpn.py

@@ -1,5 +1,9 @@
 #!/usr/bin/env python
 # -*- encoding: utf-8 -*-
+
+# SPDX-FileCopyrightText: Copyright (c) 2024, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
 # Copyright (c) Megvii Inc. All rights reserved.
 
 import torch

yolox/yolo_head.py

@@ -1,5 +1,9 @@
 #!/usr/bin/env python3
 # -*- coding:utf-8 -*-
+
+# SPDX-FileCopyrightText: Copyright (c) 2024, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
 # Copyright (c) Megvii Inc. All rights reserved.
 
 import torch

yolox/yolo_pafpn.py

@@ -1,5 +1,9 @@
 #!/usr/bin/env python
 # -*- encoding: utf-8 -*-
+
+# SPDX-FileCopyrightText: Copyright (c) 2024, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
 # Copyright (c) Megvii Inc. All rights reserved.
 
 import torch

yolox/yolox.py

@@ -1,5 +1,9 @@
 #!/usr/bin/env python
 # -*- encoding: utf-8 -*-
+
+# SPDX-FileCopyrightText: Copyright (c) 2024, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
 # Copyright (c) Megvii Inc. All rights reserved.
 
 import torch.nn as nn