Visual ChangeNet-Segmentation Model Card (Commercial)

Model Overview

The model described in this model card detects land cover semantic changes given remote sensing imagery (RSI). The inputs are a \"golden or reference\" image and the test image of the same land cover area under observation (captured between 1990 and 2010) and the output is a semantic change map denoting the semantic change between the two images.

Model Architecture

Visual ChangeNet is a state of the art transformer-based Change Detection model. Visual ChangeNet is based on Siamese Network, which is a class of neural network architectures containing two or more identical subnetworks. The training algorithm works by updating the parameters across all the sub-networks in tandem. In TAO, Visual ChangeNet supports two images as input where the end goal is to either classify or segment the change between the \"golden or reference\" image and the \"test\" image. TAO supports the FAN backbone network for both Visual ChangeNet architectures. For more details about training FAN backbones, see the Pre-trained FAN based ImageNet Classification. In TAO, two different types of Change Detection networks are supported:

Visual ChangeNet-Segmentation - for segmentation of change between two input images.
Visual ChangeNet-Classification - for classification of change between two input images.

Visual ChangeNet-Segmentation is specifically intended for change segmentation. In this model card, the Visual ChangeNet-Segmentation model is leveraged to demonstrate land cover semantic change detection using LandSat-SCD dataset. The model uses a pretrained FAN backbone, trained on NVImageNet dataset, and then fine-tunes on the LandSat-SCD dataset.

Training

This model was trained using the visual_changenet entrypoint in TAO. The training algorithm optimizes the network to minimize the cross-entropy loss for every pixel of the mask.

Training Data

Visual ChangeNet-Segmentation model was trained on open-source remote sensing semantic land change detection dataset called LandSat-SCD. The training dataset consists of 8468 images.\nLandSat-SCD is a land cover CD dataset that contains RS image pairs of resolution 416 × 416. They are randomly split into three parts to make train, val, and test sets\nof samples 6053, 1729 and 686 respectively.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

Dataset	No. of images
LandSat-SCD	8468

Following is a sample image showing the pre and post change images along with the ground truth segmentation change maps.

\n $\"Land$ \n

Performance

Evaluation Data

The model performance was evaluated on a validation dataset that had a total of 686 images.

Methodology and KPI

The performance of the Visual ChangeNet-Segmentation model for multi-class semactic change detection is measured using overall accuracy and average precision, recall and IoU score for all the classes.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

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Model	Model Architecture	Testing Images	Precision	Recall	IoU	F1	Overall Accuracy
Visual ChangeNet-Segmentation	Siamese Network	686	88.64	85.9	77.88	87.15	95.77

Real-time Inference Performance

The inference is run on the provided unpruned model at FP16 precision. The inference performance is run using trtexec on Jetson AGX Xavier, Xavier NX, Orin, Orin NX and NVIDIA T4, and Ampere GPUs. The Jetson devices are running at Max-N configuration for maximum GPU frequency. The performance shown here is the inference only performance. The end-to-end performance with streaming video data might vary depending on other bottlenecks in the hardware and software.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

Platform	BS	FPS
Orin Nano 8GB	16	4.91
Orin NX 16GB	16	7.11
AGX Orin 64GB	16	18.25
A2	16	13.28
T4	8	23.25
L4	1	50.02
A30	16	76.54
L40	1	135.4
A100	16	159.92
H100	16	316.92

How to Use this Model

These models need to be used with NVIDIA hardware and software. For hardware, the models can run on any NVIDIA GPU including NVIDIA Jetson devices. These models can only be used with Train Adapt Optimize (TAO) Toolkit, or TensorRT.

The primary use case intended for these models is for Visual ChangeNet-Segmentation using RGB images. The model is a Siamese Network that outputs semantic change maps denoting pixel-level change between the two images.

These models are intended for training and fine-tune using TAO Toolkit and user datasets for image comparison. High-fidelity models can be trained to the new use cases. A Jupyter notebook is available as a part of the TAO container and can be used to re-train.

The models are also intended for edge deployment using TensorRT.

Input

Two imput images:

Golden: RGB Image of dimensions: 416 X 416 X 3 (H x W x C)

Sample: RGB Image of dimensions: 416 X 416 X 3 (H x W x C)

Channel Ordering of the Input: NCHW, where N = Batch Size, C = number of channels (3), H = Height of images (416), W = Width of the images (416)

Output

Segmentation change map with the same resolution as the input images: 416 X 416 X 10 (H x W x C), where C = number of output change classes.

Input image

Here is a sample image for a pre and post change images along with ground-truth segmentation change map side-by-side.

\n $\"Land$ \n

Using the Model with TAO

To use these models as pretrained weights for transfer learning, use the snippet below as a template for the model and train component of the experiment spec file to train a Siamese Network model. For more information on the experiment spec file, see the TAO Toolkit User Guide - Visual ChangeNet-Segmentation.

model:\n  backbone:\n    type: \"fan_small_12_p4_hybrid\"\n    pretrained_backbone_path: null\nevaluate:\n    model_path: \"???\"\n

Expecting Co-registered Remote Sensing Imagery

The Visual ChangeNet-Segmentation Network model was trained on pair-wise co-registered RS imagery and might not perform well for mis-aligned image pairs not captured using RSI.

Model Versions

trainable_v1.0 - FAN-Hybrid Small Visual ChangeNet-Segmentation model LandSat-SCD trainable.
deployable_v1.0 - FAN-Hybrid Small Visual ChangeNet-Segmentation model LandSat-SCD deployable to deepstream.

References

Using TAO Pre-trained Models

Get TAO Container
Get other purpose-built models from the NGC model registry:\n
- TrafficCamNet
- PeopleNet
- PeopleNet
- PeopleNet-Transformer
- DashCamNet
- FaceDetectIR
- VehicleMakeNet
- VehicleTypeNet
- PeopleSegNet
- PeopleSemSegNet
- License Plate Detection
- License Plate Recognition
- Gaze Estimation
- Facial Landmark
- Heart Rate Estimation
- Gesture Recognition
- Emotion Recognition
- FaceDetect
- 2D Body Pose Estimation
- ActionRecognitionNet
- ActionRecognitionNet
- PoseClassificationNet
- People ReIdentification
- PointPillarNet
- CitySegFormer
- Retail Object Detection
- Retail Object Embedding
- Optical Inspection
- Optical Character Detection
- Optical Character Recognition
- PCB Classification
- PeopleSemSegFormer
- LPDNet
- License Plate Recognition
- Gaze Estimation
- Facial Landmark
- Heart Rate Estimation
- Gesture Recognition
- Emotion Recognition
- FaceDetect
- 2D Body Pose Estimation
- ActionRecognitionNet
- ActionRecognitionNet
- PoseClassificationNet
- People ReIdentification
- PointPillarNet
- CitySegFormer
- Retail Object Detection
- Retail Object Embedding
- Optical Inspection
- Optical Character Detection
- Optical Character Recognition
- PCB Classification
- PeopleSemSegFormer
\n

Technical Blogs

Read the 2 part blog on training and optimizing 2D body pose estimation model with TAO - Part 1 | Part 2
Learn how to train real-time License plate detection and recognition app with TAO and DeepStream.
Model accuracy is extremely important, learn how you can achieve state of the art accuracy for classification and object detection models using TAO
Learn how to train Instance segmentation model using MaskRCNN with TAO
Read the technical tutorial on how PeopleNet model can be trained with custom data using Transfer Learning Toolkit
Learn how to train and deploy real-time intelligent video analytics apps and services using DeepStream SDK

License

License to use this model is covered by CCBY 4.0. By downloading the unpruned or pruned version of the model, you accept the terms and conditions of these licenses.

Ethical Considerations

NVIDIA Visual ChangeNet-Segmentation model detects changes between pair-wise images. NVIDIA’s platforms and application frameworks enable developers to build a wide array of AI applications. Consider potential algorithmic bias when choosing or creating the models being deployed. Work with the model’s developer to ensure that it meets the requirements for the relevant industry and use case; that the necessary instruction and documentation are provided to understand error rates, confidence intervals, and results; and that the model is being used under the conditions and in the manner intended.

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The inputs are a \"golden or reference\" image and the test image of the same land cover area under observation (captured between 1990 and 2010) and the output is a semantic change map denoting the semantic change between the two images.\n\n## Model Architecture \n\nVisual ChangeNet is a state of the art transformer-based Change Detection model. Visual ChangeNet is based on Siamese Network, which is a class of neural network architectures containing two or more identical subnetworks. The training algorithm works by updating the parameters across all the sub-networks in tandem. In TAO, Visual ChangeNet supports two images as input where the end goal is to either classify or segment the change between the \"golden or reference\" image and the \"test\" image. TAO supports the [FAN](https://arxiv.org/abs/2204.12451) backbone network for both Visual ChangeNet architectures. For more details about training FAN backbones, see the [Pre-trained FAN based ImageNet Classification](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/pretrained_fan_classification_imagenet). In TAO, two different types of Change Detection networks are supported: \n\n* Visual ChangeNet-Segmentation - for segmentation of change between two input images.\n* Visual ChangeNet-Classification - for classification of change between two input images.\n\nVisual ChangeNet-Segmentation is specifically intended for change segmentation. In this model card, the Visual ChangeNet-Segmentation model is leveraged to demonstrate land cover semantic change detection using LandSat-SCD dataset. The model uses a pretrained FAN backbone, trained on NVImageNet dataset, and then fine-tunes on the LandSat-SCD dataset.\n\n\n## Training \n\nThis model was trained using the `visual_changenet` entrypoint in TAO. The training algorithm optimizes the network to minimize the cross-entropy loss for every pixel of the mask.\n\n### Training Data \n\nVisual ChangeNet-Segmentation model was trained on open-source remote sensing semantic land change detection dataset called LandSat-SCD. The training dataset consists of 8468 images.\nLandSat-SCD is a land cover CD dataset that contains RS image pairs of resolution 416 × 416. They are randomly split into three parts to make train, val, and test sets\nof samples 6053, 1729 and 686 respectively.\n\n| Dataset | No. of images |\n|--|--|\n|LandSat-SCD| 8468 | \n\n\nFollowing is a sample image showing the pre and post change images along with the ground truth segmentation change maps.\n\n\n $\"Land$ \n\n\n## Performance\n\n### Evaluation Data \n\nThe model performance was evaluated on a validation dataset that had a total of 686 images.\n\n#### Methodology and KPI\n\nThe performance of the Visual ChangeNet-Segmentation model for multi-class semactic change detection is measured using overall accuracy and average precision, recall and IoU score for all the classes.\n\n| Model | Model Architecture | Testing Images | Precision | Recall | IoU | F1 | Overall Accuracy |\n|--|--|--|--|--|--|--|--|\n| Visual ChangeNet-Segmentation | Siamese Network | 686 | 88.64 | 85.9 | 77.88 | 87.15 | 95.77 |\n\n\n### Real-time Inference Performance \n\nThe inference is run on the provided unpruned model at FP16 precision. The inference performance is run using [`trtexec`](https://github.com/NVIDIA/TensorRT/tree/main/samples/trtexec) on Jetson AGX Xavier, Xavier NX, Orin, Orin NX and NVIDIA T4, and Ampere GPUs. The Jetson devices are running at Max-N configuration for maximum GPU frequency. The performance shown here is the inference only performance. The end-to-end performance with streaming video data might vary depending on other bottlenecks in the hardware and software.\n\n| Platform | BS | FPS |\n|------------------|-----|--------|\n| Orin Nano 8GB | 16 | 4.91 |\n| Orin NX 16GB | 16 | 7.11 |\n| AGX Orin 64GB | 16 | 18.25 |\n| A2 | 16 | 13.28 |\n| T4 | 8 | 23.25 |\n| L4 | 1 | 50.02 |\n| A30 | 16 | 76.54 |\n| L40 | 1 | 135.4 |\n| A100 | 16 | 159.92 |\n| H100 | 16 | 316.92 |\n\n## How to Use this Model \n\nThese models need to be used with NVIDIA hardware and software. For hardware, the models can run on any NVIDIA GPU including NVIDIA Jetson devices. These models can only be used with [Train Adapt Optimize (TAO) Toolkit](https://developer.nvidia.com/tao-toolkit), or [TensorRT](https://developer.nvidia.com/tensorrt).\n\nThe primary use case intended for these models is for Visual ChangeNet-Segmentation using RGB images. The model is a Siamese Network that outputs semantic change maps denoting pixel-level change between the two images. \n\nThese models are intended for training and fine-tune using TAO Toolkit and user datasets for image comparison. High-fidelity models can be trained to the new use cases. A Jupyter notebook is available as a part of the [TAO container](https://ngc.nvidia.com/catalog/containers/nvidia:tao:tao-toolkit) and can be used to re-train.\n\nThe models are also intended for edge deployment using TensorRT. \n\n### Input\nTwo imput images:\n\nGolden: RGB Image of dimensions: 416 X 416 X 3 (H x W x C)\n\nSample: RGB Image of dimensions: 416 X 416 X 3 (H x W x C)\n\nChannel Ordering of the Input: NCHW, where N = Batch Size, C = number of channels (3), H = Height of images (416), W = Width of the images (416)\n\n### Output\n\nSegmentation change map with the same resolution as the input images: 416 X 416 X 10 (H x W x C), where C = number of output change classes. \n\n#### Input image\n\nHere is a sample image for a pre and post change images along with ground-truth segmentation change map side-by-side.\n\n $\"Land$ \n\n### Using the Model with TAO\n\nTo use these models as pretrained weights for transfer learning, use the snippet below as a template for the `model` and `train` component of the experiment spec file to train a Siamese Network model. For more information on the experiment spec file, see the [TAO Toolkit User Guide - Visual ChangeNet-Segmentation](https://gitlab-master.nvidia.com/tlt/tlt-docs/-/blob/master/docs/text/visual_changenet/visual_changenet_segment.rst).\n\n```yaml\nmodel:\n backbone:\n type: \"fan_small_12_p4_hybrid\"\n pretrained_backbone_path: null\nevaluate:\n model_path: \"???\"\n```\n\n\n### Expecting Co-registered Remote Sensing Imagery\n\nThe Visual ChangeNet-Segmentation Network model was trained on pair-wise co-registered RS imagery and might not perform well for mis-aligned image pairs not captured using RSI. \n\n## Model Versions\n\n- **trainable_v1.0** - FAN-Hybrid Small Visual ChangeNet-Segmentation model LandSat-SCD trainable.\n- **deployable_v1.0** - FAN-Hybrid Small Visual ChangeNet-Segmentation model LandSat-SCD deployable to deepstream.\n\n## References\n\n### Using TAO Pre-trained Models \n\n- Get [TAO Container](https://ngc.nvidia.com/catalog/containers/nvidia:tao:tao-toolkit)\n- Get other purpose-built models from the NGC model registry:\n - [TrafficCamNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:trafficcamnet)\n - [PeopleNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:peoplenet)\n - [PeopleNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:peoplenet)\n - [PeopleNet-Transformer](https://ngc.nvidia.com/catalog/models/nvidia:tao:peoplenet_transformer)\n - [DashCamNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:dashcamnet)\n - [FaceDetectIR](https://ngc.nvidia.com/catalog/models/nvidia:tao:facedetectir)\n - [VehicleMakeNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:vehiclemakenet)\n - [VehicleTypeNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:vehicletypenet)\n - [PeopleSegNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:peoplesegnet)\n - [PeopleSemSegNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:peoplesemsegnet)\n - [License Plate Detection](https://ngc.nvidia.com/catalog/models/nvidia:tao:lpdnet)\n - [License Plate Recognition](https://ngc.nvidia.com/catalog/models/nvidia:tao:lprnet)\n - [Gaze Estimation](https://ngc.nvidia.com/catalog/models/nvidia:tao:gazenet)\n - [Facial Landmark](https://ngc.nvidia.com/catalog/models/nvidia:tao:fpenet)\n - [Heart Rate Estimation](https://ngc.nvidia.com/catalog/models/nvidia:tao:heartratenet)\n - [Gesture Recognition](https://ngc.nvidia.com/catalog/models/nvidia:tao:gesturenet)\n - [Emotion Recognition](https://ngc.nvidia.com/catalog/models/nvidia:tao:emotionnet)\n - [FaceDetect](https://ngc.nvidia.com/catalog/models/nvidia:tao:facenet)\n - [2D Body Pose Estimation](https://ngc.nvidia.com/catalog/models/nvidia:tao:bodyposenet)\n - [ActionRecognitionNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:actionrecognitionnet)\n - [ActionRecognitionNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:actionrecognitionnet)\n - [PoseClassificationNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:poseclassificationnet)\n - [People ReIdentification](https://ngc.nvidia.com/catalog/models/nvidia:tao:reidentificationnet)\n - [PointPillarNet](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/pointpillarnet)\n - [CitySegFormer](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/citysemsegformer)\n - [Retail Object Detection](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/retail_object_detection)\n - [Retail Object Embedding](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/retail_object_recognition)\n - [Optical Inspection](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/optical_inspection)\n - [Optical Character Detection](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/ocdnet)\n - [Optical Character Recognition](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/ocrnet)\n - [PCB Classification](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/pcb_classification)\n - [PeopleSemSegFormer](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/peoplesemsegformer)\n - [LPDNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:lpdnet)\n - [License Plate Recognition](https://ngc.nvidia.com/catalog/models/nvidia:tao:lprnet)\n - [Gaze Estimation](https://ngc.nvidia.com/catalog/models/nvidia:tao:gazenet)\n - [Facial Landmark](https://ngc.nvidia.com/catalog/models/nvidia:tao:fpenet)\n - [Heart Rate Estimation](https://ngc.nvidia.com/catalog/models/nvidia:tao:heartratenet)\n - [Gesture Recognition](https://ngc.nvidia.com/catalog/models/nvidia:tao:gesturenet)\n - [Emotion Recognition](https://ngc.nvidia.com/catalog/models/nvidia:tao:emotionnet)\n - [FaceDetect](https://ngc.nvidia.com/catalog/models/nvidia:tao:facenet)\n - [2D Body Pose Estimation](https://ngc.nvidia.com/catalog/models/nvidia:tao:bodyposenet)\n - [ActionRecognitionNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:actionrecognitionnet)\n - [ActionRecognitionNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:actionrecognitionnet)\n - [PoseClassificationNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:poseclassificationnet)\n - [People ReIdentification](https://ngc.nvidia.com/catalog/models/nvidia:tao:reidentificationnet)\n - [PointPillarNet](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/pointpillarnet)\n - [CitySegFormer](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/citysemsegformer)\n - [Retail Object Detection](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/retail_object_detection)\n - [Retail Object Embedding](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/retail_object_recognition)\n - [Optical Inspection](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/optical_inspection)\n - [Optical Character Detection](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/ocdnet)\n - [Optical Character Recognition](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/ocrnet)\n - [PCB Classification](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/pcb_classification)\n - [PeopleSemSegFormer](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/peoplesemsegformer)\n### Technical Blogs \n\n- Read the 2 part blog on training and optimizing 2D body pose estimation model with TAO - [Part 1](https://developer.nvidia.com/blog/training-optimizing-2d-pose-estimation-model-with-tao-toolkit-part-1) | [Part 2](https://developer.nvidia.com/blog/training-optimizing-2d-pose-estimation-model-with-tao-toolkit-part-2)\n- Learn how to train [real-time License plate detection and recognition app](https://developer.nvidia.com/blog/creating-a-real-time-license-plate-detection-and-recognition-app) with TAO and DeepStream.\n- Model accuracy is extremely important, learn how you can achieve [state of the art accuracy for classification and object detection models](https://developer.nvidia.com/blog/preparing-state-of-the-art-models-for-classification-and-object-detection-with-tao-toolkit/) using TAO\n- Learn how to train [Instance segmentation model using MaskRCNN with TAO](https://developer.nvidia.com/blog/training-instance-segmentation-models-using-maskrcnn-on-tao-toolkit/)\n- Read the technical tutorial on how [PeopleNet model can be trained with custom data using Transfer Learning Toolkit](https://devblogs.nvidia.com/training-custom-pretrained-models-using-tlt/)\n- Learn how to [train and deploy real-time intelligent video analytics apps and services using DeepStream SDK](https://devblogs.nvidia.com/building-iva-apps-using-deepstream-5.0/)\n\n### Suggested Reading \n\n- More information on about TAO Toolkit and pre-trained models can be found at the [NVIDIA Developer Zone](https://developer.nvidia.com/tao-toolkit)\n- Read the [TAO Quick Start](https://docs.nvidia.com/tao/tao-toolkit/text/tao_toolkit_quick_start_guide.html) guide and [release notes](https://docs.nvidia.com/tao/tao-toolkit/text/release_notes.html).\n- If you have any questions or feedback, see the discussions on [TAO Toolkit Developer Forums](https://forums.developer.nvidia.com/c/accelerated-computing/intelligent-video-analytics/tao-toolkit/17)\n- Deploy your model on the edge using DeepStream. Learn more about [DeepStream SDK](https://developer.nvidia.com/deepstream-sdk)\n\n## License \n\nLicense to use this model is covered by CCBY 4.0. By downloading the unpruned or pruned version of the model, you accept the terms and conditions of these [licenses](https://creativecommons.org/licenses/by/4.0/).\n\n## Ethical Considerations \n\nNVIDIA Visual ChangeNet-Segmentation model detects changes between pair-wise images. NVIDIA’s platforms and application frameworks enable developers to build a wide array of AI applications. Consider potential algorithmic bias when choosing or creating the models being deployed. 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These cookies enable us to provide enhanced website functionality and personalization as well as make the marketing messages we send to you more relevant to your interests. If you do not allow these cookies, then some or all of these services may not function properly.",[617,618,619,620,621,622,623,624,625,626,627,628,629,630,631,632,633,634,635,636,637],[582,583,584,585,586],{"_385":477,"_216":477,"_168":169,"_358":608},{"_385":443,"_216":443,"_168":169,"_358":603},{"_385":596,"_216":596,"_168":169,"_358":597},{"_385":592,"_216":592,"_168":169,"_358":593},{"_385":587,"_216":587,"_168":169,"_358":588},"nvcrm.my.site.com",[589],{"_79":590,"_391":587,"_392":31,"_168":591},"BrowserId","If you know anything about this cookie and its uses that are not specific to a particular site please add your contributions.","nvidia.partners",[594],{"_79":595,"_391":592,"_392":31,"_168":426},"edge","github.com",[598,599],{"_79":602,"_391":596,"_392":31,"_168":601},{"_79":600,"_391":596,"_392":31,"_168":601},"logged_in","This domain is owned by Github. 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