SegIC: A Generalist Model for Segmenting Everything in Context

Model Overview

Description:

The SegIC (Segment-In-Context) model is designed for in-context segmentation, which aims to segment novel images using a few labeled example images (in-context examples). The research study is at SEGIC: Unleashing the Emergent Correspondence for In-Context Segmentation.

This model is ready for commercial use.

SegIC shows powerful performance as a generalist model for segmenting everything in context. It achieves SOTA results in COCO-20i, FSS-1000 and recent LVIS-92i. The NVIDIA SegIC model is ready for commerical use. It has a deployable version available. We also demonstrate its advantageous performance in retail object detection, while only training the model with a fair number of synthetic retail data.

License/Terms of Use

License to use these models is covered by the NVIDIA Open Model License. By downloading the model, you accept the terms and conditions of these licenses.

References:

Meng, Lingchen, et al. \"SEGIC: Unleashing the Emergent Correspondence for In-Context Segmentation.\" arXiv preprint arXiv:2311.14671 (2023).

Nguyen, Khoi, and Sinisa Todorovic. \"Feature weighting and boosting for few-shot segmentation.\" Proceedings of the IEEE/CVF International Conference on Computer Vision. 2019.

Li, Xiang, et al. \"Fss-1000: A 1000-class dataset for few-shot segmentation.\" Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2020.

Gupta, Agrim, Piotr Dollar, and Ross Girshick. \"Lvis: A dataset for large vocabulary instance segmentation.\" Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2019.

Model Architecture:

Architecture Type: Vision Transformer
\nNetwork Architecture:\nSegIC is mainly built upon a vision foundation model, a text encoder, and a lightweight mask decoder

Image encoder: For image feature extraction. DINOv2-L traind with proprietary images from NVIDIA
Meta description encoder: For meta feature extraction. CLIP-B trained with proprietary images from NVIDIA
Mask Decoder: For mask decoding. It is mainly composed of a few convolutional layers, projection layers and a transformer decoder.

For more details, please refer to the research paper stated in References.

Input:

Input Types:

Example images: the example image containing target objects
Example image masks: the masks of target objects in the example images. Each mask only contains one object.
(Optional) Meta description: a sentence or words describing target objects
Target image: the test image in search of target objects
Original size: the original size of the target image

Input Formats:

Example images: Red, Green, Blue (RGB). Can support any input resolution and images do not need any additional pre-processing (e.g. alpha channels or bits), but all example images needs to be in the same size.
Example image masks: Grayscale. Resolutions needs to be the same as the example images.
(Optional) Meta description: Tokenized String. Max length 77 words.
Target images: Red, Green, Blue (RGB). Can support any input resolution and images do not need any additional pre-processing (e.g. alpha channels or bits).
Original size: Height, width

Input Parameters:

Example images: Four dimensional (4D), batch_size x 3 x896 x896
Example image masks: 4D, batch_size x 1 x896 x896
meta description: 2D, batch_size x 64
Target images: 2D, 1 x 3 x 896 x 896
Original sizes: 2D, 1 x 2

Other Properties Related to Input:

Example images: Resized to 896x896 at input
Example image masks: Resized to 896x896 at input
Target image: Resized to 896x896 at input. Currently it only supports one target image each time at inference.

Output:

Output Types: Predicted segmentation masks.
\nOutput Format: a tensor with shape of batch_size x 1 x 896 x 896
\nOutput Parameters: Four Dimensional
\nOther Properties Related to Output:

segmentation masks: resolution 896x896. Post-processing is needed to resize predicted masks to Target images original sizes.
\nPost processing step:\n

# original size: W x H, predicted mask size: 896 x 896\nmax_l = max(W, H)\nh = 896 * H/max_l\nw = 869 * W/max_l\npred_mask = pred_mask[:, :, :h, :w]\npred_mask_original_size = interpolate(pred_mask, (H, W))\n

Software Integration:

Runtime Engines:

TAO 5.5.0

Supported Hardware Microarchitecture Compatibility:

NVIDIA Ampere
NVIDIA Blackwell
NVIDIA Jetson
NVIDIA Hopper
NVIDIA Lovelace
NVIDIA Pascal
NVIDIA Turing
NVIDIA Volta

[Preferred/Supported] Operating Systems:

Linux

Model Versions:

segic_v1.0_unpruned_deployable: Trained SegIC for general in-context segmentation tasks
\nprompt_feature_extract_v1.0_unpruned_deployable: SegIC feature extractor for example images

Training, Testing, and Evaluation Datasets:

Training Dataset:

Link:

OpenImage subset
MSCOCO 2017 subset: a commercially viable filtered version
Synthetic Retail data: Generated by NVIDIA Omniverse Replicator Object. Details of the dataset is described in Retail Object Detection: Synthetic Data - v2.2.x.2 Fine-Tune.
nSpect: NSPECT-156S-4A48

Data Collection Method by Dataset

Hybrid: synthetic, human

Labeling Method by Dataset

Hybrid: synthetic, automated labeling

Properties:
\nThe training dataset is a combination of 3 semantic segmentation datasets: MSCOCO 2017 subset, OpenImage, and synthetic retail data. All three datasets are semantic segmentation data. Below is the summary of each dataset:

\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

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Dataset	Description	# Classes	# Images
MSCOCO 2017 subset	Semantic segmentation dataset. It contains 80 categories of common objects. The subset extracted commercial viable images from MSCOCO.	80	48,000
OpenImage subset	The OpenImage images were auto-labeled with 80 MSCOCO categories.	80	64,000
Synthetic Retail data	scanned retail objects inserted to a mixture of synthetic scenes	315	48,000

Evaluation Dataset:

Link:

COCO-20i: a split of MSCOCO 2014 for one-shot segmentation benchmark. Details of the derivation of COCO20-i from MSCOCO see here
Voyager Cafe KPI: Retail object checkout scene collected in NVIDIA Voyager Cafeteria. Details of the dataset is described in Retail Object Detection: Performance - Evaluation Data.

Data Collection Method by Dataset

Human

Labeling Method by Dataset

Human

Properties:

\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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Dataset	Description	# Classes	# Images
COCO-20i	The one-shot evaluation splits the MSCOCO 2014 validation set to 4 sets for cross validation. Each set contains 20 classes.	80	5,000
Voyager Cafe KPI	A binary retail object detection dataset. Images of retail objects in 7 scenes, including checkout counter, shelves and convoyer belt. The 9 categories are manually assigned based on retail object geometry shapes.	1	6,042

Evaluation Results

COCO-20i

One-shot semantic segmentation

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

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Model	Training data	# shots / category	mIoU
NVIDIA SegIC	commercial-viable MSCOCO 17, Synthetic retail data, OpenImage	1	69.57

Voyager Cafe KPI

Binary retail object detection

\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

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Model	Model Architecture	Training Data	# shots / category	mAP
NVIDIA SegIC	SegIC	commercial-viable MSCOCO 17, Synthetic retail data, OpenImage	13	0.904
Retail Object Detection - v2.2.1.1 (supervised learning model)	DINO-FAN-small	Synthetic retail data	N/A	0.834

All SegIC models uses the same group of visual prompts for evaluation. The detection accuracy mAP50 is estimated from segmentation output.

Inference:

Engine: Tensor(RT)
\nTest Hardware:

A2
A30
DGX H100
DGX A100
DGX H100
JAO 64GB
Jetson AGX Xavier
L4
L40
NVIDIA T4
Orin
Orin Nano 8GB
Orin NX
Orin NX16GB
T4
Xavier NX

The inference is run on the provided unpruned model at FP16 precision. The inference performance is run using trtexec on AGX Orin 64GB, and Jetson Orin NX 16GB devices. 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\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\n\n\n

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Platform	TRT Version	Model	Batch Size	FPS
AGX Orin 64GB	8.6.2.3	SegIC	1	4.13
AGX Orin 64GB	8.6.2.3	Prompt Feature Extract	1	4.14
AGX Orin 64GB	8.6.2.3	SegIC	8	27.49
AGX Orin 64GB	8.6.2.3	Prompt Feature Extract	4	4.34
Jetson Orin NX 16GB	8.6.2.3	SegIC	1	1.26
Jetson Orin NX 16GB	8.6.2.3	Prompt Feature Extract	1	1.28
Jetson Orin NX 16GB	8.6.2.3	SegIC	8	9.03
Jetson Orin NX 16GB	8.6.2.3	Prompt Feature Extract	8	1.37
DGX H100 80GB	8.6.2.3	SegIC	1	60
DGX H100 80GB	8.6.2.3	Prompt Feature Extract	1	61
DGX H100 80GB	8.6.2.3	SegIC	4	55
DGX H100 80GB	8.6.2.3	Prompt Feature Extract	4	219
Tesla A30	8.6.2.3	SegIC	1	15
Tesla A30	8.6.2.3	Prompt Feature Extract	1	15
Tesla A30	8.6.2.3	SegIC	4	14
Tesla A30	8.6.2.3	Prompt Feature Extract	4	55

Inference Method

NVIDIA TAO Toolkit now provides a Gradio app to the deployable SegIC models.

To launch the Gradio demo, you can follow the instruction at tao_pytorch_backend for interactive experience.

Ethical Considerations:

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, Bias, Safety & Security, and Privacy Subcards [Insert Link to Model Card++ here]. Please report security vulnerabilities or NVIDIA AI Concerns here.

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\n\n### License/Terms of Use\nLicense to use these models is covered by the NVIDIA Open Model License. By downloading the model, you accept the terms and conditions of these [licenses](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/).\n\n## References:\nMeng, Lingchen, et al. \"SEGIC: Unleashing the Emergent Correspondence for In-Context Segmentation.\" arXiv preprint arXiv:2311.14671 (2023).
\n\nNguyen, Khoi, and Sinisa Todorovic. \"Feature weighting and boosting for few-shot segmentation.\" Proceedings of the IEEE/CVF International Conference on Computer Vision. 2019.
\n\nLi, Xiang, et al. \"Fss-1000: A 1000-class dataset for few-shot segmentation.\" Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2020.\n\nGupta, Agrim, Piotr Dollar, and Ross Girshick. \"Lvis: A dataset for large vocabulary instance segmentation.\" Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2019.\n\n\n## Model Architecture: \n**Architecture Type:** Vision Transformer
\n**Network Architecture:** \nSegIC is mainly built upon a vision foundation model, a text encoder, and a lightweight mask decoder\n* Image encoder: For image feature extraction. [DINOv2-L](https://arxiv.org/abs/2304.07193) traind with proprietary images from NVIDIA\n* Meta description encoder: For meta feature extraction. [CLIP-B](https://arxiv.org/abs/2103.00020) trained with proprietary images from NVIDIA
\n* Mask Decoder: For mask decoding. It is mainly composed of a few convolutional layers, projection layers and a transformer decoder.\n\nFor more details, please refer to the research paper stated in `References`.\n\n## Input:\n**Input Types:**
\n* Example images: the example image containing target objects
\n* Example image masks: the masks of target objects in the example images. Each mask only contains one object.
\n* (Optional) Meta description: a sentence or words describing target objects
\n* Target image: the test image in search of target objects
\n* Original size: the original size of the target image\n\n**Input Formats:**
\n* Example images: Red, Green, Blue (RGB). Can support any input resolution and images do not need any additional pre-processing (e.g. alpha channels or bits), but all example images needs to be in the same size.
\n* Example image masks: Grayscale. Resolutions needs to be the same as the example images.
\n* (Optional) Meta description: Tokenized String. Max length 77 words.
\n* Target images: Red, Green, Blue (RGB). Can support any input resolution and images do not need any additional pre-processing (e.g. alpha channels or bits).
\n* Original size: Height, width\n\n**Input Parameters:**
\n* Example images: Four dimensional (4D), batch_size x 3 x896 x896\n* Example image masks: 4D, batch_size x 1 x896 x896\n* meta description: 2D, batch_size x 64\n* Target images: 2D, 1 x 3 x 896 x 896\n* Original sizes: 2D, 1 x 2\n\n**Other Properties Related to Input:** \n* Example images: Resized to 896x896 at input
\n* Example image masks: Resized to 896x896 at input
\n* Target image: Resized to 896x896 at input. Currently it only supports **one** target image each time at inference.
\n\n## Output:\n**Output Types:** Predicted segmentation masks.
\n**Output Format:** a tensor with shape of batch_size x 1 x 896 x 896
\n**Output Parameters:** Four Dimensional
\n**Other Properties Related to Output:**
\n* segmentation masks: resolution 896x896. Post-processing is needed to resize predicted masks to `Target images` original sizes.
\nPost processing step:\n ```\n # original size: W x H, predicted mask size: 896 x 896\n max_l = max(W, H)\n h = 896 * H/max_l\n w = 869 * W/max_l\n pred_mask = pred_mask[:, :, :h, :w]\n pred_mask_original_size = interpolate(pred_mask, (H, W))\n ```\n\n## Software Integration:\n**Runtime Engines:** \n* TAO 5.5.0
\n\n**Supported Hardware Microarchitecture Compatibility:**
\n* NVIDIA Ampere
\n* NVIDIA Blackwell
\n* NVIDIA Jetson
\n* NVIDIA Hopper
\n* NVIDIA Lovelace
\n* NVIDIA Pascal
\n* NVIDIA Turing
\n* NVIDIA Volta
\n\n**[Preferred/Supported] Operating Systems:**
\n* Linux
\n\n## Model Versions: \n**segic_v1.0_unpruned_deployable**: Trained SegIC for general in-context segmentation tasks
\n**prompt_feature_extract_v1.0_unpruned_deployable**: SegIC feature extractor for example images
\n\n# Training, Testing, and Evaluation Datasets: \n\n## Training Dataset:\n\n**Link:**
\n* [OpenImage](https://storage.googleapis.com/openimages/web/index.html) subset\n* [MSCOCO 2017](https://cocodataset.org/#download) subset: a commercially viable filtered version \n* Synthetic Retail data: Generated by [NVIDIA Omniverse Replicator Object](https://docs.omniverse.nvidia.com/isaacsim/latest/replicator_tutorials/tutorial_replicator_object.html). Details of the dataset is described in [Retail Object Detection](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/retail_object_detection): Synthetic Data - v2.2.x.2 Fine-Tune.\n* nSpect: NSPECT-156S-4A48\n\n**Data Collection Method by Dataset**
\n* Hybrid: synthetic, human
\n\n**Labeling Method by Dataset**
\n* Hybrid: synthetic, automated labeling\n\n**Properties:**
\nThe training dataset is a combination of 3 semantic segmentation datasets: MSCOCO 2017 subset, OpenImage, and synthetic retail data. All three datasets are semantic segmentation data. Below is the summary of each dataset:\n\n| Dataset | Description | \\# Classes | \\# Images |\n| --------------------- | -------------------------------------------------------------------------------------------------------------------------------------- | ---------- | --------- |\n| MSCOCO 2017 subset | Semantic segmentation dataset. It contains 80 categories of common objects. The subset extracted commercial viable images from MSCOCO. | 80 | 48,000 |\n| OpenImage subset | The OpenImage images were auto-labeled with 80 MSCOCO categories. | 80 | 64,000 |\n| Synthetic Retail data | scanned retail objects inserted to a mixture of synthetic scenes | 315 | 48,000 |\n\n## Evaluation Dataset:\n**Link:**
\n* [COCO-20i](https://cocodataset.org/#home): a split of MSCOCO 2014 for one-shot segmentation benchmark. Details of the derivation of COCO20-i from MSCOCO see [here](https://arxiv.org/pdf/1909.13140)\n* Voyager Cafe KPI: Retail object checkout scene collected in NVIDIA Voyager Cafeteria. Details of the dataset is described in [Retail Object Detection](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/retail_object_detection): Performance - Evaluation Data.\n\n**Data Collection Method by Dataset**
\n* Human
\n\n**Labeling Method by Dataset**
\n* Human
\n\n**Properties:**
\n| Dataset | Description | \\# Classes | \\# Images |\n| ---------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------- | --------- |\n| COCO-20i | The one-shot evaluation splits the MSCOCO 2014 validation set to 4 sets for cross validation. Each set contains 20 classes. | 80 | 5,000 |\n| Voyager Cafe KPI | A binary retail object detection dataset. Images of retail objects in 7 scenes, including checkout counter, shelves and convoyer belt. The 9 categories are manually assigned based on retail object geometry shapes. | 1 | 6,042 |\n\n\n## Evaluation Results\n\n### COCO-20i\nOne-shot semantic segmentation\n\n| Model | Training data | \\# shots / category | mIoU |\n| --------------------- | ------------------------------------------------------------- | ------------------- | ----- |\n| NVIDIA SegIC | commercial-viable MSCOCO 17, Synthetic retail data, OpenImage | 1 | 69.57 |\n\n### Voyager Cafe KPI\nBinary retail object detection\n\n| Model | Model Architecture | Training Data | \\# shots / category | mAP |\n| -------------------------------------------------------------- | ------------------ | ------------------------------------------------------------- | ------------------- | ----- |\n| NVIDIA SegIC | SegIC | commercial-viable MSCOCO 17, Synthetic retail data, OpenImage | 13 | 0.904 |\n| Retail Object Detection - v2.2.1.1 (supervised learning model) | DINO-FAN-small | Synthetic retail data | N/A | 0.834 |\n\nAll SegIC models uses the same group of visual prompts for evaluation. The detection accuracy mAP50 is estimated from segmentation output.\n\n\n## Inference:\n**Engine:** Tensor(RT)
\n**Test Hardware:**
\n- A2\n- A30\n- DGX H100\n- DGX A100\n- DGX H100\n- JAO 64GB\n- Jetson AGX Xavier\n- L4\n- L40\n- NVIDIA T4\n- Orin\n- Orin Nano 8GB\n- Orin NX\n- Orin NX16GB\n- T4\n- Xavier NX\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 AGX Orin 64GB, and Jetson Orin NX 16GB devices. 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| Platform | TRT Version | Model | Batch Size | FPS |\n| ------------------- | ----------- | ---------------------- | ---------- | ----- |\n| AGX Orin 64GB | 8.6.2.3 | SegIC | 1 | 4.13 |\n| AGX Orin 64GB | 8.6.2.3 | Prompt Feature Extract | 1 | 4.14 |\n| AGX Orin 64GB | 8.6.2.3 | SegIC | 8 | 27.49 |\n| AGX Orin 64GB | 8.6.2.3 | Prompt Feature Extract | 4 | 4.34 |\n| Jetson Orin NX 16GB | 8.6.2.3 | SegIC | 1 | 1.26 |\n| Jetson Orin NX 16GB | 8.6.2.3 | Prompt Feature Extract | 1 | 1.28 |\n| Jetson Orin NX 16GB | 8.6.2.3 | SegIC | 8 | 9.03 |\n| Jetson Orin NX 16GB | 8.6.2.3 | Prompt Feature Extract | 8 | 1.37 |\n| DGX H100 80GB | 8.6.2.3 | SegIC | 1 | 60 |\n| DGX H100 80GB | 8.6.2.3 | Prompt Feature Extract | 1 | 61 |\n| DGX H100 80GB | 8.6.2.3 | SegIC | 4 | 55 |\n| DGX H100 80GB | 8.6.2.3 | Prompt Feature Extract | 4 | 219 |\n| Tesla A30 | 8.6.2.3 | SegIC | 1 | 15 |\n| Tesla A30 | 8.6.2.3 | Prompt Feature Extract | 1 | 15 |\n| Tesla A30 | 8.6.2.3 | SegIC | 4 | 14 |\n| Tesla A30 | 8.6.2.3 | Prompt Feature Extract | 4 | 55 |\n\n\n## Inference Method\n[NVIDIA TAO Toolkit](https://docs.nvidia.com/tao/tao-toolkit/text/overview.html) now provides a Gradio app to the deployable SegIC models.\n\nTo launch the Gradio demo, you can follow the instruction at [tao_pytorch_backend](https://github.com/NVIDIA/tao_pytorch_backend/tree/main/nvidia_tao_pytorch/cv/segic/README.md) for interactive experience.\n\n\n## Ethical Considerations:\nNVIDIA 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, Bias, Safety & Security, and Privacy Subcards [Insert Link to Model Card++ here]. Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).","SegIC",["D",1732745365282],"/api/image/nvidia/tao/models/segic","https://dz112fgwz7ogh.cloudfront.net/logos/Nvidia-Centric-TAO.png",1239941612,"bias","| Field | Response |\n| -- | -- |\n|Participation considerations from adversely impacted groups [(protected classes)](https://www.senate.ca.gov/content/protected-classes) in model design and testing: | None of the Above |\n| Measures taken to mitigate against unwanted bias: | No measures taken to mitigate against unwanted bias.|","explainability","| Field | Response |\n| -- | -- |\n| Intended Application(s) & Domain(s): | In-Context Segmentation |\n| Model Type: | Semantic Segmentation |\n| Intended Users: | This model is intended for developers to build in-context semantic segmentation applications. |\n| Output: | Segmentation masks |\n| Describe how the model works: | Generates segmentation masks of the test images/frames based on visual prompts features. |\n| Technical Limitations: | This model may fail to segment the target items or segment wrong areas. |\n| Verified to have met prescribed NVIDIA standards: | Yes |\n| Performance Metrics: | Mean Intersection over Union (mIoU) for segmentation evaluation; mean Average Precision (mAP) for processed bounding box evaluation |\n| Licensing: | [NVIDIA Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/) |","privacy","| Field | Response |\n| -- | -- |\n| Generatable or reverse engineerable personally-identifiable information (personal data)? | None |\n| Protected classes used to create this model? | Not Applicable (No personal data) |\n| Was consent obtained for any personal data used? | Not Applicable (No personal data) |\n| How often is dataset reviewed? | \tBefore Release |\n| Is a mechanism in place to honor data subject right of access or deletion of personal data? | Not Applicable (No personal data) |\n| If personal data collected for the development of the model, was it collected directly by NVIDIA? |Not Applicable (No personal data) |\n| If personal data collected for the development of the model by NVIDIA, do you maintain or have access to disclosures made to data subjects?\t| Not Applicable (No personal data) |\n| If personal data collected for the development of this AI model, was it minimized to only what was required? | Not Known (Collected by Third-Party) |\n| Is there provenance for all datasets used in training? | Yes |\n| Does data labeling (annotation, metadata) comply with privacy laws? | Yes |\n| Is data compliant with data subject requests for data correction or removal, if such a request was made? | Yes, for data collected by NVIDIA. No, for all externally-sourced data. |\n| Applicable NVIDIA Privacy Policy\t| [https://www.nvidia.com/en-us/data-center/products/nvidia-ai-enterprise/eula/](https://www.nvidia.com/en-us/data-center/products/nvidia-ai-enterprise/eula/) |","safetyAndSecurity","| Field | Response |\n| -- | -- |\n| Model Application(s): | In-Context Segmentation (General Purpose) |\n| Describe the life-critical application (if present). | None: Not within Operational Design Domain |\n| Use Case Restrictions: | None |\n| Describe access restrictions (if any): | The Principle of least privilege (PoLP) is applied limiting access for dataset generation and model development. |",[],[291,292,293,294,295,296],[],[],[],{"_91":144,"_128":145,"_129":307,"_131":132},{"_91":305,"_128":305,"_129":306,"_131":132},{"_91":303,"_128":303,"_129":304,"_131":132},{"_91":301,"_128":301,"_129":302,"_131":132},{"_91":92,"_128":92,"_129":300,"_131":132},{"_91":297,"_128":297,"_129":298,"_131":132},"TAO Toolkit",{"_133":299,"_128":297},"framework",{"_133":132,"_128":92},"NSPECT-FY5H-V6CO",{"_133":132,"_128":301},"Deep Learning",{"_133":132,"_128":303},"CV",{"_133":132,"_128":305},{"_133":137,"_128":145},{"_177":1282,"_179":1283},"ngcApps",[1235],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1216":1217,"_1218":1219,"_1220":1221,"_1222":1223,"_1224":319,"_209":345,"_1225":1226,"_1227":51,"_1228":155,"_1229":155,"_1230":1231,"_1232":155,"_1233":1234},"amplitude",{"_1213":1214,"_859":-7,"_1215":26},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1207":1208,"_1209":1210,"_1211":1212},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1787654053675,"1.12.0","defaultSearchSuggestions",{"_1099":235,"_1100":1101,"_1102":1103,"_1104":1105},"announcements",[],"consentState",{"_384":1089,"_1090":155,"_1091":26,"_1092":26,"_1093":1094},"consentConfig",{"_366":367,"_368":369,"_370":371,"_372":373,"_374":375,"_376":377,"_378":379,"_380":381,"_382":383,"_384":385},"showContactAdmin",1787654053677,[],[],[359,360,361,362,363,364,365],{"_40":54,"_42":65,"_44":172,"_46":-5,"_48":-5,"_50":51},{"_40":54,"_42":63,"_44":172,"_46":-5,"_48":-5,"_50":51},{"_40":54,"_42":61,"_44":172,"_46":-5,"_48":-5,"_50":51},{"_40":54,"_42":59,"_44":172,"_46":-5,"_48":-5,"_50":51},{"_40":54,"_42":55,"_44":172,"_46":-5,"_48":-5,"_50":51},{"_40":52,"_42":53,"_44":45,"_46":47,"_48":49,"_50":51},{"_40":41,"_42":43,"_44":172,"_46":-5,"_48":-5,"_50":51},"mainInfoText","We and our third-party partners (including social media, advertising, and analytics partners) use cookies and other tracking technologies to collect, store, monitor, and process certain information about you when you visit our website. 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","kndctr_F207D74D549850760A4C98C6_AdobeOrg_bc_session_id","ttcsid_XXXXXXXXXXXXXXXXXXXXCategory","This cookie name is associated with TikTok which is used to track your activity on the platform, including how you interact with their embedded services on other websites.This cookie is associated with TikTok. It helps track your activity on the platform, including how you interact with their embedded services on other websites.","_fbp","Used by Facebook to deliver a series of advertisement products such as real time bidding from third party advertisers","_zitok","_twpid","This cookie is typically associated with the use of Taptap Digital, a company specializing in digital advertising. It is used for tracking and ad personalization purposes.","_tt_enable_cookie","This cookie name is associated with TikTok which is used to collect data about behavior and purchases on the website and to measure the effect of advertising. 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