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Field	Response
Participation considerations from adversely impacted groups (protected classes) in model design and testing:	Not Applicable
Measures taken to mitigate against unwanted bias:	Not Applicable.

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TAO Pretrained Mask Grounding DINO with Commercial License

Description

Open vocabulary instance segmentation is a computer vision technique that can segment one or multiple objects in a frame based on the text input. Object segmentation recognizes the individual objects in an image and predicts bounding boxes and the segmentation masks. This model card contains pre-trained weights for the Mask Grounding DINO model pretrained on the commercial dataset. The goal of this card is to facilitate transfer learning through the Train Adapt Optimize (TAO) Toolkit.

This model is ready for commercial use.

License

GOVERNING TERMS: Use of this model is governed by the NVIDIA Open Model License

Deployment Geography:

Global

Use Case:

\n
Open-Set (or Open-Vocabulary) Object Detection and Segmentation:\nGrounding DINO can detect objects that were not pre-defined in the training categories — you can give it new category names via text (e.g., “lion”, “bench”, “ear”) and it will localise them in images.\nThis is useful in systems that need flexibility to recognise arbitrary categories without retraining for each new class.
\n
\n
Referring Expression / Text-Guided Object Detection and Segmentation:\nThe model supports being given a referring expression (“the bottom man with his head up”, “the dog next to the tree”) and then detect/localise the object described.
\n

Release Date:

NGC [11/25/2025]

References

Zhang, H., Li, F., Liu, S., Zhang, L., Su, H., Zhu, J., Ni, L., Shum, H.: DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection.
Ziu, S., Zeng, Z., Ren, T., Li, F., Zhang, H., Yang, J., Li, C., Yang, J., Su, H., Zhu, J., Zhang, L.: Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection.
Devlin, J., Chang, M., Lee, K., Toutanova, K.: BERT: PRe-training of Deep Bidirectional Transformers for Language Understanding.
Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin Transformer: Hierarchical Vision Transformer using Shifted Windows.
Tian Z., Shen C., Chen H.: Conditional Convolutions for Instance Segmentation.
Liu, C., Ding, H., & Jiang, X.: Gres: Generalized referring expression segmentation.

Model Architecture

Architecture Type: Transformer-Based Segmentation Model
\nNetwork Architecture: Swin-Tiny
\nNumber of Parameters: 182,668,026 params

This model is an NVIDIA proprietary open-vocabulary object segmentation model that takes RGB images and either a list of phrases or a referring expression as input, and outputs bounding boxes, segmentation masks, and corresponding labels.

The model was initialized from a commercial GroundingDINO checkpoint. It employs a Swin-Tiny backbone with a transformer-based segmentation head. All components—including the ReLA module—were pretrained end-to-end on a large-scale mixed corpus of approximately 1.2 million image–expression or image–category name pairs, sourced from Open Images V5, Localized Narratives, COCO, and RefCOCO variants.

Following large-scale pretraining, the model was fine-tuned on RefCOCO, RefCOCO+, and RefCOCOg, using roughly 100,000 referring expression–image pairs to improve grounding accuracy and region-level localization.

All raw images used during training were verified to have commercial licenses, ensuring safe and compliant commercial deployment.

Input(s):

Input Type(s):
\nImage and Text (list of tokenized captions processed via Hugging Face).

Input Format(s):

Image: Red, Green, Blue (RGB)
Text: Tokenized string inputs
Resolution: Minimum 32 × 32 pixels required
Other: No alpha channel; standard 8-bit color depth

Input Parameters:

Image: Two-Dimensional (2D)
Text: One-Dimensional (1D)

Other Properties Related to Input:

inputs: B × 3 × 544 × 960 — RGB image tensor (Batch Size × Channels × Height × Width)
input_ids: B × 256 — tokenized text input IDs (Batch Size × Max Token Length)
attention_mask: B × 256 — attention mask for text tokens
position_ids: B × 256 — positional encoding indices
token_type_ids: B × 256 — token type indicators for multi-sentence text
text_token_mask: B × 256 × 256 — attention map between tokens (Batch Size × Max Token Length × Max Token Length)

Because ONNX and TensorRT do not support string inputs directly, the tokenization step is performed externally using the Hugging Face tokenizer.
\nFor deployment details, refer to the TAO Deploy Repository.

Output(s):

Output Type(s):
\nBounding Boxes, Confidence Scores, Segmentation Masks for each detected object in the input image, and a No-Target Indicator.

Output Format(s):

Bounding Boxes: Predicted 2D boxes in (cx, cy, w, h) format
Confidence Scores: Scalar scores for each predicted object
Segmentation Masks: 3D masks aligned with the input image dimensions
No-Target Indicator: Binary logits indicating whether a query corresponds to a valid object or not

Output Parameters:

Bounding Boxes: Two-Dimensional (2D)
Segmentation Masks: Three-Dimensional (3D)
Confidence Scores: One-Dimensional (1D)
No-Target Indicator: Two-Dimensional (B × 2)

Other Properties Related to Output:

pred_logits: B × 900 — classification logits for each query (Batch Size × Number of Queries)
pred_boxes: B × 900 × 4 — predicted bounding boxes in (cx, cy, w, h) format
pred_masks: B × 900 × H × W — segmentation masks for each predicted object
no_targets: B × 2 — binary logits indicating positive/negative (object/no-object) classes
union_logit_masks: B × H × W — union of predicted object masks across all queries (objectness map)

Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA’s hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.

Hardware Optimization Note:
\nThis model is optimized for NVIDIA GPU-accelerated systems.
\nBy leveraging CUDA, TensorRT, and other NVIDIA frameworks, it achieves significantly faster training and inference performance compared to CPU-only systems.

Software Integration

Runtime Engines:

TAO 5.5.0

Supported Hardware Architectures:

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

Supported Operating Systems:

Linux
Linux 4 Tegra

Model Versions

mask_grounding_dino_swin_tiny_commercial_trainable_v2.0 - Fine-tuned Swin-Tiny Mask Grounding DINO model on Commercial dataset.

\n\n

Training and Evaluation

This model was trained using the mask_grounding_dino entrypoint in TAO. The training algorithm optimizes the network to minimize the localization and contrastive embedding loss between text and visual features as well as the mask losses between predictions and mask groundtruth.

Using 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 intended use for these models is detecting objects in a color (RGB) image. The model can be used to detect objects from photos and videos by using appropriate video or image decoding and pre-processing.

These models are intended for training and fine-tune with the TAO Toolkit and your datasets for object detection. High-fidelity models can be trained with 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 easy edge deployment using TensorRT.

Using the Model with TAO

To use these models as pretrained weights for transfer learning, use the following snippet as a template for the model and train components of the experiment spec file. For more information on the experiment spec file, see the TAO Toolkit User Guide.

train:\n  pretrained_model_path: /path/to/the/mask_groundingdino.pth\n  freeze: [\"backbone.0\", \"bert\"]  # freeze the backbone for finetuning\nmodel:\n  backbone: swin_tiny_224_1k\n  num_feature_levels: 4\n  dec_layers: 6\n  enc_layers: 6\n  num_queries: 900\n  dropout_ratio: 0.0\n  dim_feedforward: 2048\n  log_scale: auto\n  class_embed_bias: True\n  num_region_queries: 100\n  loss_types: ['labels', 'boxes', 'masks', 'rela']\n

Training Dataset

Training Data

RefCOCO

Link: https://github.com/lichengunc/refer
\nData Collection Method by dataset: Human.
\nLabeling Method by dataset: Human.
\nProperties: The RefCOCO dataset provides referring expressions linking natural-language descriptions to specific object instances in images.

Data size: 36,459 samples
Expression style: Short (~3.5 words average), allows spatial terms.

Dataset License(s): Commercial-use permitted

RefCOCO+

Link: https://github.com/lichengunc/refer
\nData Collection Method by dataset: Human.
\nLabeling Method by dataset: Human.
\nProperties: RefCOCO+ focuses on appearance descriptors rather than spatial descriptions.

Data size: 36,302 samples
Expression style: Short (~3.5 words average), no spatial terms allowed.

RefCOCOg

Link: https://github.com/lichengunc/refer
\nData Collection Method by dataset: Human.
\nLabeling Method by dataset: Human.
\nProperties: RefCOCOg offers longer, more descriptive referring expressions for visual grounding tasks.

Data size: 23,695 samples
Expression style: Long, descriptive, more challenging for grounding models.

COCO (Subset)

Link: COCO dataset
\nData Collection Method by dataset: Human.
\nLabeling Method by dataset: Human-verified annotations.
\nProperties: This subset of COCO is used for open-vocabulary grounding with a fixed set of 80 category names.

Data size: 36,055 images
Object categories: 80
Provides bounding boxes and category labels for open-vocabulary object segmentation.

OpenImages V5

Link: Open Images V7 overview
\nData Collection Method by dataset: Hybrid: Automated web image collection with human verification
\nLabeling Method by dataset: Human
\nProperties:
\nOpenImages provides large-scale annotations for object detection and segmentation, suitable for open-vocabulary grounding.

Data size: 662,956 samples (image-category name pairs)
Object categories: 350+

Localized Narratives

Link: Localized Narratives
\nData Collection Method by dataset: Human.
\nLabeling Method by dataset: Pseudo-labeling.
\nProperties:
\nProvides dense vision-language annotations for fine-grained grounding of text to image regions.

Data size: 459,999 samples
Supports detailed region grounding for open-vocabulary segmentation

All datasets were selected for commercial-use license compatibility. These sources collectively provide high-quality visual-language supervision for both targeted object grounding and no-target reasoning tasks.

Evaluation Data

Data Collection Method by dataset:
\nHuman

Labeling Method by dataset:
\nHuman

Properties (Evaluation Sets and General Information):

Referring-expression segmentation (RES) requires models to output object masks given natural language expressions. Classic datasets (RefCOCO, RefCOCO+, RefCOCOg) mostly focus on single-target expressions, while gRefCOCO supports single-, multi-, and no-target expressions, making it suitable for generalized RES (GRES) tasks.

\n
gRefCOCO:
\n
- Validation Set: 16,870 expressions / 8,163 sets / 1,500 images
- Test Set A (People): 18,712 expressions / 6,266 sets / 750 images
- Test Set B (Objects): 14,933 expressions / 4,618 sets / 750 images
- Expression Types: Mixed (single-, multi-, no-target)
- Use Case: Phrase-level grounding; multi-target and no-target scenarios
\n
\n
RefCOCO:
\n
- Validation Set: 3,811 expressions / 1,500 images
- Test Set A (People): 1,975 expressions / 750 images
- Test Set B (Objects): 1,810 expressions / 750 images
- Expression Style: Short (~3.6 words), allows spatial terms
- Use Case: Single-target RES; people-vs-objects split
\n
\n
RefCOCO+:
\n
- Validation Set: 3,805 expressions / 1,500 images
- Test Set A (People): 1,975 expressions / 750 images
- Test Set B (Objects): 1,798 expressions / 750 images
- Expression Style: Short (~3.5 words), no spatial terms; appearance-based
- Use Case: Single-target RES; appearance-based grounding
\n
\n
RefCOCOg:
\n
- Validation Set (Google): 10,234 expressions / 4896 images
- Validation Set (UMD): 5,000 expressions / 5,257 images
- Test Set (UMD): 5,023 expressions / 5,096 images
- Expression Style: Long, descriptive expressions
- Use Case: Single-target RES; per-object split
\n

Methodology and KPI

The evaluation metrics focus on generalized Intersection over Union (gIoU), target accuracy (T_acc), and no-target accuracy (N_acc):

gIoU: Mean of per-image IoU values across all samples; for true positive no-target samples, IoU = 1, otherwise IoU = 0.
T_acc: Accuracy on target samples, measuring robustness to no-target generalization.
N_acc: Accuracy in correctly identifying no-target samples (true positive no-target predictions divided by total no-target samples).
mask_mAP: Mean Average Precision (mAP) computed across multiple IoU thresholds (0.5 to 0.95); used to evaluate segmentation performance.

Evaluation Results: gRefCOCO

\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\">

Eval Set	gIoU (%)	T_acc (%)	N_acc (%)	mask_mAP (%)
val	46.35	66.94	59.75	33.73
testA	46.48	71.22	61.45	40.95
testB	38.54	57.89	54.50	34.87

Evaluation Results: RefCOCO, RefCOCO+, RefCOCOg

\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

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

Dataset	Eval Set	gIoU (%)	mask_mAP (%)
RefCOCO	val	58.24	75.30
	testA	64.08	76.55
	testB	50.70	73.46
RefCOCO+	val	48.61	70.16
	testA	55.03	72.68
	testB	37.34	63.58
RefCOCOg	valG	48.27	72.25
	valU	45.17	68.21
	testU	44.51	67.71

Inference

Engine: Tensor(RT)
\nTest Hardware:

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

The inference is run on the provided unpruned model at FP16 precision. The Jetson devices are running at Max-N configuration for maximum GPU frequency.

\n\n

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Model accuracy is extremely important; learn how you can achieve state of the art accuracy for classification and object detection models using TAO.

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 supporting 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++ Promise and the Explainability, Bias, Safety & Security, and Privacy Subcards. Please report security vulnerabilities or NVIDIA AI Concerns here.

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\n\n### Use Case:
\n1. **Open-Set (or Open-Vocabulary) Object Detection and Segmentation**:\nGrounding DINO can detect objects that were not pre-defined in the training categories — you can give it new category names via text (e.g., “lion”, “bench”, “ear”) and it will localise them in images. \nThis is useful in systems that need flexibility to recognise arbitrary categories without retraining for each new class.\n\n2. **Referring Expression / Text-Guided Object Detection and Segmentation**:\nThe model supports being given a referring expression (“the bottom man with his head up”, “the dog next to the tree”) and then detect/localise the object described.\n\n### Release Date:
\nNGC [11/25/2025]
\n\n## References \n\n- Zhang, H., Li, F., Liu, S., Zhang, L., Su, H., Zhu, J., Ni, L., Shum, H.: DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection.\n- Ziu, S., Zeng, Z., Ren, T., Li, F., Zhang, H., Yang, J., Li, C., Yang, J., Su, H., Zhu, J., Zhang, L.: Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection. \n- Devlin, J., Chang, M., Lee, K., Toutanova, K.: BERT: PRe-training of Deep Bidirectional Transformers for Language Understanding.\n- Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin Transformer: Hierarchical Vision Transformer using Shifted Windows.\n- Tian Z., Shen C., Chen H.: Conditional Convolutions for Instance Segmentation.\n- Liu, C., Ding, H., & Jiang, X.: Gres: Generalized referring expression segmentation.\n\n## Model Architecture \n\n**Architecture Type:** Transformer-Based Segmentation Model
\n**Network Architecture:** Swin-Tiny
\n**Number of Parameters:** 182,668,026 params\n\nThis model is an NVIDIA proprietary open-vocabulary object segmentation model that takes RGB images and either a list of phrases or a referring expression as input, and outputs bounding boxes, segmentation masks, and corresponding labels.\n\nThe model was initialized from a commercial GroundingDINO checkpoint. It employs a [Swin-Tiny backbone](https://arxiv.org/pdf/2103.14030) with a transformer-based segmentation head. All components—including the ReLA module—were pretrained end-to-end on a large-scale mixed corpus of approximately 1.2 million image–expression or image–category name pairs, sourced from Open Images V5, Localized Narratives, COCO, and RefCOCO variants.\n\nFollowing large-scale pretraining, the model was fine-tuned on RefCOCO, RefCOCO+, and RefCOCOg, using roughly 100,000 referring expression–image pairs to improve grounding accuracy and region-level localization.\n\nAll raw images used during training were verified to have commercial licenses, ensuring safe and compliant commercial deployment.\n\n## Input(s):
\n\n**Input Type(s):** \nImage and Text (list of tokenized captions processed via Hugging Face).
\n\n**Input Format(s):** \n- **Image:** Red, Green, Blue (RGB) \n- **Text:** Tokenized string inputs \n- **Resolution:** Minimum 32 × 32 pixels required \n- **Other:** No alpha channel; standard 8-bit color depth
\n\n**Input Parameters:** \n- **Image:** Two-Dimensional (2D) \n- **Text:** One-Dimensional (1D)
\n\n**Other Properties Related to Input:** \n- **`inputs`**: `B × 3 × 544 × 960` — RGB image tensor (Batch Size × Channels × Height × Width) \n- **`input_ids`**: `B × 256` — tokenized text input IDs (Batch Size × Max Token Length) \n- **`attention_mask`**: `B × 256` — attention mask for text tokens \n- **`position_ids`**: `B × 256` — positional encoding indices \n- **`token_type_ids`**: `B × 256` — token type indicators for multi-sentence text \n- **`text_token_mask`**: `B × 256 × 256` — attention map between tokens (Batch Size × Max Token Length × Max Token Length) \n\nBecause **ONNX** and **TensorRT** do not support string inputs directly, the **tokenization step** is performed externally using the **Hugging Face tokenizer**. \nFor deployment details, refer to the [TAO Deploy Repository](https://github.com/NVIDIA/tao_deploy).
\n \n\n## Output(s):
\n\n**Output Type(s):** \nBounding Boxes, Confidence Scores, Segmentation Masks for each detected object in the input image, and a No-Target Indicator.
\n\n**Output Format(s):** \n- **Bounding Boxes:** Predicted 2D boxes in `(cx, cy, w, h)` format \n- **Confidence Scores:** Scalar scores for each predicted object \n- **Segmentation Masks:** 3D masks aligned with the input image dimensions \n- **No-Target Indicator:** Binary logits indicating whether a query corresponds to a valid object or not
\n\n**Output Parameters:** \n- **Bounding Boxes:** Two-Dimensional (2D) \n- **Segmentation Masks:** Three-Dimensional (3D) \n- **Confidence Scores:** One-Dimensional (1D) \n- **No-Target Indicator:** Two-Dimensional (B × 2)
\n\n**Other Properties Related to Output:** \n- **`pred_logits`**: `B × 900` — classification logits for each query (Batch Size × Number of Queries) \n- **`pred_boxes`**: `B × 900 × 4` — predicted bounding boxes in `(cx, cy, w, h)` format \n- **`pred_masks`**: `B × 900 × H × W` — segmentation masks for each predicted object \n- **`no_targets`**: `B × 2` — binary logits indicating positive/negative (object/no-object) classes \n- **`union_logit_masks`**: `B × H × W` — union of predicted object masks across all queries (objectness map) \n\nOur AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA’s hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.\n\n---\n\n**Hardware Optimization Note:** \nThis model is optimized for **NVIDIA GPU-accelerated systems**. \nBy leveraging **CUDA**, **TensorRT**, and other NVIDIA frameworks, it achieves significantly faster training and inference performance compared to CPU-only systems. \n\n\n## Software Integration\n**Runtime Engines:** \n* TAO 5.5.0
\n\n**Supported Hardware Architectures:**
\n* NVIDIA Ampere
\n* NVIDIA Jetson
\n* NVIDIA Hopper
\n* NVIDIA Lovelace
\n* NVIDIA Pascal
\n* NVIDIA Turing
\n* NVIDIA Volta
\n\n**Supported Operating Systems:**
\n* Linux
\n* Linux 4 Tegra
\n\n## Model Versions\n\n- **mask_grounding_dino_swin_tiny_commercial_trainable_v2.0** - Fine-tuned Swin-Tiny Mask Grounding DINO model on Commercial dataset.\n\n\n## Training and Evaluation \n\nThis model was trained using the `mask_grounding_dino` entrypoint in TAO. The training algorithm optimizes the network to minimize the localization and contrastive embedding loss between text and visual features as well as the mask losses between predictions and mask groundtruth.\n\n## Using 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 intended use for these models is detecting objects in a color (RGB) image. The model can be used to detect objects from photos and videos by using appropriate video or image decoding and pre-processing.\n\nThese models are intended for training and fine-tune with the TAO Toolkit and your datasets for object detection. High-fidelity models can be trained with 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-pytorch) and can be used to re-train.\n\nThe models are also intended for easy edge deployment using TensorRT.\n\n### Using the Model with TAO\n\nTo use these models as pretrained weights for transfer learning, use the following snippet as a template for the `model` and `train` components of the experiment spec file. For more information on the experiment spec file, see the [TAO Toolkit User Guide](https://docs.nvidia.com/tao/tao-toolkit/index.html).\n\n```yaml\ntrain:\n pretrained_model_path: /path/to/the/mask_groundingdino.pth\n freeze: [\"backbone.0\", \"bert\"] # freeze the backbone for finetuning\nmodel:\n backbone: swin_tiny_224_1k\n num_feature_levels: 4\n dec_layers: 6\n enc_layers: 6\n num_queries: 900\n dropout_ratio: 0.0\n dim_feedforward: 2048\n log_scale: auto\n class_embed_bias: True\n num_region_queries: 100\n loss_types: ['labels', 'boxes', 'masks', 'rela']\n```\n\n## Training Dataset \n\n## Training Data \n\n### RefCOCO \n**Link:** https://github.com/lichengunc/refer \n**Data Collection Method by dataset:** Human.
\n**Labeling Method by dataset:** Human.
\n**Properties:** The RefCOCO dataset provides referring expressions linking natural-language descriptions to specific object instances in images. \n- Data size: 36,459 samples \n- Expression style: Short (~3.5 words average), allows spatial terms.\n\n**Dataset License(s):** Commercial-use permitted \n\n---\n\n### RefCOCO+ \n**Link:** https://github.com/lichengunc/refer \n**Data Collection Method by dataset:** Human.
\n**Labeling Method by dataset:** Human.
\n**Properties:** RefCOCO+ focuses on appearance descriptors rather than spatial descriptions. \n- Data size: 36,302 samples \n- Expression style: Short (~3.5 words average), no spatial terms allowed.\n\n\n---\n\n### RefCOCOg \n**Link:** https://github.com/lichengunc/refer \n**Data Collection Method by dataset:** Human.
\n**Labeling Method by dataset:** Human.
\n**Properties:** RefCOCOg offers longer, more descriptive referring expressions for visual grounding tasks. \n- Data size: 23,695 samples \n- Expression style: Long, descriptive, more challenging for grounding models. \n \n\n---\n\n### COCO (Subset) \n**Link:** [COCO dataset](https://cocodataset.org/) \n**Data Collection Method by dataset:** Human.
\n**Labeling Method by dataset:** Human-verified annotations.
\n**Properties:** This subset of COCO is used for open-vocabulary grounding with a fixed set of 80 category names. \n- Data size: 36,055 images \n- Object categories: 80 \n- Provides bounding boxes and category labels for open-vocabulary object segmentation. \n\n\n---\n\n### OpenImages V5\n**Link:** [Open Images V7 overview](https://storage.googleapis.com/openimages/web/factsfigures_v7.html)
\n**Data Collection Method by dataset:** Hybrid: Automated web image collection with human verification
\n**Labeling Method by dataset:** Human
\n**Properties:** \nOpenImages provides large-scale annotations for object detection and segmentation, suitable for open-vocabulary grounding. \n- Data size: 662,956 samples (image-category name pairs) \n- Object categories: 350+ \n\n\n---\n\n### Localized Narratives\n**Link:** [Localized Narratives](https://google.github.io/localized-narratives/)
\n**Data Collection Method by dataset:** Human.
\n**Labeling Method by dataset:** Pseudo-labeling.
\n**Properties:** \nProvides dense vision-language annotations for fine-grained grounding of text to image regions. \n- Data size: 459,999 samples \n- Supports detailed region grounding for open-vocabulary segmentation \n\n\nAll datasets were selected for **commercial-use license compatibility**. These sources collectively provide high-quality visual-language supervision for both **targeted object grounding** and **no-target reasoning** tasks.\n\n## Evaluation Data \n\n**Data Collection Method by dataset:**
\nHuman\n\n**Labeling Method by dataset:**
\nHuman\n\n**Properties (Evaluation Sets and General Information):**
\n\nReferring-expression segmentation (RES) requires models to output object masks given natural language expressions. Classic datasets (RefCOCO, RefCOCO+, RefCOCOg) mostly focus on **single-target expressions**, while gRefCOCO supports **single-, multi-, and no-target expressions**, making it suitable for **generalized RES (GRES)** tasks.
\n\n- **gRefCOCO:** \n - **Validation Set:** 16,870 expressions / 8,163 sets / 1,500 images \n - **Test Set A (People):** 18,712 expressions / 6,266 sets / 750 images \n - **Test Set B (Objects):** 14,933 expressions / 4,618 sets / 750 images \n - **Expression Types:** Mixed (single-, multi-, no-target) \n - **Use Case:** Phrase-level grounding; multi-target and no-target scenarios \n\n- **RefCOCO:** \n - **Validation Set:** 3,811 expressions / 1,500 images \n - **Test Set A (People):** 1,975 expressions / 750 images \n - **Test Set B (Objects):** 1,810 expressions / 750 images \n - **Expression Style:** Short (~3.6 words), allows spatial terms \n - **Use Case:** Single-target RES; people-vs-objects split \n\n- **RefCOCO+:** \n - **Validation Set:** 3,805 expressions / 1,500 images \n - **Test Set A (People):** 1,975 expressions / 750 images \n - **Test Set B (Objects):** 1,798 expressions / 750 images \n - **Expression Style:** Short (~3.5 words), no spatial terms; appearance-based \n - **Use Case:** Single-target RES; appearance-based grounding \n\n- **RefCOCOg:** \n - **Validation Set (Google):** 10,234 expressions / 4896 images\n - **Validation Set (UMD):** 5,000 expressions / 5,257 images \n - **Test Set (UMD):** 5,023 expressions / 5,096 images \n - **Expression Style:** Long, descriptive expressions \n - **Use Case:** Single-target RES; per-object split \n\n\n### Methodology and KPI\n\nThe evaluation metrics focus on **generalized Intersection over Union (gIoU)**, **target accuracy (T_acc)**, and **no-target accuracy (N_acc)**: \n\n- **gIoU:** Mean of per-image IoU values across all samples; for true positive no-target samples, IoU = 1, otherwise IoU = 0. \n- **T_acc:** Accuracy on target samples, measuring robustness to no-target generalization. \n- **N_acc:** Accuracy in correctly identifying no-target samples (true positive no-target predictions divided by total no-target samples).\n- **mask_mAP:** Mean Average Precision (mAP) computed across multiple IoU thresholds (0.5 to 0.95); used to evaluate segmentation performance.\n\n#### Evaluation Results: gRefCOCO\n\n| Eval Set | gIoU (%) | T_acc (%) | N_acc (%) | mask_mAP (%) |\n|----------|-----------|-----------|-----------|--------------|\n| val | 46.35 | 66.94 | 59.75 | 33.73 |\n| testA | 46.48 | 71.22 | 61.45 | 40.95 |\n| testB | 38.54 | 57.89 | 54.50 | 34.87 |\n\n---\n\n#### Evaluation Results: RefCOCO, RefCOCO+, RefCOCOg\n\n| Dataset | Eval Set | gIoU (%) | mask_mAP (%) |\n|------------|----------|----------|--------------|\n| RefCOCO | val | 58.24 | 75.30 |\n| | testA | 64.08 | 76.55 |\n| | testB | 50.70 | 73.46 |\n| RefCOCO+ | val | 48.61 | 70.16 |\n| | testA | 55.03 | 72.68 |\n| | testB | 37.34 | 63.58 |\n| RefCOCOg | valG | 48.27 | 72.25 |\n| | valU | 45.17 | 68.21 |\n| | testU | 44.51 | 67.71 |\n\n\n\n## Inference \n\n**Engine:** Tensor(RT)
\n**Test Hardware:**
\n- A2\n- A30\n- DGX H100\n- DGX A100\n- Jetson AGX Xavier\n- L4\n- L40\n- JAO 64GB\n- Orin Nano 8GB\n- Orin NX\n\nThe inference is run on the provided unpruned model at FP16 precision. The Jetson devices are running at Max-N configuration for maximum GPU frequency.\n\n\n\n\n\n## Technical Blogs \n\n- [Train like a ‘pro’ without being an AI expert using TAO AutoML](https://developer.nvidia.com/blog/training-like-an-ai-pro-using-tao-automl/)\n- [Create Custom AI models using NVIDIA TAO Toolkit with Azure Machine Learning ](https://developer.nvidia.com/blog/creating-custom-ai-models-using-nvidia-tao-toolkit-with-azure-machine-learning/)\n- [Developing and Deploying AI-powered Robots with NVIDIA Isaac Sim and NVIDIA TAO](https://developer.nvidia.com/blog/developing-and-deploying-ai-powered-robots-with-nvidia-isaac-sim-and-nvidia-tao/)\n- Learn endless ways to adapt and supercharge your AI workflows with TAO - [Whitepaper](https://developer.nvidia.com/tao-toolkit-usecases-whitepaper/1-introduction)\n- [Customize Action Recognition with TAO and deploy with DeepStream](https://developer.nvidia.com/blog/developing-and-deploying-your-custom-action-recognition-application-without-any-ai-expertise-using-tao-and-deepstream/)\n- Read the two-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 a [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\n## Suggested Reading \n\n- More information on TAO Toolkit and pre-trained models can be found at the [NVIDIA Developer Zone](https://developer.nvidia.com/tao-toolkit).\n- Refer to the [TAO documentation](https://docs.nvidia.com/tao/tao-toolkit/index.html).\n- Read the [TAO Toolkit Quick Start Guide](https://docs.nvidia.com/tao/tao-toolkit/text/tao_quick_start_guide.html) 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 the [TAO Toolkit Developer Forums](https://forums.developer.nvidia.com/c/accelerated-computing/intelligent-video-analytics/tao-toolkit/17).\n- Deploy your models for video analytics application using the [DeepStream SDK](https://developer.nvidia.com/deepstream-sdk).\n- Deploy your models in [Riva](https://developer.nvidia.com/riva) for ConvAI use case.\n\n## Ethical Considerations \n\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 supporting 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++ Promise and the Explainability, Bias, Safety & Security, and Privacy Subcards. Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).","displayName","Mask Grounding DINO CC",["D",1773116823098],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/tao/models/mask_grounding_dino","rawImageUrl","https://dz112fgwz7ogh.cloudfront.net/logos/Nvidia-Centric-TAO.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms",{"_246":247,"_248":249,"_250":251,"_252":20},"isMultinodeEnabled","isMultiArchitecture","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: | Not Applicable |\n| Measures taken to mitigate against unwanted bias: | Not Applicable.|","explainability","| Field | Response |\n| -- | -- |\n| Intended Applications & Domains: | Open Set Segmenting (Text) |\n| Model Type: | Instance Segmentation with arbitrary object categories or reference descriptions |\n| Intended Users: | This model is intended for developers working in smart spaces, retail, and industrial applications. |\n| Output: | Bounding Boxes, Confidence Scores, and Segmentation masks |\n| Describe how the model works: | This model predicts bounding boxes and segmentation masks for each object in the image. It can detect objects specified via open-vocabulary text input or referring expressions, allowing flexible object recognition. |\n| Name the adversely impacted groups this has been tested to deliver comparable outcomes regardless of:\t| Not Applicable |\n| Technical Limitations: | The model may have difficulties in non-Flickr style data like medical, satellite, and industrial data. |\n| Verified to have met prescribed NVIDIA standards: | Yes |\n| Performance Metrics: | generalized Intersection over Union (gIoU), Target Accuracy (T_acc), No-Target Accuracy (N_acc), Mean Average Precision (mask_mAP) |\n| Potential Known Risks: | Model may mis-localize or over-segment objects when expression/categories are ambiguous or outside the training distribution. |\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 personal data? | No |\n| Personal data used to create this model? | No |\n| Was consent obtained for any personal data used? | Not Applicable |\n| How often is dataset reviewed? | \tBefore Release |\n| Was data from user interactions with the AI model (e.g. user input and prompts) used to train the model? | No |\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? | No |\n| Applicable NVIDIA Privacy Policy\t| [NVIDIA Privacy Policy](https://www.nvidia.com/en-us/about-nvidia/privacy-policy/)|","safetyAndSecurity","| Field | Response |\n| -- | -- |\n| Model Application Field(s): | Segmentation (General Purpose) |\n| Describe the life critical impact (if present). | Not Applicable |\n| Use Case Restrictions: | Abide by [NVIDIA Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/) |\n| 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. |","featureLabels",[],"displayLabels",[214,215,216,217,218,219,220,221],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata","computedResourceType",{"_176":242,"_223":243,"_224":244,"_226":227},{"_176":240,"_223":240,"_224":241,"_226":227},{"_176":238,"_223":238,"_224":239,"_226":227},{"_176":236,"_223":236,"_224":237,"_226":227},{"_176":234,"_223":234,"_224":235,"_226":227},{"_176":232,"_223":232,"_224":233,"_226":227},{"_176":230,"_223":230,"_224":231,"_226":227},{"_176":222,"_223":222,"_224":225,"_226":227},"TAO Toolkit","value","filter",{"_228":229,"_223":222},"category","label","field","framework","TAO",{"_228":227,"_223":230},"NSPECT-Y7JB-DQEW",{"_228":227,"_223":232},"NSPECT-FY5H-V6CO",{"_228":227,"_223":234},"False",{"_228":227,"_223":236},"Deep Learning",{"_228":227,"_223":238},"CV",{"_228":227,"_223":240},"Computer Vision","soln_computer_vision",{"_228":245,"_223":243},"technology","governingTerms","

Governing Terms: Use of this model is governed by the NVIDIA Open Model License.

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They are capable of tracking your browser across other sites and building up a profile of your interests. This may impact the content and messages you see on other websites you visit. If you do not allow these cookies you may not be able to use or see these sharing tools.","alwaysActive","order",11,"cookies",[],"hosts",[],"C0004","Advertising Cookies","These cookies record your visit to our websites, the pages you have visited and the links you have followed to influence the advertisements that you see on other websites. These cookies and the information they collect may be managed by other companies, including our advertising partners, and may be used to build a profile of your interests and show you relevant advertising on other sites. 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users logged in across NVIDIA domains.","api.ngc.nvidia.com","This cookie is managed by AWS and is used for load balancing.","bm_so","This cookie name is used to load the site as quickly as possible and to recognize bots using Akamai.","OptanonAlertBoxClosed","docs.ngc.nvidia.com","This cookie is set by websites using certain versions of the cookie law compliance solution from OneTrust. It is set after visitors have seen a cookie information notice and in some cases only when they actively close the notice down. It enables the website not to show the message more than once to a user. The cookie has a normal lifespan of one year and contains no personal information.","This cookie is associated with Akamai and is used to differentiate between traffic from humans and bots.","omniverse.nvidia.com","The __cf_bm cookie is a cookie necessary to support Cloudflare Bot Management, currently in private beta. As part of our bot management service, this cookie helps manage incoming traffic that matches criteria associated with bots.\nThis is a CloudFoundry cookie","OptanonConsent","nvid.nvidia.com","This cookie is set by the cookie compliance solution from OneTrust. It stores information about the categories of cookies the site uses and whether visitors have given or withdrawn consent for the use of each category. This enables site owners to prevent cookies in each category from being set in the users browser, when consent is not given. The cookie has a normal lifespan of one year, so that returning visitors to the site will have their preferences remembered. It contains no information that can identify the site visitor.","_rdt_em","This cookie name is associated with Reddit which is used to manage session state and user interactions.","events.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","sessionid","This is a very generic cookie name that may have different purposes on different sites, but generally it will be some kind of anonymous session identifier.","_csrf","This cookie is used to prevent Cross-Site Request Forgery (CSRF) attacks, ensuring that each request made to the server is from the user's browser and is intentional.","enterprise-support.nvidia.com","renderCtx","academicgrants.nvidia.com","Used to store site parameters in the session for reuse across requests by a single client for functionality and performance reasons.","This cookie is managed by AWS and is used for load balancing.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","nvidiaCart","This domain is owned by NVIDIA Corporation which provides information about the company, its products, solutions, industries, news, events, and career opportunities.","nv_co","__uxq412__ses.88c1","AWS ELB application load balancerUsed to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","learn.learn.nvidia.com","pctrk","Used to count page views by unauthenticated users against license usage.","bm_s","This cookie is associated with Akamai. 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