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Governing Terms: Use of this model is governed by the NVIDIA Open Model License. Additional Information: Apache 2.0.

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SigLIP 2

Model Overview

Description:

SigLIP 2 is a vision-language encoder that produces joint image and text embeddings for cross-modal retrieval. It supports multiple variants (e.g., SO400M, giant) with embedding dimensions from 768 to 1536 and image resolutions such as 224×224, 256×256, 384×384, and 512×512 depending on variant. The model is suitable for text-to-image and image-to-image search, object search, and re-identification in applications such as transportation, warehouse operations, and other industrial contexts. This model is ready for commercial use.

\n
\n

License/Terms of Use

Governing Terms: Use of this model is governed by the NVIDIA Open Model License. Additional Information: Apache 2.0.

Deployment Geography:

Global

Use Case:

Object search, re-identification, and cross-modal retrieval (text-to-image and image-to-image) in applications such as transportation, warehouse operations, and other industrial contexts. Users include developers and integrators building vision-language retrieval systems for these domains.

Release Date:

NGC 03/09/2026 via URL

References(s):

SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features, 2025.\n

Model Architecture:

Architecture Type: Vision-Language Transformer (SigLIP 2)

Network Architecture: SigLIP 2 (e.g., siglip2-so400m-patch16-256, siglip2-so400m-patch16-384, siglip2-giant-opt-patch16-384, or variant as specified on NGC).

This model was developed based on: Google SigLIP 2 (vision and text encoders).

Number of model parameters: Varies by variant (e.g., ~4×10^8 for SO400M, ~1×10^9 for giant).

Input(s):

Input Type(s): Image, Text

Input Format(s):

Image: Red, Green, Blue (RGB)
Text: String (tokenized via model processor/tokenizer)

Input Parameters:

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

Other Properties Related to Input: Image: Resolution depends on variant (e.g., 256×256, 384×384, 512×512); RGB, 8-bit; pre-processing: resize and normalize per HuggingFace processor. Some variants support dynamic resolution (NaFlex). Text: Context length and vocabulary per model processor; pre-processing: tokenization.

Output(s)

Output Type(s): Embedding vectors (floating-point)

Output Format(s): Real-valued vectors (L2-normalized)

Output Parameters: One-Dimensional (1D) embedding vectors

Other Properties Related to Output: Embedding dimension varies by variant (e.g., 1152 for SO400M, 1536 for giant); L2-normalized for cosine similarity; no character or resolution limits.

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.

Software Integration:

Runtime Engine(s):

TAO - minimally compatible versions as specified on NGC
PyTorch / HuggingFace Transformers

Supported Hardware Microarchitecture Compatibility:

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

Preferred/Supported Operating System(s):

Linux

The integration of foundation and fine-tuned models into AI systems requires additional testing using use-case-specific data to ensure safe and effective deployment. Following the V-model methodology, iterative testing and validation at both unit and system levels are essential to mitigate risks, meet technical and functional requirements, and ensure compliance with safety and ethical standards before deployment.

This AI model can be embedded as an Application Programming Interface (API) call into the software environment described above.

Model Version(s):

Trainable v1.0 and deployable v1.0 are available on NGC (e.g., SigLIP 2 SO400M).

Training, Testing, and Evaluation Datasets:

Training Dataset

Data Modality:

Image
Text

Image Training Data Size:

Less than a Million Images (for fine-tuning workflows; base model training may use larger sets). TAO fine-tuning training set sizes: 50,019 train images (6,668 persons).

Text Training Data Size:

Less than a Billion Tokens

Data Collection Method by dataset:

Hybrid: Human, Automated

Labeling Method by dataset:

Hybrid: Human, Automated

Properties: Training data comprises image-text pairs: images (RGB) and associated text (captions or attribute-based descriptions). Content includes person and object imagery for retrieval and re-identification. Data sources used in TAO fine-tuning may include internal and licensed datasets (e.g., person attribute search); see the CLIP CLI notebook and experiment specs for exact sources and counts.

Testing Dataset:

Data Collection Method by dataset:

Hybrid: Human, Automated

Labeling Method by dataset:

Human

Properties: Test sets include image-text pairs with same modalities and nature as training (images and captions/attributes); used for retrieval and re-identification metrics.

Evaluation Dataset:

Data Collection Method by dataset:

Human

Labeling Method by dataset:

Human

Properties: Evaluation uses benchmark datasets for retrieval and person re-identification (e.g., person attribute search benchmarks); image and text modalities; descriptive and attribute-based captions.

Benchmark Score: Retrieval metrics (e.g., Recall@K, mAP) on person re-identification and text-to-image retrieval benchmarks.

Inference

Acceleration Engine: TensorRT (SigLIP 2 vision encoder), PyTorch / Hugging Face Transformers

Test Hardware:

NVIDIA datacenter GPUs (e.g., L4, L40, L40S, A100, H100, H200, RTX PRO 6000 Blackwell Server Edition, B200, GB200)
Jetson AGX Orin, Jetson AGX Thor T5000, Jetson IGX Thor T7000 (Stargazer), DGX Spark
Minimum hardware as required by your TAO / PyTorch / TensorRT deployment

Datacenter GPU results below are for the SigLIP 2 SO400M patch16-256 vision encoder (google/siglip2-so400m-patch16-256). Measurements used TensorRT FP16 and trtexec. Throughput is inference-only; end-to-end latency with decoding, pre/post-processing, or full application pipelines may differ.

Environment (dGPU)

\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\">

Component	Version
TensorRT	10.14.1.48
CUDA	13.1 (V13.1.115)
cuDNN	9.17.1
Driver	580.105.08
Container	gitlab-master_26.01_py3_stage_252181976
OS	Ubuntu 24.04

Environment (edge)

Jetson IGX Thor T7000 (Stargazer) — flashed with IGX-r38v2.0.11; TensorRT 10.13.3.9; CUDA 13.0; cuDNN 9.12.0.46; power mode 120W; jetson_clocks applied.

Jetson AGX Thor T5000 — flashed with JP7.1 b148; TensorRT 10.13.3.9; CUDA 13.0; cuDNN 9.12.0.46; power mode 120W; jetson_clocks applied.

DGX Spark — FastOS OTA2 Mainline Release 1.120.36; driver 580.126.09 / 590.48.01; CUDA 13.0 / 13.1; cuDNN 9.12 / 9.17; TensorRT 10.13.2 / 10.14.1.48.

Jetson AGX Orin — flashed with JP7.2 b19; TensorRT 10.13.3.9; CUDA 12.9; cuDNN 9.12.0.46; power mode MAXN.

Model configuration and TensorRT build

Hugging Face model id: google/siglip2-so400m-patch16-256. Input: pixel_values shape [batch_size, 3, 256, 256]. Output: image_embeds shape [batch_size, 1152]. ONNX opset 17.

TensorRT conversion command (from export log):

trtexec --onnx=onnx_exports/siglip2_v1.0.onnx --saveEngine=siglip2_v1.0.engine --shapes=pixel_values:1x3x256x256 --fp16

Vision encoder throughput (FP16)

\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\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=1	BS=2	BS=4	BS=8	BS=16	BS=32	BS=64	BS=128
NVIDIA L4	72	58	37	22	11	5	2	1
NVIDIA L40	98	97	87	53	26	13	6	3
NVIDIA L40S	129	126	107	67	40	18	9	4
NVIDIA A100-SXM4-80GB	123	117	103	66	40	21	10	5
NVIDIA H100 NVL	200	190	169	117	68	35	18	9
NVIDIA H100 80GB HBM3	221	210	191	141	92	49	26	13
NVIDIA H200 141GB HBM3	227	214	192	142	93	49	26	13
NVIDIA RTX PRO 6000 Blackwell Server Edition	101	100	84	81	65	32	16	8
NVIDIA B200	267	261	254	222	160	85	46	24
NVIDIA GB200	259	256	245	210	157	90	48	26
Jetson AGX Orin	50	35	20	11	5	3	1	1
Jetson AGX Thor T5000	98	90	66	40	20	10	5	2
Jetson IGX Thor T7000 (Stargazer)	95	—	64	38	19	9	5	2
DGX Spark	92	69	47	27	12	6	3	1

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.

Please make sure you have proper rights and permissions for all input image and video content; if image or video includes people, personal health information, or intellectual property, the image or video generated will not blur or maintain proportions of image subjects included.

For more detailed information on ethical considerations for this model, please see the Model Card++ Bias, Explainability, Safety & Security, and Privacy Subcards.

Please report model quality, risk, security vulnerabilities or NVIDIA AI Concerns here.

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\n\n### License/Terms of Use\n\n**Governing Terms**: Use of this model is governed by the [NVIDIA Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/). **Additional Information**: [Apache 2.0](https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/apache-2.0.md).
\n\n### Deployment Geography:\nGlobal
\n\n### Use Case:
\nObject search, re-identification, and cross-modal retrieval (text-to-image and image-to-image) in applications such as transportation, warehouse operations, and other industrial contexts. Users include developers and integrators building vision-language retrieval systems for these domains.
\n\n### Release Date:
\nNGC 03/09/2026 via [URL](https://catalog.ngc.nvidia.com/orgs/nvstaging/teams/tao/models/siglip_v2)
\n\n## References(s):\n[SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features](https://arxiv.org/abs/2502.14786), 2025.\n
\n\n## Model Architecture:\n**Architecture Type:** Vision-Language Transformer (SigLIP 2)
\n\n**Network Architecture:** SigLIP 2 (e.g., siglip2-so400m-patch16-256, siglip2-so400m-patch16-384, siglip2-giant-opt-patch16-384, or variant as specified on NGC).
\n\n**This model was developed based on:** Google SigLIP 2 (vision and text encoders).
\n\n**Number of model parameters:** Varies by variant (e.g., ~4×10^8 for SO400M, ~1×10^9 for giant).
\n\n## Input(s):
\n**Input Type(s):** Image, Text
\n\n**Input Format(s):**
\n- Image: Red, Green, Blue (RGB)
\n- Text: String (tokenized via model processor/tokenizer)
\n\n**Input Parameters:**
\n- Image: Two-Dimensional (2D)
\n- Text: One-Dimensional (1D)
\n\n**Other Properties Related to Input:** Image: Resolution depends on variant (e.g., 256×256, 384×384, 512×512); RGB, 8-bit; pre-processing: resize and normalize per HuggingFace processor. Some variants support dynamic resolution (NaFlex). Text: Context length and vocabulary per model processor; pre-processing: tokenization.
\n\n## Output(s)\n\n**Output Type(s):** Embedding vectors (floating-point)
\n\n**Output Format(s):** Real-valued vectors (L2-normalized)
\n\n**Output Parameters:** One-Dimensional (1D) embedding vectors
\n\n**Other Properties Related to Output:** Embedding dimension varies by variant (e.g., 1152 for SO400M, 1536 for giant); L2-normalized for cosine similarity; no character or resolution limits.
\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## Software Integration:\n**Runtime Engine(s):** \n* TAO - minimally compatible versions as specified on NGC
\n* PyTorch / HuggingFace Transformers
\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 System(s):**\n* Linux
\n\nThe integration of foundation and fine-tuned models into AI systems requires additional testing using use-case-specific data to ensure safe and effective deployment. Following the V-model methodology, iterative testing and validation at both unit and system levels are essential to mitigate risks, meet technical and functional requirements, and ensure compliance with safety and ethical standards before deployment.
\n\nThis AI model can be embedded as an Application Programming Interface (API) call into the software environment described above.
\n\n## Model Version(s):\n\nTrainable v1.0 and deployable v1.0 are available on NGC (e.g., SigLIP 2 SO400M).
\n\n## Training, Testing, and Evaluation Datasets:\n\n### Training Dataset\n\n**Data Modality:**
\n* Image
\n* Text
\n\n**Image Training Data Size:**
\n* Less than a Million Images (for fine-tuning workflows; base model training may use larger sets). TAO fine-tuning training set sizes: 50,019 train images (6,668 persons).
\n\n**Text Training Data Size:**
\n* Less than a Billion Tokens
\n\n**Data Collection Method by dataset:**
\n* Hybrid: Human, Automated
\n\n**Labeling Method by dataset:**
\n* Hybrid: Human, Automated
\n\n**Properties:** Training data comprises image-text pairs: images (RGB) and associated text (captions or attribute-based descriptions). Content includes person and object imagery for retrieval and re-identification. Data sources used in TAO fine-tuning may include internal and licensed datasets (e.g., person attribute search); see the [CLIP CLI notebook](../../../resource/notebooks/tao_launcher_starter_kit/clip/clip.ipynb) and [experiment specs](../../../resource/notebooks/tao_launcher_starter_kit/clip/specs/) for exact sources and counts.
\n\n### Testing Dataset:\n\n**Data Collection Method by dataset:**
\n* Hybrid: Human, Automated
\n\n**Labeling Method by dataset:**
\n* Human
\n\n**Properties:** Test sets include image-text pairs with same modalities and nature as training (images and captions/attributes); used for retrieval and re-identification metrics.
\n\n### Evaluation Dataset:\n\n**Data Collection Method by dataset:**
\n* Human
\n\n**Labeling Method by dataset:**
\n* Human
\n\n**Properties:** Evaluation uses benchmark datasets for retrieval and person re-identification (e.g., person attribute search benchmarks); image and text modalities; descriptive and attribute-based captions.
\n\n**Benchmark Score:** Retrieval metrics (e.g., Recall@K, mAP) on person re-identification and text-to-image retrieval benchmarks.
\n\n## Inference \n\n**Acceleration Engine:** [TensorRT](https://developer.nvidia.com/tensorrt) (SigLIP 2 vision encoder), PyTorch / Hugging Face Transformers
\n\n**Test Hardware:**
\n* NVIDIA datacenter GPUs (e.g., L4, L40, L40S, A100, H100, H200, RTX PRO 6000 Blackwell Server Edition, B200, GB200)
\n* Jetson AGX Orin, Jetson AGX Thor T5000, Jetson IGX Thor T7000 (Stargazer), DGX Spark
\n* Minimum hardware as required by your TAO / PyTorch / TensorRT deployment
\n\nDatacenter GPU results below are for the **SigLIP 2 SO400M patch16-256 vision encoder** (`google/siglip2-so400m-patch16-256`). Measurements used TensorRT FP16 and [`trtexec`](https://github.com/NVIDIA/TensorRT/tree/main/samples/trtexec). Throughput is **inference-only**; end-to-end latency with decoding, pre/post-processing, or full application pipelines may differ.\n\n### Environment (dGPU)\n\n| Component | Version |\n| --- | --- |\n| TensorRT | 10.14.1.48 |\n| CUDA | 13.1 (V13.1.115) |\n| cuDNN | 9.17.1 |\n| Driver | 580.105.08 |\n| Container | gitlab-master_26.01_py3_stage_252181976 |\n| OS | Ubuntu 24.04 |\n\n### Environment (edge)\n\n**Jetson IGX Thor T7000 (Stargazer)** — flashed with IGX-r38v2.0.11; TensorRT 10.13.3.9; CUDA 13.0; cuDNN 9.12.0.46; power mode 120W; `jetson_clocks` applied.\n\n**Jetson AGX Thor T5000** — flashed with JP7.1 b148; TensorRT 10.13.3.9; CUDA 13.0; cuDNN 9.12.0.46; power mode 120W; `jetson_clocks` applied.\n\n**DGX Spark** — FastOS OTA2 Mainline Release 1.120.36; driver 580.126.09 / 590.48.01; CUDA 13.0 / 13.1; cuDNN 9.12 / 9.17; TensorRT 10.13.2 / 10.14.1.48.\n\n**Jetson AGX Orin** — flashed with JP7.2 b19; TensorRT 10.13.3.9; CUDA 12.9; cuDNN 9.12.0.46; power mode MAXN.\n\n### Model configuration and TensorRT build\n\nHugging Face model id: `google/siglip2-so400m-patch16-256`. Input: `pixel_values` shape `[batch_size, 3, 256, 256]`. Output: `image_embeds` shape `[batch_size, 1152]`. ONNX opset 17.\n\nTensorRT conversion command (from export log):\n\n`trtexec --onnx=onnx_exports/siglip2_v1.0.onnx --saveEngine=siglip2_v1.0.engine --shapes=pixel_values:1x3x256x256 --fp16`\n\n### Vision encoder throughput (FP16)\n\n| Platform | BS=1 | BS=2 | BS=4 | BS=8 | BS=16 | BS=32 | BS=64 | BS=128 |\n| --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: |\n| NVIDIA L4 | 72 | 58 | 37 | 22 | 11 | 5 | 2 | 1 |\n| NVIDIA L40 | 98 | 97 | 87 | 53 | 26 | 13 | 6 | 3 |\n| NVIDIA L40S | 129 | 126 | 107 | 67 | 40 | 18 | 9 | 4 |\n| NVIDIA A100-SXM4-80GB | 123 | 117 | 103 | 66 | 40 | 21 | 10 | 5 |\n| NVIDIA H100 NVL | 200 | 190 | 169 | 117 | 68 | 35 | 18 | 9 |\n| NVIDIA H100 80GB HBM3 | 221 | 210 | 191 | 141 | 92 | 49 | 26 | 13 |\n| NVIDIA H200 141GB HBM3 | 227 | 214 | 192 | 142 | 93 | 49 | 26 | 13 |\n| NVIDIA RTX PRO 6000 Blackwell Server Edition | 101 | 100 | 84 | 81 | 65 | 32 | 16 | 8 |\n| NVIDIA B200 | 267 | 261 | 254 | 222 | 160 | 85 | 46 | 24 |\n| NVIDIA GB200 | 259 | 256 | 245 | 210 | 157 | 90 | 48 | 26 |\n| Jetson AGX Orin | 50 | 35 | 20 | 11 | 5 | 3 | 1 | 1 |\n| Jetson AGX Thor T5000 | 98 | 90 | 66 | 40 | 20 | 10 | 5 | 2 |\n| Jetson IGX Thor T7000 (Stargazer) | 95 | — | 64 | 38 | 19 | 9 | 5 | 2 |\n| DGX Spark | 92 | 69 | 47 | 27 | 12 | 6 | 3 | 1 |\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.
\n\nPlease make sure you have proper rights and permissions for all input image and video content; if image or video includes people, personal health information, or intellectual property, the image or video generated will not blur or maintain proportions of image subjects included.
\n\nFor more detailed information on ethical considerations for this model, please see the [Model Card++ Bias, Explainability, Safety & Security, and Privacy Subcards](https://catalog.ngc.nvidia.com/orgs/nvstaging/teams/tao/models/siglip_v2).
\n\nPlease report model quality, risk, security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).
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