Model Overview
Description
This model performs visual feature extraction.
For instance, RADIO generates image embeddings that can be used by a downstream model to classify images.
C-RADIOv3 models are available in multiple sizes:
- Base (90M parameters).
- Large (320M parameters).
- Huge (653M parameters). (In training)
- Gigantic (1.1B parameters).
C-RADIOv3 was trained for 1M steps (400k more steps than v1), using inverse frequency sampling for data balancing, and
PHI Standardization for teacher distribution balancing. As well as new techniques for summary distribution matching, and domain generalization.
This model is ready for commercial/non-commercial use.
License/Terms of Use
GOVERNING TERMS: Use of this model is governed by the
NVIDIA Open Model License Agreement.
Deployment Geography
Global.
Use Case
The embeddings generated by this model are expected to be used by a downstream application.
For example:
- Image-level understanding (image classification, curation, etc.).
- Dense processing (semantic segmentation, depth estimation, etc.).
- Integration into a Vision-Language Model.
Release Date
Huggingface: 03/26/2025 via
RADIO Collection of Models.
References
Model Architecture
Architecture Type: Neural Network
Network Architecture: Vision Transformer
Input
Input Type(s): Image
Input Format(s): Red, Green, Blue (RGB)
Input Parameters: Two Dimensional (2D)
Other Properties Related to Input: Image resolutions up to 2048x2028 in increments of 16 pixels
Output
Output Type(s): Embeddings
Output Format: Tensor
Output Parameters: 2D
Other Properties Related to Output: Downstream model required to leverage image features
Usage:
RADIO will return a tuple with two tensors.
The summary is similar to the cls_token in ViT and is meant to represent the general concept of the entire image.
It has shape (B,C) with B being the batch dimension, and C being some number of channels.
The spatial_features represent more localized content which should be suitable for dense tasks such as semantic segmentation, or for integration into an LLM.
1import torch
2from PIL import Image
3from transformers import AutoModel, CLIPImageProcessor
4
5hf_repo = "nvidia/C-RADIOv3-L"
6
7image_processor = CLIPImageProcessor.from_pretrained(hf_repo)
8model = AutoModel.from_pretrained(hf_repo, trust_remote_code=True)
9model.eval().cuda()
10
11image = Image.open('./assets/radio.png').convert('RGB')
12pixel_values = image_processor(images=image, return_tensors='pt', do_resize=True).pixel_values
13pixel_values = pixel_values.cuda()
14
15summary, features = model(pixel_values)
Spatial features have shape (B,T,D) with T being the flattened spatial tokens, and D being the channels for spatial features. Note that C!=D in general.
Converting to a spatial tensor format can be done using the downsampling size of the model, combined with the input tensor shape. For RADIO, the patch size is 16.
1from einops import rearrange
2spatial_features = rearrange(spatial_features, 'b (h w) d -> b d h w', h=x.shape[-2] // patch_size, w=x.shape[-1] // patch_size)
The resulting tensor will have shape (B,D,H,W), as is typically seen with computer vision models.
Software Integration
Runtime Engine(s):
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
- Linux 4 Tegra
- QNX
- Windows
Model Version(s)
- C-RADIOv3-B (90M parameters).
- C-RADIOv3-L (320M parameters).
- C-RADIOv3-H (653M parameters).
- C-RADIOv3-g (1.2B parameters).
Links:
Training and Evaluation Datasets
Training Dataset
NV-CC-Img-Text-Dataset
Data Collection Method by dataset
Labeling Method by dataset
- Not Applicable (no labels are needed)
Properties
Evaluation Dataset
Data Collection Method by dataset
Labeling Method by dataset
Properties: This dataset spans 1000 object classes and contains 1,281,167 training images, 50,000 validation images and 100,000 test images.
Inference
Engine: PyTorch
Test Hardware: A100
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 below.
Please report security vulnerabilities or NVIDIA AI Concerns
here.
Bias
| Field | Response |
|---|
| Participation considerations from adversely impacted groups protected classes in model design and testing: | None |
| Measures taken to mitigate against unwanted bias: | None |
Explainability
| Field | Response |
|---|
| Intended Application & Domain: | Visual Feature Extraction |
| Model Type: | Vision Transformer |
| Intended Users: | Developers of downstream vision applications |
| Output: | Image embeddings |
| Describe how the model works: | The model takes an image as input, processes the image through multiple transformer blocks, and outputs summary and patch embeddings. |
| Name the adversely impacted groups this has been tested to deliver comparable outcomes regardless of: | Not Applicable |
| Technical Limitations: | This model generates image embeddings that can be used by a downstream model to, for example, classify images. The downstream model must be trained to leverage the visual embeddings. |
| Verified to have met prescribed NVIDIA quality standards: | Yes |
| Performance Metrics: | Image classification accuracy, semantic segmentation mean-over-intersection. |
| Potential Known Risks: | This model is only tested on input resolutions ranging from 256 to 2048, in increments of 16 pixels. Additionally, the generated embeddings might fail to disambiguate differences that appear evident to humans (e.g. two images showing different breeds of dogs might in fact produce very similar embeddings). Domain-specific evaluation is required for the target application. |
| Licensing: | NVIDIA Open Model License |
Privacy
| Field | Response |
|---|
| Generatable or reverse engineerable personal data? | None |
| Personal data used to create this model? | None |
| How often is dataset reviewed? | Before Every Release |
| Is there provenance for all datasets used in training? | Yes |
| Does data labeling (annotation, metadata) comply with privacy laws? | Yes |
| Is data compliant with data subject requests for data correction or removal, if such a request was made? | Yes |
Safety
| Field | Response |
|---|
| Model Application(s): | Generation of visual embeddings |
| Describe the life critical impact (if present). | Not Applicable |
| Use Case Restrictions: | Abide by NVIDIA Open Model License Agreement |
| 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. |
