nvidia/C-RADIOv4-1D-H

Model Overview

Description

This model performs visual feature extraction. Unlike standard Vision Transformers that produce a fixed 2D grid of patch features, RADIO1D compresses an input image into a compact, variable-length 1D sequence of tokens. The number of output tokens (from 1 up to 256) can be selected by the user at inference time, providing a continuous accuracy/efficiency trade-off. For example, an image can be summarized into a single token for retrieval, or expanded to 256 tokens for fine-grained tasks such as OCR.

RADIO1D was produced by fine-tuning C-RADIOv4-H using multi-teacher agglomerative distillation from:

• SigLIP2-g
• DINOv3-7B
• SAM3

The encoder integrates a learnable Patch Merging block (4× sequence-length reduction, 2× channel expansion) part-way through the network for efficiency, and a lightweight Vision Transformer decoder is used only during training to project the 1D tokens back into a 2D-compatible grid for teacher alignment. At inference, only the encoder runs.

This model is ready for commercial or 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. The variable-length 1D token output makes RADIO1D especially well suited to:

• Integration into a Vision-Language Model with a user-tunable token budget per image (trade off accuracy vs. time-to-first-token).
• Image-level understanding (image classification, scene summarization, image curation).
• Composition-aware image-to-image retrieval (object presence and spatial arrangement).
• Dense processing (semantic segmentation) when a decoder is used.

Release Date

Hugging Face: 07/01/2026 via RADIO Collection of Models.

References

• RADIO1D paper: G. Heinrich, M. Ranzinger, C. McCarthy et al., "RADIO1D: Elastic Representations for Condensed Vision Modeling," ICML 2026.
• AM-RADIO (arXiv:2312.06709)
• RADIOv2.5 (arXiv:2412.07679)
• PHI-S / RADIO scaling (arXiv:2410.01680)
• C-RADIO (arXiv:2502.16025)
• C-RADIOv4 (arXiv:2601.17237)

Model Architecture

Architecture Type: Neural Network

Network Architecture: Vision Transformer with encoder-decoder for elastic 1D token generation

Number of model parameters: ~1.14B (encoder, used at inference); ~314M additional decoder parameters used only during training

The RADIO1D-H encoder is built from a ViT-H/16 backbone. Image patches (16×16 pixels) are flattened into a 1D sequence and processed by 24 transformer blocks at embedding dimension 1280, followed by a learnable Patch Merging block that groups 2×2 neighboring tokens (reducing sequence length by 4× and expanding the channel dimension by ρ=2 to 2560), followed by 8 further transformer blocks at the wider dimension. During training, a length ℓ is sampled stochastically from a triangular distribution p(x)=2−2x, and only the first ℓ encoder tokens are retained (a form of nested dropout). Earlier tokens are therefore encouraged to encode global, high-level semantics while later tokens specialize in finer details.

A small ViT decoder (~314M parameters, 6 blocks with one Patch Splitting upscale) is used only during training. It receives duplicated learnable query tokens equal in number to the original patch grid plus the encoder's ℓ tokens as additional register tokens, and cross-attention reconstructs a 2D-compatible feature grid that is aligned to the teacher representations.

At inference, only the encoder is used and the user specifies the desired number of output tokens.

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 2048×2048 in increments of 16 pixels. Training used a mix of low-resolution images (128, 192, 224, 256, 384, 432 px) and high-resolution images (512, 768, 1024, 1152 px).

Output

Output Type(s): Embeddings

Output Format: Tensor

Output Parameters: One Dimensional (1D) — variable-length sequence of tokens

Other Properties Related to Output: The encoder returns a sequence of prefix tokens (CLS + register tokens) followed by ℓ global 1D tokens, where ℓ is selected by the caller at inference (typical values: 1, 8, 32, 64, 128, 192, 224, 256). Tokens are ordered hierarchically: token 0 encodes the strongest global summary (e.g., 85.0% k-NN Top-1 on ImageNet-1k with a single token), and later tokens add progressively finer detail. A downstream model is required to leverage the image features. 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):

• PyTorch

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

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)

• C-RADIOv4-1D-H (~1.14B parameters; based on a...