C-RADIOv4-1D-H

• 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 (arXiv:2607.03624.
• 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-ca