C RADIO EU AI Act Compliance Profile

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.

License/Terms of Use

[License] This model is governed by the NVIDIA Open Model License Agreement.

References:

AM-RADIO: Agglomerative Vision Foundation Model - Reduce All Domains Into One

PHI-S: Distribution Balancing for Label-Free Multi-Teacher Distillation

RADIO Amplified: Improved Baselines for Agglomerative Vision Foundation Models

Model Architecture:

Architecture Type: Neural Network <br> Network Architecture: Vision Transformer <br>

Input:

Input Type(s): Image <br> Input Format(s): Red, Green, Blue (RGB) pixel values in [0, 1] range. <br> Input Parameters: Two Dimensional (2D) <br> Other Properties Related to Input: Image resolutions up to 2048x2028 in increments of 16 pixels <br>

Output:

Output Type(s): Embeddings <br> Output Format: Tensor <br> Output Parameters: 2D <br> Other Properties Related to Output: Downstream model required to leverage image features <br>

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.

import torch
from PIL import Image
from transformers import AutoModel, CLIPImageProcessor

hf_repo = "nvidia/C-RADIO"

image_processor = CLIPImageProcessor.from_pretrained(hf_repo)
model = AutoModel.f

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Library