transformers/docs/source/en/model_doc/aimv2.md

This model was published in HF papers on 2024-11-21 and contributed to Hugging Face Transformers on 2025-07-08.

AIMv2

Overview

The AIMv2 model was proposed in Multimodal Autoregressive Pre-training of Large Vision Encoders by Enrico Fini, Mustafa Shukor, Xiujun Li, Philipp Dufter, Michal Klein, David Haldimann, Sai Aitharaju, Victor Guilherme Turrisi da Costa, Louis Béthune, Zhe Gan, Alexander T Toshev, Marcin Eichner, Moin Nabi, Yinfei Yang, Joshua M. Susskind, Alaaeldin El-Nouby.

The abstract from the paper is the following:

We introduce a novel method for pre-training of large-scale vision encoders. Building on recent advancements in autoregressive pre-training of vision models, we extend this framework to a multimodal setting, i.e., images and text. In this paper, we present AIMV2, a family of generalist vision encoders characterized by a straightforward pre-training process, scalability, and remarkable performance across a range of downstream tasks. This is achieved by pairing the vision encoder with a multimodal decoder that autoregressively generates raw image patches and text tokens. Our encoders excel not only in multimodal evaluations but also in vision benchmarks such as localization, grounding, and classification. Notably, our AIMV2-3B encoder achieves 89.5% accuracy on ImageNet-1k with a frozen trunk. Furthermore, AIMV2 consistently outperforms state-of-the-art contrastive models (e.g., CLIP, SigLIP) in multimodal image understanding across diverse settings.

This model was contributed by Yaswanth Gali. The original code can be found here.

Usage Example

Here is an example of Image Feature Extraction using specific checkpoints on resized images and native resolution images:

import requests
from PIL import Image

from transformers import AutoImageProcessor, AutoModel


url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)

processor = AutoImageProcessor.from_pretrained("apple/aimv2-large-patch14-native")
model = AutoModel.from_pretrained("apple/aimv2-large-patch14-native", device_map="auto")

inputs = processor(images=image, return_tensors="pt").to(model.device)
outputs = model(**inputs)

Here is an example of a checkpoint performing zero-shot classification:

import requests
from PIL import Image

from transformers import AutoModel, AutoProcessor


url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
text = ["Picture of a dog.", "Picture of a cat.", "Picture of a horse."]

processor = AutoProcessor.from_pretrained("apple/aimv2-large-patch14-224-lit")
model = AutoModel.from_pretrained("apple/aimv2-large-patch14-224-lit", device_map="auto")

inputs = processor(
    images=image,
    text=text,
    add_special_tokens=True,
    truncation=True,
    padding=True,
    return_tensors="pt",
)
outputs = model(**inputs)
probs = outputs.logits_per_image.softmax(dim=-1)

Aimv2Config

autodoc Aimv2Config

Aimv2TextConfig

autodoc Aimv2TextConfig

Aimv2VisionConfig

autodoc Aimv2VisionConfig

Aimv2Model

autodoc Aimv2Model - forward - get_text_features - get_image_features

Aimv2VisionModel

autodoc Aimv2VisionModel - forward

Aimv2TextModel

autodoc Aimv2TextModel - forward