apple / aimv2-large-patch14-native

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Total runs: 406
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Model's Last Updated: July 09 2025
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Introduction of aimv2-large-patch14-native

Model Details of aimv2-large-patch14-native

Introduction

[ AIMv2 Paper ] [ BibTeX ]

We introduce the AIMv2 family of vision models pre-trained with a multimodal autoregressive objective. AIMv2 pre-training is simple and straightforward to train and scale effectively. Some AIMv2 highlights include:

  1. Outperforms OAI CLIP and SigLIP on the majority of multimodal understanding benchmarks.
  2. Outperforms DINOv2 on open-vocabulary object detection and referring expression comprehension.
  3. Exhibits strong recognition performance with AIMv2-3B achieving 89.5% on ImageNet using a frozen trunk .
AIMv2 Overview
Usage
PyTorch
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",
    trust_remote_code=True,
)

inputs = processor(images=image, return_tensors="pt")
outputs = model(**inputs)
JAX
import requests
from PIL import Image
from transformers import AutoImageProcessor, FlaxAutoModel

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 = FlaxAutoModel.from_pretrained(
    "apple/aimv2-large-patch14-native",
    trust_remote_code=True,
)

inputs = processor(images=image, return_tensors="jax")
outputs = model(**inputs)
Citation

If you find our work useful, please consider citing us as:

@misc{fini2024multimodal,
  title         = {Multimodal Autoregressive Pre-training of Large Vision Encoders},
  author        = {Enrico Fini and Mustafa Shukor and Xiujun Li and Philipp Dufter and Michal Klein and David Haldimann and Sai Aitharaju and Victor Guilherme Turrisi da Costa and Louis Béthune and Zhe Gan and Alexander T Toshev and Marcin Eichner and Moin Nabi and Yinfei Yang and Joshua M. Susskind and Alaaeldin El-Nouby},
  year          = {2024},
  archivePrefix = {arXiv},
  primaryClass  = {cs.CV},
}

Runs of apple aimv2-large-patch14-native on huggingface.co

406
Total runs
-12
24-hour runs
-8
3-day runs
-36
7-day runs
0
30-day runs

More Information About aimv2-large-patch14-native huggingface.co Model

More aimv2-large-patch14-native license Visit here:

https://choosealicense.com/licenses/apple-amlr

aimv2-large-patch14-native huggingface.co

aimv2-large-patch14-native huggingface.co is an AI model on huggingface.co that provides aimv2-large-patch14-native's model effect (), which can be used instantly with this apple aimv2-large-patch14-native model. huggingface.co supports a free trial of the aimv2-large-patch14-native model, and also provides paid use of the aimv2-large-patch14-native. Support call aimv2-large-patch14-native model through api, including Node.js, Python, http.

aimv2-large-patch14-native huggingface.co Url

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apple aimv2-large-patch14-native online free

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apple aimv2-large-patch14-native online free url in huggingface.co:

https://huggingface.co/apple/aimv2-large-patch14-native

aimv2-large-patch14-native install

aimv2-large-patch14-native is an open source model from GitHub that offers a free installation service, and any user can find aimv2-large-patch14-native on GitHub to install. At the same time, huggingface.co provides the effect of aimv2-large-patch14-native install, users can directly use aimv2-large-patch14-native installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

aimv2-large-patch14-native install url in huggingface.co:

https://huggingface.co/apple/aimv2-large-patch14-native

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