apple / aimv2-large-patch14-336

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Total runs: 203
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7-day runs: 83
30-day runs: 149
Model's Last Updated: July 09 2025
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Introduction of aimv2-large-patch14-336

Model Details of aimv2-large-patch14-336

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-patch14-336",
)
model = AutoModel.from_pretrained(
    "apple/aimv2-large-patch14-336",
    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-patch14-336",
)
model = FlaxAutoModel.from_pretrained(
    "apple/aimv2-large-patch14-336",
    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{fini2024multimodalautoregressivepretraininglarge,
  author      = {Fini, Enrico and Shukor, Mustafa and Li, Xiujun and Dufter, Philipp and Klein, Michal and Haldimann, David and Aitharaju, Sai and da Costa, Victor Guilherme Turrisi and Béthune, Louis and Gan, Zhe and Toshev, Alexander T and Eichner, Marcin and Nabi, Moin and Yang, Yinfei and Susskind, Joshua M. and El-Nouby, Alaaeldin},
  url         = {https://arxiv.org/abs/2411.14402},
  eprint      = {2411.14402},
  eprintclass = {cs.CV},
  eprinttype  = {arXiv},
  title       = {Multimodal Autoregressive Pre-training of Large Vision Encoders},
  year        = {2024},
}

Runs of apple aimv2-large-patch14-336 on huggingface.co

203
Total runs
49
24-hour runs
66
3-day runs
83
7-day runs
149
30-day runs

More Information About aimv2-large-patch14-336 huggingface.co Model

More aimv2-large-patch14-336 license Visit here:

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aimv2-large-patch14-336 huggingface.co

aimv2-large-patch14-336 huggingface.co is an AI model on huggingface.co that provides aimv2-large-patch14-336's model effect (), which can be used instantly with this apple aimv2-large-patch14-336 model. huggingface.co supports a free trial of the aimv2-large-patch14-336 model, and also provides paid use of the aimv2-large-patch14-336. Support call aimv2-large-patch14-336 model through api, including Node.js, Python, http.

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aimv2-large-patch14-336 install

aimv2-large-patch14-336 is an open source model from GitHub that offers a free installation service, and any user can find aimv2-large-patch14-336 on GitHub to install. At the same time, huggingface.co provides the effect of aimv2-large-patch14-336 install, users can directly use aimv2-large-patch14-336 installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

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