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README.md

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----
-license: apple-ascl
----
+---
+library_name: transformers
+license: apple-ascl
+pipeline_tag: zero-shot-image-classification
+tags:
+- vision
+- mlx
+- pytorch
+---
+# Introduction
+[[`AIMv2 Paper`](#)] [[`BibTeX`](#citation)]
+
+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 to 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*.
+
+<img src="aimv2_overview_light.png" alt="AIMv2 Overview"/>
+
+## Usage
+Under construction. Please consider using the models in the [ml-aim](https://github.com/apple/ml-aim) repository.
+
+## Citation
+If you find our work useful, please consider citing us as:
+```bibtex
+@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},
+}
+```

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