File size: 5,680 Bytes
2690807 93d85d1 2690807 93d85d1 2690807 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 |
---
library_name: transformers
license: apple-ascl
metrics:
- accuracy
model-index:
- name: aimv2-3B-patch14-224
results:
- dataset:
name: imagenet-1k
type: imagenet-1k
metrics:
- name: Accuracy
type: accuracy
value: 88.5
verified: false
task:
name: Classification
type: classification
- dataset:
name: inaturalist-18
type: inaturalist-18
metrics:
- name: Accuracy
type: accuracy
value: 81.5
verified: false
task:
name: Classification
type: classification
- dataset:
name: cifar10
type: cifar10
metrics:
- name: Accuracy
type: accuracy
value: 99.5
verified: false
task:
name: Classification
type: classification
- dataset:
name: cifar100
type: cifar100
metrics:
- name: Accuracy
type: accuracy
value: 94.3
verified: false
task:
name: Classification
type: classification
- dataset:
name: food101
type: food101
metrics:
- name: Accuracy
type: accuracy
value: 96.8
verified: false
task:
name: Classification
type: classification
- dataset:
name: dtd
type: dtd
metrics:
- name: Accuracy
type: accuracy
value: 88.9
verified: false
task:
name: Classification
type: classification
- dataset:
name: oxford-pets
type: oxford-pets
metrics:
- name: Accuracy
type: accuracy
value: 97.1
verified: false
task:
name: Classification
type: classification
- dataset:
name: stanford-cars
type: stanford-cars
metrics:
- name: Accuracy
type: accuracy
value: 96.5
verified: false
task:
name: Classification
type: classification
- dataset:
name: camelyon17
type: camelyon17
metrics:
- name: Accuracy
type: accuracy
value: 93.5
verified: false
task:
name: Classification
type: classification
- dataset:
name: patch-camelyon
type: patch-camelyon
metrics:
- name: Accuracy
type: accuracy
value: 89.4
verified: false
task:
name: Classification
type: classification
- dataset:
name: rxrx1
type: rxrx1
metrics:
- name: Accuracy
type: accuracy
value: 7.3
verified: false
task:
name: Classification
type: classification
- dataset:
name: eurosat
type: eurosat
metrics:
- name: Accuracy
type: accuracy
value: 99.0
verified: false
task:
name: Classification
type: classification
- dataset:
name: fmow
type: fmow
metrics:
- name: Accuracy
type: accuracy
value: 64.2
verified: false
task:
name: Classification
type: classification
- dataset:
name: domainnet-infographic
type: domainnet-infographic
metrics:
- name: Accuracy
type: accuracy
value: 72.2
verified: false
task:
name: Classification
type: classification
pipeline_tag: image-feature-extraction
tags:
- vision
- image-feature-extraction
- mlx
- pytorch
---
# Introduction
[[`AIMv2 Paper`](https://arxiv.org/abs/2411.14402)] [[`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 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
### PyTorch
```python
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/aimv2-3B-patch14-224",
)
model = AutoModel.from_pretrained(
"apple/aimv2-3B-patch14-224",
trust_remote_code=True,
)
inputs = processor(images=image, return_tensors="pt")
outputs = model(**inputs)
```
### JAX
```python
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/aimv2-3B-patch14-224",
)
model = FlaxAutoModel.from_pretrained(
"apple/aimv2-3B-patch14-224",
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:
```bibtex
@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},
}
```
|