File size: 8,648 Bytes
20ee378 81896da 20ee378 81896da d43fc23 81896da |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 |
---
license: other
datasets:
- imagenet-1k
---
[**FasterViT: Fast Vision Transformers with Hierarchical Attention**](https://arxiv.org/abs/2306.06189).
FasterViT achieves a new SOTA Pareto-front in
terms of accuracy vs. image throughput without extra training data !
<p align="center">
<img src="https://github.com/NVlabs/FasterViT/assets/26806394/253d1a2e-b5f5-4a9b-a362-6cdd16bfccc1" width=62% height=62%
class="center">
</p>
Note: Please use the [**latest NVIDIA TensorRT release**](https://docs.nvidia.com/deeplearning/tensorrt/container-release-notes/index.html) to enjoy the benefits of optimized FasterViT ops.
## Quick Start
We can import pre-trained FasterViT models with **1 line of code**. First, FasterViT can be simply installed by:
```bash
pip install fastervit
```
A pretrained FasterViT model with default hyper-parameters can be created as in the following:
```python
>>> from fastervit import create_model
# Define fastervit-0 model with 224 x 224 resolution
>>> model = create_model('faster_vit_0_224',
pretrained=True,
model_path="/tmp/faster_vit_0.pth.tar")
```
`model_path` is used to set the directory to download the model.
We can also simply test the model by passing a dummy input image. The output is the logits:
```python
>>> import torch
>>> image = torch.rand(1, 3, 224, 224)
>>> output = model(image) # torch.Size([1, 1000])
```
We can also use the any-resolution FasterViT model to accommodate arbitrary image resolutions. In the following, we define an any-resolution FasterViT-0
model with input resolution of 576 x 960, window sizes of 12 and 6 in 3rd and 4th stages, carrier token size of 2 and embedding dimension of
64:
```python
>>> from fastervit import create_model
# Define any-resolution FasterViT-0 model with 576 x 960 resolution
>>> model = create_model('faster_vit_0_any_res',
resolution=[576, 960],
window_size=[7, 7, 12, 6],
ct_size=2,
dim=64,
pretrained=True)
```
Note that the above model is intiliazed from the original ImageNet pre-trained FasterViT with original resolution of 224 x 224. As a result, missing keys and mis-matches could be expected since we are addign new layers (e.g. addition of new carrier tokens, etc.)
We can simply test the model by passing a dummy input image. The output is the logits:
```python
>>> import torch
>>> image = torch.rand(1, 3, 576, 960)
>>> output = model(image) # torch.Size([1, 1000])
```
---
## Results + Pretrained Models
### ImageNet-1K
**FasterViT ImageNet-1K Pretrained Models**
<table>
<tr>
<th>Name</th>
<th>Acc@1(%)</th>
<th>Acc@5(%)</th>
<th>Throughput(Img/Sec)</th>
<th>Resolution</th>
<th>#Params(M)</th>
<th>FLOPs(G)</th>
<th>Download</th>
</tr>
<tr>
<td>FasterViT-0</td>
<td>82.1</td>
<td>95.9</td>
<td>5802</td>
<td>224x224</td>
<td>31.4</td>
<td>3.3</td>
<td><a href="https://drive.google.com/uc?export=download&id=1twI2LFJs391Yrj8MR4Ui9PfrvWqjE1iB">model</a></td>
</tr>
<tr>
<td>FasterViT-1</td>
<td>83.2</td>
<td>96.5</td>
<td>4188</td>
<td>224x224</td>
<td>53.4</td>
<td>5.3</td>
<td><a href="https://drive.google.com/uc?export=download&id=1r7W10n5-bFtM3sz4bmaLrowN2gYPkLGT">model</a></td>
</tr>
<tr>
<td>FasterViT-2</td>
<td>84.2</td>
<td>96.8</td>
<td>3161</td>
<td>224x224</td>
<td>75.9</td>
<td>8.7</td>
<td><a href="https://drive.google.com/uc?export=download&id=1n_a6s0pgi0jVZOGmDei2vXHU5E6RH5wU">model</a></td>
</tr>
<tr>
<td>FasterViT-3</td>
<td>84.9</td>
<td>97.2</td>
<td>1780</td>
<td>224x224</td>
<td>159.5</td>
<td>18.2</td>
<td><a href="https://drive.google.com/uc?export=download&id=1tvWElZ91Sia2SsXYXFMNYQwfipCxtI7X">model</a></td>
</tr>
<tr>
<td>FasterViT-4</td>
<td>85.4</td>
<td>97.3</td>
<td>849</td>
<td>224x224</td>
<td>424.6</td>
<td>36.6</td>
<td><a href="https://drive.google.com/uc?export=download&id=1gYhXA32Q-_9C5DXel17avV_ZLoaHwdgz">model</a></td>
</tr>
<tr>
<td>FasterViT-5</td>
<td>85.6</td>
<td>97.4</td>
<td>449</td>
<td>224x224</td>
<td>975.5</td>
<td>113.0</td>
<td><a href="https://drive.google.com/uc?export=download&id=1mqpai7XiHLr_n1tjxjzT8q369xTCq_z-">model</a></td>
</tr>
<tr>
<td>FasterViT-6</td>
<td>85.8</td>
<td>97.4</td>
<td>352</td>
<td>224x224</td>
<td>1360.0</td>
<td>142.0</td>
<td><a href="https://drive.google.com/uc?export=download&id=12jtavR2QxmMzcKwPzWe7kw-oy34IYi59">model</a></td>
</tr>
</table>
### ImageNet-21K
**FasterViT ImageNet-21K Pretrained Models (ImageNet-1K Fine-tuned)**
<table>
<tr>
<th>Name</th>
<th>Acc@1(%)</th>
<th>Acc@5(%)</th>
<th>Resolution</th>
<th>#Params(M)</th>
<th>FLOPs(G)</th>
<th>Download</th>
</tr>
<tr>
<td>FasterViT-4-21K-224</td>
<td>86.6</td>
<td>97.8</td>
<td>224x224</td>
<td>271.9</td>
<td>40.8</td>
<td><a href="https://huggingface.co/ahatamiz/FasterViT/resolve/main/fastervit_4_21k_224_w14.pth.tar">model</a></td>
</tr>
<tr>
<td>FasterViT-4-21K-384</td>
<td>87.6</td>
<td>98.3</td>
<td>384x384</td>
<td>271.9</td>
<td>120.1</td>
<td><a href="https://huggingface.co/ahatamiz/FasterViT/resolve/main/fastervit_4_21k_384_w24.pth.tar">model</a></td>
</tr>
<tr>
<td>FasterViT-4-21K-512</td>
<td>87.8</td>
<td>98.4</td>
<td>512x512</td>
<td>271.9</td>
<td>213.5</td>
<td><a href="https://huggingface.co/ahatamiz/FasterViT/resolve/main/fastervit_4_21k_512_w32.pth.tar">model</a></td>
</tr>
<tr>
<td>FasterViT-4-21K-768</td>
<td>87.9</td>
<td>98.5</td>
<td>768x768</td>
<td>271.9</td>
<td>480.4</td>
<td><a href="https://huggingface.co/ahatamiz/FasterViT/resolve/main/fastervit_4_21k_768_w48.pth.tar">model</a></td>
</tr>
</table>
### Robustness (ImageNet-A - ImageNet-R - ImageNet-V2)
All models use `crop_pct=0.875`. Results are obtained by running inference on ImageNet-1K pretrained models without finetuning.
<table>
<tr>
<th>Name</th>
<th>A-Acc@1(%)</th>
<th>A-Acc@5(%)</th>
<th>R-Acc@1(%)</th>
<th>R-Acc@5(%)</th>
<th>V2-Acc@1(%)</th>
<th>V2-Acc@5(%)</th>
</tr>
<tr>
<td>FasterViT-0</td>
<td>23.9</td>
<td>57.6</td>
<td>45.9</td>
<td>60.4</td>
<td>70.9</td>
<td>90.0</td>
</tr>
<tr>
<td>FasterViT-1</td>
<td>31.2</td>
<td>63.3</td>
<td>47.5</td>
<td>61.9</td>
<td>72.6</td>
<td>91.0</td>
</tr>
<tr>
<td>FasterViT-2</td>
<td>38.2</td>
<td>68.9</td>
<td>49.6</td>
<td>63.4</td>
<td>73.7</td>
<td>91.6</td>
</tr>
<tr>
<td>FasterViT-3</td>
<td>44.2</td>
<td>73.0</td>
<td>51.9</td>
<td>65.6</td>
<td>75.0</td>
<td>92.2</td>
</tr>
<tr>
<td>FasterViT-4</td>
<td>49.0</td>
<td>75.4</td>
<td>56.0</td>
<td>69.6</td>
<td>75.7</td>
<td>92.7</td>
</tr>
<tr>
<td>FasterViT-5</td>
<td>52.7</td>
<td>77.6</td>
<td>56.9</td>
<td>70.0</td>
<td>76.0</td>
<td>93.0</td>
</tr>
<tr>
<td>FasterViT-6</td>
<td>53.7</td>
<td>78.4</td>
<td>57.1</td>
<td>70.1</td>
<td>76.1</td>
<td>93.0</td>
</tr>
</table>
A, R and V2 denote ImageNet-A, ImageNet-R and ImageNet-V2 respectively.
## Citation
Please consider citing FasterViT if this repository is useful for your work.
```
@article{hatamizadeh2023fastervit,
title={FasterViT: Fast Vision Transformers with Hierarchical Attention},
author={Hatamizadeh, Ali and Heinrich, Greg and Yin, Hongxu and Tao, Andrew and Alvarez, Jose M and Kautz, Jan and Molchanov, Pavlo},
journal={arXiv preprint arXiv:2306.06189},
year={2023}
}
```
## Licenses
Copyright © 2023, NVIDIA Corporation. All rights reserved.
This work is made available under the NVIDIA Source Code License-NC. Click [here](LICENSE) to view a copy of this license.
For license information regarding the timm repository, please refer to its [repository](https://github.com/rwightman/pytorch-image-models).
For license information regarding the ImageNet dataset, please see the [ImageNet official website](https://www.image-net.org/).
## Acknowledgement
This repository is built on top of the [timm](https://github.com/huggingface/pytorch-image-models) repository. We thank [Ross Wrightman](https://rwightman.com/) for creating and maintaining this high-quality library. |