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--- |
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license: other |
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datasets: |
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- imagenet-1k |
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--- |
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[**FasterViT: Fast Vision Transformers with Hierarchical Attention**](https://arxiv.org/abs/2306.06189). |
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FasterViT achieves a new SOTA Pareto-front in |
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terms of accuracy vs. image throughput without extra training data ! |
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<p align="center"> |
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<img src="https://github.com/NVlabs/FasterViT/assets/26806394/253d1a2e-b5f5-4a9b-a362-6cdd16bfccc1" width=62% height=62% |
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class="center"> |
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</p> |
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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. |
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## Quick Start |
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We can import pre-trained FasterViT models with **1 line of code**. First, FasterViT can be simply installed by: |
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```bash |
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pip install fastervit |
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``` |
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A pretrained FasterViT model with default hyper-parameters can be created as in the following: |
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```python |
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>>> from fastervit import create_model |
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# Define fastervit-0 model with 224 x 224 resolution |
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>>> model = create_model('faster_vit_0_224', |
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pretrained=True, |
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model_path="/tmp/faster_vit_0.pth.tar") |
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``` |
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`model_path` is used to set the directory to download the model. |
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We can also simply test the model by passing a dummy input image. The output is the logits: |
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```python |
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>>> import torch |
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>>> image = torch.rand(1, 3, 224, 224) |
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>>> output = model(image) # torch.Size([1, 1000]) |
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``` |
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We can also use the any-resolution FasterViT model to accommodate arbitrary image resolutions. In the following, we define an any-resolution FasterViT-0 |
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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 |
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64: |
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```python |
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>>> from fastervit import create_model |
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# Define any-resolution FasterViT-0 model with 576 x 960 resolution |
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>>> model = create_model('faster_vit_0_any_res', |
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resolution=[576, 960], |
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window_size=[7, 7, 12, 6], |
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ct_size=2, |
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dim=64, |
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pretrained=True) |
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``` |
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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.) |
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We can simply test the model by passing a dummy input image. The output is the logits: |
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```python |
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>>> import torch |
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>>> image = torch.rand(1, 3, 576, 960) |
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>>> output = model(image) # torch.Size([1, 1000]) |
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``` |
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--- |
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## Results + Pretrained Models |
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### ImageNet-1K |
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**FasterViT ImageNet-1K Pretrained Models** |
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<table> |
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<tr> |
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<th>Name</th> |
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<th>Acc@1(%)</th> |
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<th>Acc@5(%)</th> |
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<th>Throughput(Img/Sec)</th> |
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<th>Resolution</th> |
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<th>#Params(M)</th> |
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<th>FLOPs(G)</th> |
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<th>Download</th> |
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</tr> |
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<tr> |
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<td>FasterViT-0</td> |
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<td>82.1</td> |
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<td>95.9</td> |
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<td>5802</td> |
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<td>224x224</td> |
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<td>31.4</td> |
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<td>3.3</td> |
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<td><a href="https://drive.google.com/uc?export=download&id=1twI2LFJs391Yrj8MR4Ui9PfrvWqjE1iB">model</a></td> |
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</tr> |
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<tr> |
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<td>FasterViT-1</td> |
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<td>83.2</td> |
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<td>96.5</td> |
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<td>4188</td> |
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<td>224x224</td> |
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<td>53.4</td> |
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<td>5.3</td> |
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<td><a href="https://drive.google.com/uc?export=download&id=1r7W10n5-bFtM3sz4bmaLrowN2gYPkLGT">model</a></td> |
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</tr> |
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<tr> |
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<td>FasterViT-2</td> |
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<td>84.2</td> |
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<td>96.8</td> |
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<td>3161</td> |
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<td>224x224</td> |
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<td>75.9</td> |
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<td>8.7</td> |
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<td><a href="https://drive.google.com/uc?export=download&id=1n_a6s0pgi0jVZOGmDei2vXHU5E6RH5wU">model</a></td> |
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</tr> |
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<tr> |
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<td>FasterViT-3</td> |
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<td>84.9</td> |
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<td>97.2</td> |
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<td>1780</td> |
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<td>224x224</td> |
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<td>159.5</td> |
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<td>18.2</td> |
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<td><a href="https://drive.google.com/uc?export=download&id=1tvWElZ91Sia2SsXYXFMNYQwfipCxtI7X">model</a></td> |
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</tr> |
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<tr> |
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<td>FasterViT-4</td> |
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<td>85.4</td> |
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<td>97.3</td> |
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<td>849</td> |
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<td>224x224</td> |
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<td>424.6</td> |
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<td>36.6</td> |
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<td><a href="https://drive.google.com/uc?export=download&id=1gYhXA32Q-_9C5DXel17avV_ZLoaHwdgz">model</a></td> |
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</tr> |
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<tr> |
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<td>FasterViT-5</td> |
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<td>85.6</td> |
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<td>97.4</td> |
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<td>449</td> |
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<td>224x224</td> |
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<td>975.5</td> |
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<td>113.0</td> |
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<td><a href="https://drive.google.com/uc?export=download&id=1mqpai7XiHLr_n1tjxjzT8q369xTCq_z-">model</a></td> |
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</tr> |
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<tr> |
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<td>FasterViT-6</td> |
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<td>85.8</td> |
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<td>97.4</td> |
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<td>352</td> |
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<td>224x224</td> |
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<td>1360.0</td> |
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<td>142.0</td> |
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<td><a href="https://drive.google.com/uc?export=download&id=12jtavR2QxmMzcKwPzWe7kw-oy34IYi59">model</a></td> |
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</tr> |
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</table> |
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### Robustness (ImageNet-A - ImageNet-R - ImageNet-V2) |
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All models use `crop_pct=0.875`. Results are obtained by running inference on ImageNet-1K pretrained models without finetuning. |
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<table> |
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<tr> |
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<th>Name</th> |
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<th>A-Acc@1(%)</th> |
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<th>A-Acc@5(%)</th> |
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<th>R-Acc@1(%)</th> |
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<th>R-Acc@5(%)</th> |
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<th>V2-Acc@1(%)</th> |
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<th>V2-Acc@5(%)</th> |
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</tr> |
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<tr> |
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<td>FasterViT-0</td> |
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<td>23.9</td> |
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<td>57.6</td> |
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<td>45.9</td> |
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<td>60.4</td> |
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<td>70.9</td> |
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<td>90.0</td> |
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</tr> |
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<tr> |
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<td>FasterViT-1</td> |
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<td>31.2</td> |
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<td>63.3</td> |
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<td>47.5</td> |
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<td>61.9</td> |
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<td>72.6</td> |
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<td>91.0</td> |
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</tr> |
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<tr> |
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<td>FasterViT-2</td> |
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<td>38.2</td> |
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<td>68.9</td> |
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<td>49.6</td> |
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<td>63.4</td> |
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<td>73.7</td> |
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<td>91.6</td> |
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</tr> |
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<tr> |
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<td>FasterViT-3</td> |
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<td>44.2</td> |
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<td>73.0</td> |
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<td>51.9</td> |
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<td>65.6</td> |
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<td>75.0</td> |
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<td>92.2</td> |
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</tr> |
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<tr> |
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<td>FasterViT-4</td> |
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<td>49.0</td> |
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<td>75.4</td> |
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<td>56.0</td> |
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<td>69.6</td> |
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<td>75.7</td> |
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<td>92.7</td> |
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</tr> |
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<tr> |
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<td>FasterViT-5</td> |
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<td>52.7</td> |
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<td>77.6</td> |
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<td>56.9</td> |
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<td>70.0</td> |
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<td>76.0</td> |
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<td>93.0</td> |
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</tr> |
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<tr> |
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<td>FasterViT-6</td> |
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<td>53.7</td> |
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<td>78.4</td> |
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<td>57.1</td> |
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<td>70.1</td> |
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<td>76.1</td> |
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<td>93.0</td> |
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</tr> |
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</table> |
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A, R and V2 denote ImageNet-A, ImageNet-R and ImageNet-V2 respectively. |
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## Citation |
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Please consider citing FasterViT if this repository is useful for your work. |
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``` |
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@article{hatamizadeh2023fastervit, |
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title={FasterViT: Fast Vision Transformers with Hierarchical Attention}, |
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author={Hatamizadeh, Ali and Heinrich, Greg and Yin, Hongxu and Tao, Andrew and Alvarez, Jose M and Kautz, Jan and Molchanov, Pavlo}, |
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journal={arXiv preprint arXiv:2306.06189}, |
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year={2023} |
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} |
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``` |
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## Licenses |
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Copyright © 2023, NVIDIA Corporation. All rights reserved. |
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This work is made available under the NVIDIA Source Code License-NC. Click [here](LICENSE) to view a copy of this license. |
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For license information regarding the timm repository, please refer to its [repository](https://github.com/rwightman/pytorch-image-models). |
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For license information regarding the ImageNet dataset, please see the [ImageNet official website](https://www.image-net.org/). |
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## Acknowledgement |
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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. |
