fastervit 0.8.9

FasterViT: Fast Vision Transformers with Hierarchical Attention

FasterViT: Fast Vision Transformers with Hierarchical Attention

Official PyTorch implementation of FasterViT: Fast Vision Transformers with Hierarchical Attention.

Ali Hatamizadeh, Greg Heinrich, Hongxu (Danny) Yin, Andrew Tao, Jose M. Alvarez, Jan Kautz, Pavlo Molchanov.

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FasterViT achieves a new SOTA Pareto-front in terms of accuracy vs. image throughput (no extra training data !)

Quick Start

The FasterViT can be conveniently installed by:

pip install fastervit

A FasterViT model with default hyper-parameters can be created as in the following:

>>> from fastervit import create_model

# Define fastervit-0 model with 224 x 224 resolution
>>> model = create_model('faster_vit_0_224')

We can also use the any-resolution FasterViT model to accommodate arbitrary image resolutions. In the following, we define an any-resolution FasterViT-1 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 128:

>>> from fastervit import create_model

# Define any-resolution FasterViT-1 model with 576 x 960 resolution
>>> model = create_model('faster_vit_1_any_res', 
                          resolution=[576, 960],
                          window_size=[7, 7, 12, 6],
                          ct_size=2,
                          dim=128)

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Results + Pretrained Models

ImageNet-1K

FasterViT ImageNet-1K Pretrained Models

Name	Acc@1(%)	Acc@5(%)	Throughput(Img/Sec)	Resolution	#Params(M)	FLOPs(G)	Download
FasterViT-0	82.1	95.9	5802	224x224	31.4	3.3	model
FasterViT-1	83.2	96.5	4188	224x224	53.4	5.3	model
FasterViT-2	84.2	96.8	3161	224x224	75.9	8.7	model
FasterViT-3	84.9	97.2	1780	224x224	159.5	18.2	model
FasterViT-4	85.4	97.3	849	224x224	424.6	36.6	model
FasterViT-5	85.6	97.4	449	224x224	975.5	113.0	model
FasterViT-6	85.8	97.4	352	224x224	1360.0	142.0	model

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.

Name	A-Acc@1(%)	A-Acc@5(%)	R-Acc@1(%)	R-Acc@5(%)	V2-Acc@1(%)	V2-Acc@5(%)
FasterViT-0	23.9	57.6	45.9	60.4	70.9	90.0
FasterViT-1	31.2	63.3	47.5	61.9	72.6	91.0
FasterViT-2	38.2	68.9	49.6	63.4	73.7	91.6
FasterViT-3	44.2	73.0	51.9	65.6	75.0	92.2
FasterViT-4	49.0	75.4	56.0	69.6	75.7	92.7
FasterViT-5	52.7	77.6	56.9	70.0	76.0	93.0
FasterViT-6	53.7	78.4	57.1	70.1	76.1	93.0

A, R and V2 denote ImageNet-A, ImageNet-R and ImageNet-V2 respectively.

Training

Please see TRAINING.md for detailed training instructions of all models.

Evaluation

The FasterViT models can be evaluated on ImageNet-1K validation set using the following:

python validate.py \
--model <model-name>
--checkpoint <checkpoint-path>
--data_dir <imagenet-path>
--batch-size <batch-size-per-gpu

Here --model is the FasterViT variant (e.g. faster_vit_0_224_1k), --checkpoint is the path to pretrained model weights, --data_dir is the path to ImageNet-1K validation set and --batch-size is the number of batch size. We also provide a sample script here.

Installation

The dependencies can be installed by running:

pip install -r requirements.txt

Third-party Extentions

We always welcome third-party extentions/implementations and usage for other purposes. If you would like your work to be listed in this repository, please raise and issue and provide us with detailed information.

Acknowledgement

This repository is built on top of the timm repository. We thank Ross Wrightman for creating and maintaining this high-quality library.

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 to view a copy of this license.

For license information regarding the timm repository, please refer to its repository.

For license information regarding the ImageNet dataset, please see the ImageNet official website.