MViT V2¶

MViTv2: Improved Multiscale Vision Transformers for Classification and Detection

Abstract¶

In this paper, we study Multiscale Vision Transformers (MViTv2) as a unified architecture for image and video classification, as well as object detection. We present an improved version of MViT that incorporates decomposed relative positional embeddings and residual pooling connections. We instantiate this architecture in five sizes and evaluate it for ImageNet classification, COCO detection and Kinetics video recognition where it outperforms prior work. We further compare MViTv2s’ pooling attention to window attention mechanisms where it outperforms the latter in accuracy/compute. Without bells-and-whistles, MViTv2 has state-of-the-art performance in 3 domains: 88.8% accuracy on ImageNet classification, 58.7 boxAP on COCO object detection as well as 86.1% on Kinetics-400 video classification.

How to use it?¶

from mmpretrain import inference_model

predict = inference_model('mvitv2-tiny_3rdparty_in1k', 'demo/bird.JPEG')
print(predict['pred_class'])
print(predict['pred_score'])

import torch
from mmpretrain import get_model

model = get_model('mvitv2-tiny_3rdparty_in1k', pretrained=True)
inputs = torch.rand(1, 3, 224, 224)
out = model(inputs)
print(type(out))
# To extract features.
feats = model.extract_feat(inputs)
print(type(feats))

Prepare your dataset according to the docs.

Test:

python tools/test.py configs/mvit/mvitv2-tiny_8xb256_in1k.py https://download.openmmlab.com/mmclassification/v0/mvit/mvitv2-tiny_3rdparty_in1k_20220722-db7beeef.pth

Models and results¶

Image Classification on ImageNet-1k¶

Model	Pretrain	Params (M)	Flops (G)	Top-1 (%)	Top-5 (%)	Config	Download
`mvitv2-tiny_3rdparty_in1k`*	From scratch	24.17	4.70	82.33	96.15	config	model
`mvitv2-small_3rdparty_in1k`*	From scratch	34.87	7.00	83.63	96.51	config	model
`mvitv2-base_3rdparty_in1k`*	From scratch	51.47	10.16	84.34	96.86	config	model
`mvitv2-large_3rdparty_in1k`*	From scratch	217.99	43.87	85.25	97.14	config	model

Models with * are converted from the official repo. The config files of these models are only for inference. We haven’t reproduce the training results.

Citation¶

@inproceedings{li2021improved,
  title={MViTv2: Improved multiscale vision transformers for classification and detection},
  author={Li, Yanghao and Wu, Chao-Yuan and Fan, Haoqi and Mangalam, Karttikeya and Xiong, Bo and Malik, Jitendra and Feichtenhofer, Christoph},
  booktitle={CVPR},
  year={2022}
}