MViT V2

MViTv2: Improved Multiscale Vision Transformers for Classification and Detection

摘要

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.

使用方式

Predict imageUse the modelTest Command

from mmpretrain import inference_model

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

Models and results

Image Classification on ImageNet-1k

模型	预训练	Params (M)	Flops (G)	Top-1 (%)	Top-5 (%)	配置文件	下载
mvitv2-tiny_3rdparty_in1k*	从头训练	24.17	4.70	82.33	96.15	config	model
mvitv2-small_3rdparty_in1k*	从头训练	34.87	7.00	83.63	96.51	config	model
mvitv2-base_3rdparty_in1k*	从头训练	51.47	10.16	84.34	96.86	config	model
mvitv2-large_3rdparty_in1k*	从头训练	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 reprodcue the training results.

引用

@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}
}