mixmim

MixMIM

MixMIM: Mixed and Masked Image Modeling for Efficient Visual Representation Learning

Abstract

In this study, we propose Mixed and Masked Image Modeling (MixMIM), a simple but efficient MIM method that is applicable to various hierarchical Vision Transformers. Existing MIM methods replace a random subset of input tokens with a special [MASK] symbol and aim at reconstructing original image tokens from the corrupted image. However, we find that using the [MASK] symbol greatly slows down the training and causes training-finetuning inconsistency, due to the large masking ratio (e.g., 40% in BEiT). In contrast, we replace the masked tokens of one image with visible tokens of another image, i.e., creating a mixed image. We then conduct dual reconstruction to reconstruct the original two images from the mixed input, which significantly improves efficiency. While MixMIM can be applied to various architectures, this paper explores a simpler but stronger hierarchical Transformer, and scales with MixMIM-B, -L, and -H. Empirical results demonstrate that MixMIM can learn high-quality visual representations efficiently. Notably, MixMIM-B with 88M parameters achieves 85.1% top-1 accuracy on ImageNet-1K by pretraining for 600 epochs, setting a new record for neural networks with comparable model sizes (e.g., ViT-B) among MIM methods. Besides, its transferring performances on the other 6 datasets show MixMIM has better FLOPs / performance tradeoff than previous MIM methods

How to use it?

Predict image

from mmpretrain import inference_model

predict = inference_model('mixmim-base_mixmim-pre_8xb128-coslr-100e_in1k', 'demo/bird.JPEG')
print(predict['pred_class'])
print(predict['pred_score'])

Use the model

import torch
from mmpretrain import get_model

model = get_model('mixmim_mixmim-base_16xb128-coslr-300e_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))

Train/Test Command

Prepare your dataset according to the docs.

Train:

python tools/train.py configs/mixmim/mixmim_mixmim-base_16xb128-coslr-300e_in1k.py

Test:

python tools/test.py configs/mixmim/benchmarks/mixmim-base_8xb128-coslr-100e_in1k.py https://download.openmmlab.com/mmselfsup/1.x/mixmim/mixmim-base-p16_16xb128-coslr-300e_in1k/mixmim-base-p16_ft-8xb128-coslr-100e_in1k/mixmim-base-p16_ft-8xb128-coslr-100e_in1k_20221208-41ecada9.pth

Models and results

Pretrained models

Model	Params (M)	Flops (G)	Config	Download
mixmim_mixmim-base_16xb128-coslr-300e_in1k	114.67	16.35	config	model | log

Image Classification on ImageNet-1k

Model	Pretrain	Params (M)	Flops (G)	Top-1 (%)	Config	Download
mixmim-base_mixmim-pre_8xb128-coslr-100e_in1k	MIXMIM	88.34	16.35	84.63	config	model | log

Citation

@article{MixMIM2022,
  author  = {Jihao Liu, Xin Huang, Yu Liu, Hongsheng Li},
  journal = {arXiv:2205.13137},
  title   = {MixMIM: Mixed and Masked Image Modeling for Efficient Visual Representation Learning},
  year    = {2022},
}