Architecture-Agnostic Masked Image Modeling - From ViT back to CNN

Siyuan Li^*,1,2, Di Wu^*,1,2, Fang Wu^1,3, Zelin Zang^1,2, Stan Z. Li^†,1

¹Westlake University, ²Zhejiang University, ³Tsinghua University

Masked image modeling (MIM), an emerging self-supervised pre-training method, has shown impressive success across numerous downstream vision tasks with Vision transformers (ViT). Its underlying idea is simple: a portion of the input image is randomly masked out and then reconstructed via the pre-text task. However, why MIM works well is not well explained, and previous studies insist that MIM primarily works for the Transformer family but is incompatible with CNNs. In this paper, we first study interactions among patches to understand what knowledge is learned and how it is acquired via the MIM task. We observe that MIM essentially teaches the model to learn better middle-level interactions among patches and extract more generalized features. Based on this fact, we propose an Architecture-Agnostic Masked Image Modeling framework (A2MIM), which is compatible with not only Transformers but also CNNs in a unified way. Extensive experiments on popular benchmarks show that our A2MIM learns better representations and endows the backbone model with the stronger capability to transfer to various downstream tasks for both Transformers and CNNs.

Table of Contents

1. Catalog
2. License
3. Acknowledgement
4. Citation

Catalog

We have released implementations of A2MIM based on OpenMixup. In the future, we plan to add A2MIM implementations to MMPretrain. Pre-trained and fine-tuned models are released in GitHub / Baidu Cloud.

• Update camera-ready version of A2MIM [arXiv] [poster]
• ImageNet pre-training and fine-tuning with OpenMixup [config_pretrain] [config_finetune]
• ImageNet pre-training and fine-tuning with MMPretrain
• Downstream Transfer to Object Detection on COCO with MMDetection [config]
• Downstream Transfer to Semantic Segmentation on ADE20K MMSegmentation [config]
• Analysis tools and results [rep_bottleneck]
• Visualization of pre-training on Google Colab and Notebook Demo

Pre-training on ImageNet

1. Installation

Please refer to INSTALL.md for installation instructions.

2. Pre-training and fine-tuning

We provide scripts for multiple GPUs pre-training and the specified CONFIG_FILE.

bash tools/dist_train.sh ${CONFIG_FILE} ${GPUS} [optional arguments]

For example, you can run the script below to pre-train ResNet-50 with A2MIM on ImageNet with 8 GPUs:

PORT=29500 bash tools/dist_train.sh configs/openmixup/pretrain/a2mim/imagenet/r50_l3_sz224_init_8xb256_cos_ep300.py 8

After pre-trianing, you can fine-tune and evaluate the models with the corresponding script:

python tools/model_converters/extract_backbone_weights.py work_dirs/openmixup/pretrain/a2mim/imagenet/r50_l3_sz224_init_8xb256_cos_ep300/latest.pth ${PATH_TO_CHECKPOINT}
PORT=29500 bash tools/dist_train_ft_8gpu.sh configs/openmixup/finetune/imagenet/r50_rsb_a3_ft_sz160_4xb512_cos_fp16_ep100.py ${PATH_TO_CHECKPOINT}

3. Implementation Details

• A2MIM model: In a2mim.py, the A2MIM method takes input samples, applies masking, encodes and caculates the MIM losses.
• A2MIM head: In mim_head.py, two MIM losses are computed, where regression_loss.py caculates the L1 loss and focal_loss.py caculates for the Fourier domain loss.
• Dataloader: In masked_image.py, loading the processed RGB images with the masked RGB mean.

Results and Models

We provide the summarization of pre-training (800 or 300 epochs) and fine-tuning (100 or 300 epochs) results of A2MIM and baselines on ImageNet-1K.

Methods	# Params.	Supervision	SimMIM	A2MIM
Target	(M)	Label	RGB	RGB
ViT-S	48.8	79.9	81.7	82.1
ViT-B	86.7	81.8	83.8	84.2
ViT-L	304.6	82.6	85.6	86.1
ResNet-50	25.6	79.8	79.9	80.4
ResNet-101	44.5	81.3	81.3	81.9
ResNet-152	60.2	81.8	81.9	82.5
ResNet-200	64.7	82.1	82.2	83.0
ConvNeXt-S	50.2	83.1	83.2	83.7
ConvNeXt-B	88.6	83.5	83.6	84.1

Config files, models, logs, and visualization of reconstructions are provided as follows. These files can also be downloaded from a2mim-in1k-weights, OpenMixup-a2mim-in1k-weights or Baidu Cloud: A2MIM (3q5i).

ViT-S/B/L on ImageNet-1K.

Method	Backbone	PT Epoch	FT Top-1	Pre-training	Fine-tuning	Results
SimMIM	ViT-Small	800	81.7	config | ckpt | vis	config	ckpt | log
A2MIM	ViT-Small	800	82.1	config | ckpt | vis	config	ckpt | log
SimMIM	ViT-Base	800	83.8	config | ckpt | vis	config	ckpt | log
A2MIM	ViT-Base	800	84.3	config | ckpt | vis	config	ckpt | log
SimMIM	ViT-Large	800	85.6	config | ckpt | vis	config	log
A2MIM	ViT-Large	800	86.1	config | ckpt | vis	config	log

ResNet-50/101/152/200 on ImageNet-1K.

Method	Backbone	PT Epoch	FT (A2) Top-1	Pre-training	Fine-tuning	Results
SimMIM	ResNet-50	300	79.9	config | ckpt | vis	RSB A2	-
A2MIM	ResNet-50	100	78.8	config | ckpt | vis	RSB A3	ckpt | log
A2MIM	ResNet-50	300	80.4	config | ckpt | vis	RSB A2	ckpt | log
SimMIM	ResNet-101	300	81.3	config | ckpt	RSB A2	ckpt (A3) | log (A3)
A2MIM	ResNet-101	300	81.9	config | ckpt (300ep) | ckpt (800ep)	RSB A2	ckpt (A2) | log (A2)
SimMIM	ResNet-152	300	81.9	config | ckpt	RSB A2	log (A3)
A2MIM	ResNet-152	300	82.5	config | ckpt (300ep) | ckpt (800ep)	RSB A2	ckpt (A2) | log (A2)
SimMIM	ResNet-200	300	82.2	config | ckpt | vis	RSB A2	ckpt | log
A2MIM	ResNet-200	300	83.0	config | ckpt | vis	RSB A2	ckpt | log

ConvNeXt-S/B on ImageNet-1K.

Method	Backbone	PT Epoch	FT (A2) Top-1	Pre-training	Fine-tuning	Results
SimMIM	ConvNeXt-S	300	83.2	config | ckpt | vis	RSB A2	-
A2MIM	ConvNeXt-S	300	83.7	config | ckpt | vis	RSB A2	ckpt | log
SimMIM	ConvNeXt-B	300	83.6	config | ckpt	RSB A2	ckpt | log
A2MIM	ConvNeXt-B	300	84.1	config | ckpt	RSB A2	ckpt (A2) | ckpt (A3) | log (A2) | log (A3)

4. Empirical Studies

We provided interpretation of how masked image modeling works with representation bottleneck based on ViTs and CNNs. As shown in Figure 1/5/A1/A2 in A2MIM and following figures, we visualize the multi-order interation strengths with representation_bottleneck. We also provided analysis from frequency perspectives in Figure A3/A4 in A2MIM based on fourier_analysis.

License

This project is released under the Apache 2.0 license.

Acknowledgement

Our implementation is mainly based on the following codebases. We gratefully thank the authors for their wonderful works.

• OpenMixup: Open-source toolbox for supervised and self-supervised visual representation learning.
• pytorch-image-models: PyTorch image models, scripts, pretrained weights.
• SimMIM: Official PyTorch implementation of SimMIM.
• MMPretrain: OpenMMLab Pre-training Toolbox and Benchmark.
• MMDetection: OpenMMLab Detection Toolbox and Benchmark.
• MMSegmentation: OpenMMLab Semantic Segmentation Toolbox and Benchmark.

Citation

If you find this repository helpful, please consider citing our paper:

@inproceedings{icml2023a2mim,
  title={Architecture-Agnostic Masked Image Modeling -- From ViT back to CNN},
  author={Li, Siyuan and Wu, Di and Wu, Fang and Zang, Zelin and Li, Stan. Z.},
  booktitle={International Conference on Machine Learning},
  year={2023},
}

(back to top)