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README.md

MILAN

MILAN: Masked Image Pretraining on Language Assisted Representation

Abstract

Self-attention based transformer models have been dominating many computer vision tasks in the past few years. Their superb model qualities heavily depend on the excessively large labeled image datasets. In order to reduce the reliance on large labeled datasets, reconstruction based masked autoencoders are gaining popularity, which learn high quality transferable representations from unlabeled images. For the same purpose, recent weakly supervised image pretraining methods explore language supervision from text captions accompanying the images. In this work, we propose masked image pretraining on language assisted representation, dubbed as MILAN. Instead of predicting raw pixels or low level features, our pretraining objective is to reconstruct the image features with substantial semantic signals that are obtained using caption supervision. Moreover, to accommodate our reconstruction target, we propose a more efficient prompting decoder architecture and a semantic aware mask sampling mechanism, which further advance the transfer performance of the pretrained model. Experimental results demonstrate that MILAN delivers higher accuracy than the previous works. When the masked autoencoder is pretrained and finetuned on ImageNet-1K dataset with an input resolution of 224×224, MILAN achieves a top-1 accuracy of 85.4% on ViTB/16, surpassing previous state-of-the-arts by 1%. In the downstream semantic segmentation task, MILAN achieves 52.7 mIoU using ViT-B/16 backbone on ADE20K dataset, outperforming previous masked pretraining results by 4 points.

How to use it?

Predict image

from mmpretrain import inference_model

predict = inference_model('vit-base-p16_milan-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('milan_vit-base-p16_16xb256-amp-coslr-400e_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/milan/milan_vit-base-p16_16xb256-amp-coslr-400e_in1k.py

Test:

python tools/test.py configs/milan/benchmarks/vit-base-p16_8xb128-coslr-100e_in1k.py https://download.openmmlab.com/mmselfsup/1.x/milan/milan_vit-base-p16_16xb256-amp-coslr-400e_in1k/vit-base-p16_ft-8xb128-coslr-100e_in1k/vit-base-p16_ft-8xb128-coslr-100e_in1k-milan_20221129-74ac94fa.pth

Models and results

Pretrained models

Model Params (M) Flops (G) Config Download
milan_vit-base-p16_16xb256-amp-coslr-400e_in1k 111.91 17.58 config model | log

Image Classification on ImageNet-1k

Model Pretrain Params (M) Flops (G) Top-1 (%) Config Download
vit-base-p16_milan-pre_8xb128-coslr-100e_in1k MILAN 86.57 17.58 85.30 config model | log
vit-base-p16_milan-pre_8xb2048-linear-coslr-100e_in1k MILAN 86.57 17.58 78.90 config model | log

Citation

@article{Hou2022MILANMI,
  title={MILAN: Masked Image Pretraining on Language Assisted Representation},
  author={Zejiang Hou and Fei Sun and Yen-Kuang Chen and Yuan Xie and S. Y. Kung},
  journal={ArXiv},
  year={2022}
}