# MFF > [Improving Pixel-based MIM by Reducing Wasted Modeling Capability](https://arxiv.org/abs/2308.00261) ## Abstract There has been significant progress in Masked Image Modeling (MIM). Existing MIM methods can be broadly categorized into two groups based on the reconstruction target: pixel-based and tokenizer-based approaches. The former offers a simpler pipeline and lower computational cost, but it is known to be biased toward high-frequency details. In this paper, we provide a set of empirical studies to confirm this limitation of pixel-based MIM and propose a new method that explicitly utilizes low-level features from shallow layers to aid pixel reconstruction. By incorporating this design into our base method, MAE, we reduce the wasted modeling capability of pixel-based MIM, improving its convergence and achieving non-trivial improvements across various downstream tasks. To the best of our knowledge, we are the first to systematically investigate multi-level feature fusion for isotropic architectures like the standard Vision Transformer (ViT). Notably, when applied to a smaller model (e.g., ViT-S), our method yields significant performance gains, such as 1.2% on fine-tuning, 2.8% on linear probing, and 2.6% on semantic segmentation.
**Train/Test Command** Prepare your dataset according to the [docs](https://mmpretrain.readthedocs.io/en/latest/user_guides/dataset_prepare.html#prepare-dataset). Train: ```shell python tools/train.py configs/mff/mff_vit-base-p16_8xb512-amp-coslr-300e_in1k.py ``` Test: ```shell python tools/test.py configs/mff/benchmarks/vit-base-p16_8xb128-coslr-100e_in1k.py None ``` ## Models and results ### Pretrained models | Model | Params (M) | Flops (G) | Config | Download | | :-------------------------------------------- | :--------: | :-------: | :------------------------------------------------------: | :------------------------------------------------------------------------------: | | `mff_vit-base-p16_8xb512-amp-coslr-300e_in1k` | - | - | [config](mff_vit-base-p16_8xb512-amp-coslr-300e_in1k.py) | [model](https://download.openmmlab.com/mmpretrain/v1.0/mff/mff_vit-base-p16_8xb512-amp-coslr-300e_in1k/mff_vit-base-p16_8xb512-amp-coslr-300e_in1k_20230801-3c1bcce4.pth) \| [log](https://download.openmmlab.com/mmpretrain/v1.0/mff/mff_vit-base-p16_8xb512-amp-coslr-300e_in1k/mff_vit-base-p16_8xb512-amp-coslr-300e_in1k_20230801-3c1bcce4.json) | | `mff_vit-base-p16_8xb512-amp-coslr-800e_in1k` | - | - | [config](mff_vit-base-p16_8xb512-amp-coslr-300e_in1k.py) | [model](https://download.openmmlab.com/mmpretrain/v1.0/mff/mff_vit-base-p16_8xb512-amp-coslr-800e_in1k/mff_vit-base-p16_8xb512-amp-coslr-800e_in1k_20230801-3af7cd9d.pth) \| [log](https://download.openmmlab.com/mmpretrain/v1.0/mff/mff_vit-base-p16_8xb512-amp-coslr-800e_in1k/mff_vit-base-p16_8xb512-amp-coslr-800e_in1k_20230801-3af7cd9d.json) | ### Image Classification on ImageNet-1k | Model | Pretrain | Params (M) | Flops (G) | Top-1 (%) | Config | Download | | :---------------------------------------- | :------------------------------------------: | :--------: | :-------: | :-------: | :----------------------------------------: | :-------------------------------------------: | | `vit-base-p16_mff-300e-pre_8xb128-coslr-100e_in1k` | [MFF 300-Epochs](https://download.openmmlab.com/mmpretrain/v1.0/mff/mff_vit-base-p16_8xb512-amp-coslr-300e_in1k/mff_vit-base-p16_8xb512-amp-coslr-300e_in1k_20230801-3c1bcce4.pth) | 86.57 | 17.58 | 83.00 | [config](benchmarks/vit-base-p16_8xb128-coslr-100e_in1k.py) | [model](https://download.openmmlab.com/mmpretrain/v1.0/mff/mff_vit-base-p16_8xb512-amp-coslr-300e_in1k/vit-base-p16_8xb128-coslr-100e_in1k/vit-base-p16_8xb128-coslr-100e_in1k_20230802-d746fdb7.pth) / [log](https://download.openmmlab.com/mmpretrain/v1.0/mff/mff_vit-base-p16_8xb512-amp-coslr-300e_in1k/vit-base-p16_8xb128-coslr-100e_in1k/vit-base-p16_8xb128-coslr-100e_in1k_20230802-d746fdb7.json) | | `vit-base-p16_mff-800e-pre_8xb128-coslr-100e_in1k` | [MFF 800-Epochs](https://download.openmmlab.com/mmpretrain/v1.0/mff/mff_vit-base-p16_8xb512-amp-coslr-800e_in1k/mff_vit-base-p16_8xb512-amp-coslr-800e_in1k_20230801-3af7cd9d.pth) | 86.57 | 17.58 | 83.70 | [config](benchmarks/vit-base-p16_8xb128-coslr-100e_in1k.py) | [model](https://download.openmmlab.com/mmpretrain/v1.0/mff/mff_vit-base-p16_8xb512-amp-coslr-800e_in1k/vit-base-p16_8xb128-coslr-100e/vit-base-p16_8xb128-coslr-100e_20230802-6780e47d.pth) / [log](https://download.openmmlab.com/mmpretrain/v1.0/mff/mff_vit-base-p16_8xb512-amp-coslr-800e_in1k/vit-base-p16_8xb128-coslr-100e/vit-base-p16_8xb128-coslr-100e_20230802-6780e47d.json) | | `vit-base-p16_mff-300e-pre_8xb2048-linear-coslr-90e_in1k` | [MFF 300-Epochs](https://download.openmmlab.com/mmpretrain/v1.0/mff/mff_vit-base-p16_8xb512-amp-coslr-300e_in1k/mff_vit-base-p16_8xb512-amp-coslr-300e_in1k_20230801-3c1bcce4.pth) | 304.33 | 61.60 | 64.20 | [config](benchmarks/vit-base-p16_8xb128-coslr-100e_in1k.py) | [log](https://download.openmmlab.com/mmpretrain/v1.0/mff/mff_vit-base-p16_8xb512-amp-coslr-300e_in1k/vit-base-p16_8xb2048-linear-coslr-90e_in1k/vit-base-p16_8xb2048-linear-coslr-90e_in1k.json) | | `vit-base-p16_mff-800e-pre_8xb2048-linear-coslr-90e_in1k` | [MFF 800-Epochs](https://download.openmmlab.com/mmselfsup/1.x/mae/mae_vit-base-p16_8xb512-fp16-coslr-1600e_in1k/mae_vit-base-p16_8xb512-fp16-coslr-1600e_in1k_20220825-f7569ca2.pth) | 304.33 | 61.60 | 68.30 | [config](benchmarks/vit-base-p16_8xb128-coslr-100e_in1k.py) | [model](https://download.openmmlab.com/mmpretrain/v1.0/mff/mff_vit-base-p16_8xb512-amp-coslr-800e_in1k/vit-base-p16_8xb2048-linear-coslr-90e/vit-base-p16_8xb2048-linear-coslr-90e_20230802-6b1f7bc8.pth) / [log](https://download.openmmlab.com/mmpretrain/v1.0/mff/mff_vit-base-p16_8xb512-amp-coslr-800e_in1k/vit-base-p16_8xb2048-linear-coslr-90e/vit-base-p16_8xb2048-linear-coslr-90e_20230802-6b1f7bc8.json) | ## Citation ```bibtex @article{MFF, title={Improving Pixel-based MIM by Reducing Wasted Modeling Capability}, author={Yuan Liu, Songyang Zhang, Jiacheng Chen, Zhaohui Yu, Kai Chen, Dahua Lin}, journal={arXiv}, year={2023} } ```