# SparK: the first successful BERT/MAE-style pretraining on *any* convolutional networks [](https://www.reddit.com/r/MachineLearning/comments/10ix0l1/r_iclr2023_spotlight_the_first_bertstyle/) [](https://twitter.com/keyutian/status/1616606179144380422)
This is the official implementation of ICLR paper [Designing BERT for Convolutional Networks: ***Spar***se and Hierarchical Mas***k***ed Modeling](https://arxiv.org/abs/2301.03580), which can pretrain **any CNN** (e.g., ResNet) in a **BERT-style self-supervised** manner.
We've tried our best to make the codebase clean, short, easy to read, state-of-the-art, and only rely on minimal dependencies.
https://user-images.githubusercontent.com/39692511/226858919-dd4ccf7e-a5ba-4a33-ab21-4785b8a7833c.mp4
## News
- A brief introduction (in English) is available on our ICLR poster page! [[`Recorded Video, Poster, and Slides`](https://iclr.cc/virtual/2023/poster/12227)].
- On **May. 11th** another livestream on OpenMMLab & ReadPaper (bilibili)! [[`Recorded Video`](https://www.bilibili.com/video/BV11s4y1M7qL/)]
- On **Apr. 27th (UTC+8 8pm)** another livestream would be held at [OpenMMLab (bilibili)](https://space.bilibili.com/1293512903)!
- On **Mar. 22nd (UTC+8 8pm)** another livestream would be held at 极市平台 (bilibili)! [[`Recorded Video`](https://www.bilibili.com/video/BV1Da4y1T7mr/)]
- The share on [TechBeat (将门创投)](https://www.techbeat.net/talk-info?id=758) is scheduled on **Mar. 16th (UTC+8 8pm)** too! [[`Recorded Video`](https://www.techbeat.net/talk-info?id=758)]
- We are honored to be invited by Synced ("机器之心机动组 视频号" on WeChat) to give a talk about SparK on **Feb. 27th (UTC+0 11am, UTC+8 7pm)**, welcome! [[`Recorded Video`](https://www.bilibili.com/video/BV1J54y1u7U3/)]
- This work got accepted to ICLR 2023 as a Spotlight (notable-top-25%).
- Other articles: [[`Synced`](https://syncedreview.com/2023/01/19/bert-style-pretraining-on-convnets-peking-u-bytedance-oxford-us-sparse-masked-modelling-with-hierarchy-leads-the-way/)]
[[`DeepAI`](https://deepai.org/publication/designing-bert-for-convolutional-networks-sparse-and-hierarchical-masked-modeling)]
[[`TheGradient`](https://thegradientpub.substack.com/p/update-42-ai-news-editors-make-mistakes)]
[[`Bytedance`](https://mp.weixin.qq.com/s/Ak1CeeG83sgO0Wf8KgEIQQ)]
[[`CVers`](https://zhuanlan.zhihu.com/p/598056871)
[[`QbitAI(量子位)`](https://www.qbitai.com/2023/02/42109.html)]
[[`BAAI(智源)`](https://hub.baai.ac.cn/view/23360)]
[[`机器之心机动组`](https://mp.weixin.qq.com/s/Ylek_lf5enYHRTnkEwAFpg)]
[[`极市平台`](https://mp.weixin.qq.com/s/GSVHUtBNw5k5wfn2pbC99Q)]
[[`ReadPaper笔记`](https://readpaper.com/paper/4710371282714116097)]
## ️ Colab Visualization Demo
Check [pretrain/viz_reconstruction.ipynb](pretrain/viz_reconstruction.ipynb) for visualizing the reconstruction of SparK pretrained models, like:
We also provide [pretrain/viz_spconv.ipynb](pretrain/viz_spconv.ipynb) that shows the "mask pattern vanishing" issue of dense conv layers.
## What's new here?
### Pretrained CNN beats pretrained Swin-Transformer:
### After SparK pretraining, smaller models can beat un-pretrained larger models:
### All models can benefit, showing a scaling behavior:
### Generative self-supervised pretraining surpasses contrastive learning:
#### See our [paper](https://arxiv.org/pdf/2301.03580.pdf) for more analysis, discussions, and evaluations.
## Todo list
catalog
- [x] Pretraining code
- [x] Pretraining toturial for customized CNN model ([Tutorial for pretraining your own CNN model](https://github.com/keyu-tian/SparK/tree/main/pretrain/#tutorial-for-pretraining-your-own-cnn-model))
- [x] Pretraining toturial for customized dataset ([Tutorial for pretraining your own dataset](https://github.com/keyu-tian/SparK/tree/main/pretrain/#tutorial-for-pretraining-your-own-dataset))
- [x] Pretraining Colab visualization playground ([reconstruction](/pretrain/viz_reconstruction.ipynb), [sparse conv](pretrain/viz_spconv.ipynb))
- [x] Finetuning code
- [ ] Weights & visualization playground in `huggingface`
- [ ] Weights in `timm`
## Pretrained weights (self-supervised; w/o decoder; can be directly finetuned)
**Note: for network definitions, we directly use `timm.models.ResNet` and [official ConvNeXt](https://github.com/facebookresearch/ConvNeXt/blob/048efcea897d999aed302f2639b6270aedf8d4c8/models/convnext.py).**
`reso.`: the image resolution; `acc@1`: ImageNet-1K finetuned acc (top-1)
| arch. | reso. | acc@1 | #params | flops | weights (self-supervised, without SparK's decoder) |
|:--------------:|:-----:|:-----:|:-------:|:------:|:---------------------------------------------------------------------------------------------------------------------------------------|
| ResNet50 | 224 | 80.6 | 26M | 4.1G | [resnet50_1kpretrained_timm_style.pth](https://drive.google.com/file/d/1H8605HbxGvrsu4x4rIoNr-Wkd7JkxFPQ/view?usp=share_link) |
| ResNet101 | 224 | 82.2 | 45M | 7.9G | [resnet101_1kpretrained_timm_style.pth](https://drive.google.com/file/d/1ZwTztjU-_rfvOVfLoce9SMw2Fx0DQfoO/view?usp=share_link) |
| ResNet152 | 224 | 82.7 | 60M | 11.6G | [resnet152_1kpretrained_timm_style.pth](https://drive.google.com/file/d/1FOVuECnzQAI-OzE-hnrqW7tVpg8kTziM/view?usp=share_link) |
| ResNet200 | 224 | 83.1 | 65M | 15.1G | [resnet200_1kpretrained_timm_style.pth](https://drive.google.com/file/d/1_Q4e30qqhjchrdyW3fT6P98Ga-WnQ57s/view?usp=share_link) |
| ConvNeXt-S | 224 | 84.1 | 50M | 8.7G | [convnextS_1kpretrained_official_style.pth](https://drive.google.com/file/d/1Ah6lgDY5YDNXoXHQHklKKMbEd08RYivN/view?usp=share_link) |
| ConvNeXt-B | 224 | 84.8 | 89M | 15.4G | [convnextB_1kpretrained_official_style.pth](https://drive.google.com/file/d/1ZjWbqI1qoBcqeQijI5xX9E-YNkxpJcYV/view?usp=share_link) |
| ConvNeXt-L | 224 | 85.4 | 198M | 34.4G | [convnextL_1kpretrained_official_style.pth](https://drive.google.com/file/d/1qfYzGUpYBzuA88_kXkVl4KNUwfutMVfw/view?usp=share_link) |
| ConvNeXt-L | 384 | 86.0 | 198M | 101.0G | [convnextL_384_1kpretrained_official_style.pth](https://drive.google.com/file/d/1YgWNXJjI89l35P4ksAmBNWZ2JZCpj9n4/view?usp=share_link) |
Pretrained weights (with SparK's UNet-style decoder; can be used to reconstruct images)
| arch. | reso. | acc@1 | #params | flops | weights (self-supervised, with SparK's decoder) |
|:----------:|:-----:|:-----:|:-------:|:------:|:------------------------------------------------------------------------------------------------------------------------------------------|
| ResNet50 | 224 | 80.6 | 26M | 4.1G | [res50_withdecoder_1kpretrained_spark_style.pth](https://drive.google.com/file/d/1STt3w3e5q9eCPZa8VzcJj1zG6p3jLeSF/view?usp=share_link) |
| ResNet101 | 224 | 82.2 | 45M | 7.9G | [res101_withdecoder_1kpretrained_spark_style.pth](https://drive.google.com/file/d/1GjN48LKtlop2YQre6---7ViCWO-3C0yr/view?usp=share_link) |
| ResNet152 | 224 | 82.7 | 60M | 11.6G | [res152_withdecoder_1kpretrained_spark_style.pth](https://drive.google.com/file/d/1U3Cd94j4ZHfYR2dUjWmsEWfjP6Opx4oo/view?usp=share_link) |
| ResNet200 | 224 | 83.1 | 65M | 15.1G | [res200_withdecoder_1kpretrained_spark_style.pth](https://drive.google.com/file/d/13AFSqvIr0v-2hmb4DzVza45t_lhf2CnD/view?usp=share_link) |
| ConvNeXt-S | 224 | 84.1 | 50M | 8.7G | [cnxS224_withdecoder_1kpretrained_spark_style.pth](https://drive.google.com/file/d/1bKvrE4sNq1PfzhWlQJXEPrl2kHqHRZM-/view?usp=share_link) |
| ConvNeXt-L | 384 | 86.0 | 198M | 101.0G | [cnxL384_withdecoder_1kpretrained_spark_style.pth](https://drive.google.com/file/d/1ZI9Jgtb3fKWE_vDFEly29w-1FWZSNwa0/view?usp=share_link) |
## Installation & Running
We highly recommended you to use `torch==1.10.0`, `torchvision==0.11.1`, and `timm==0.5.4` for reproduction.
Check [INSTALL.md](INSTALL.md) to install all pip dependencies.
- **Loading pretrained model weights in 3 lines**
```python3
# download our weights `resnet50_1kpretrained_timm_style.pth` first
import torch, timm
res50, state = timm.create_model('resnet50'), torch.load('resnet50_1kpretrained_timm_style.pth', 'cpu')
res50.load_state_dict(state.get('module', state), strict=False) # just in case the model weights are actually saved in state['module']
```
- **Pretraining**
- any ResNet or ConvNeXt on ImageNet-1k: see [pretrain/](pretrain)
- **your own CNN model**: see [pretrain/](pretrain), especially [pretrain/models/custom.py](pretrain/models/custom.py)
- **Finetuning**
- any ResNet or ConvNeXt on ImageNet-1k: check [downstream_imagenet/](downstream_imagenet) for subsequent instructions.
- ResNets on COCO: see [downstream_d2/](downstream_d2)
- ConvNeXts on COCO: see [downstream_mmdet/](downstream_mmdet)
## Acknowledgement
We referred to these useful codebases:
- [BEiT](https://github.com/microsoft/unilm/tree/master/beit), [MAE](https://github.com/facebookresearch/mae), [ConvNeXt](https://github.com/facebookresearch/ConvNeXt)
- [timm](https://github.com/rwightman/pytorch-image-models), [MoCoV2](https://github.com/facebookresearch/moco), [Detectron2](https://github.com/facebookresearch/detectron2), [MMDetection](https://github.com/open-mmlab/mmdetection)
## License
This project is under the MIT license. See [LICENSE](LICENSE) for more details.
## Citation
If you found this project useful, you can kindly give us a star ⭐, or cite us in your work :
```
@Article{tian2023designing,
author = {Keyu Tian and Yi Jiang and Qishuai Diao and Chen Lin and Liwei Wang and Zehuan Yuan},
title = {Designing BERT for Convolutional Networks: Sparse and Hierarchical Masked Modeling},
journal = {arXiv:2301.03580},
year = {2023},
}
```