Initial commit

CODE_OF_CONDUCT.md

@@ -0,0 +1,80 @@
+# Code of Conduct
+
+## Our Pledge
+
+In the interest of fostering an open and welcoming environment, we as
+contributors and maintainers pledge to make participation in our project and
+our community a harassment-free experience for everyone, regardless of age, body
+size, disability, ethnicity, sex characteristics, gender identity and expression,
+level of experience, education, socio-economic status, nationality, personal
+appearance, race, religion, or sexual identity and orientation.
+
+## Our Standards
+
+Examples of behavior that contributes to creating a positive environment
+include:
+
+* Using welcoming and inclusive language
+* Being respectful of differing viewpoints and experiences
+* Gracefully accepting constructive criticism
+* Focusing on what is best for the community
+* Showing empathy towards other community members
+
+Examples of unacceptable behavior by participants include:
+
+* The use of sexualized language or imagery and unwelcome sexual attention or
+advances
+* Trolling, insulting/derogatory comments, and personal or political attacks
+* Public or private harassment
+* Publishing others' private information, such as a physical or electronic
+address, without explicit permission
+* Other conduct which could reasonably be considered inappropriate in a
+professional setting
+
+## Our Responsibilities
+
+Project maintainers are responsible for clarifying the standards of acceptable
+behavior and are expected to take appropriate and fair corrective action in
+response to any instances of unacceptable behavior.
+
+Project maintainers have the right and responsibility to remove, edit, or
+reject comments, commits, code, wiki edits, issues, and other contributions
+that are not aligned to this Code of Conduct, or to ban temporarily or
+permanently any contributor for other behaviors that they deem inappropriate,
+threatening, offensive, or harmful.
+
+## Scope
+
+This Code of Conduct applies within all project spaces, and it also applies when
+an individual is representing the project or its community in public spaces.
+Examples of representing a project or community include using an official
+project e-mail address, posting via an official social media account, or acting
+as an appointed representative at an online or offline event. Representation of
+a project may be further defined and clarified by project maintainers.
+
+This Code of Conduct also applies outside the project spaces when there is a
+reasonable belief that an individual's behavior may have a negative impact on
+the project or its community.
+
+## Enforcement
+
+Instances of abusive, harassing, or otherwise unacceptable behavior may be
+reported by contacting the project team at <opensource-conduct@fb.com>. All
+complaints will be reviewed and investigated and will result in a response that
+is deemed necessary and appropriate to the circumstances. The project team is
+obligated to maintain confidentiality with regard to the reporter of an incident.
+Further details of specific enforcement policies may be posted separately.
+
+Project maintainers who do not follow or enforce the Code of Conduct in good
+faith may face temporary or permanent repercussions as determined by other
+members of the project's leadership.
+
+## Attribution
+
+This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4,
+available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html
+
+[homepage]: https://www.contributor-covenant.org
+
+For answers to common questions about this code of conduct, see
+https://www.contributor-covenant.org/faq

CONTRIBUTING.md

@@ -0,0 +1,31 @@
+# Contributing to ConvNeXt
+We want to make contributing to this project as easy and transparent as
+possible.
+
+## Pull Requests
+We actively welcome your pull requests.
+
+1. Fork the repo and create your branch from `main`.
+2. If you've added code that should be tested, add tests.
+3. If you've changed APIs, update the documentation.
+4. Ensure the test suite passes.
+5. Make sure your code lints.
+6. If you haven't already, complete the Contributor License Agreement ("CLA").
+
+## Contributor License Agreement ("CLA")
+In order to accept your pull request, we need you to submit a CLA. You only need
+to do this once to work on any of Meta's open source projects.
+
+Complete your CLA here: <https://code.facebook.com/cla>
+
+## Issues
+We use GitHub issues to track public bugs. Please ensure your description is
+clear and has sufficient instructions to be able to reproduce the issue.
+
+Meta has a [bounty program](https://www.facebook.com/whitehat/) for the safe
+disclosure of security bugs. In those cases, please go through the process
+outlined on that page and do not file a public issue.
+
+## License
+By contributing to ConvNeXt, you agree that your contributions will be licensed
+under the LICENSE file in the root directory of this source tree.

INSTALL.md

@@ -0,0 +1,53 @@
+# Installation
+
+We provide installation instructions for ImageNet classification experiments here.
+
+## Dependency Setup
+Create an new conda virtual environment
+```
+conda create -n convnext python=3.8 -y
+conda activate convnext
+```
+
+Install [Pytorch](https://pytorch.org/)>=1.8.0, [torchvision](https://pytorch.org/vision/stable/index.html)>=0.9.0 following official instructions. For example:
+```
+pip install torch==1.8.0+cu111 torchvision==0.9.0+cu111 -f https://download.pytorch.org/whl/torch_stable.html
+```
+
+Clone this repo and install required packages:
+```
+git clone https://github.com/facebookresearch/ConvneXt
+pip install timm==0.3.2 tensorboardX six
+```
+
+The results in the paper are generated with `torch==1.8.0+cu111 torchvision==0.9.0+cu111 timm==0.3.2`.
+
+## Dataset Preparation
+
+Download the [ImageNet-1K](http://image-net.org/) classification dataset and structure the data as follows:
+```
+/path/to/imagenet-1k/
+  train/
+    class1/
+      img1.jpeg
+    class2/
+      img2.jpeg
+  val/
+    class1/
+      img3.jpeg
+    class2/
+      img4.jpeg
+```
+
+For pre-training on [ImageNet-22K](http://image-net.org/), download the dataset and structure the data as follows:
+```
+/path/to/imagenet-22k/
+  class1/
+    img1.jpeg
+  class2/
+    img2.jpeg
+  class1/
+    img3.jpeg
+  class2/
+    img4.jpeg
+```

LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) Meta Platforms, Inc. and affiliates.
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

@@ -0,0 +1,105 @@
+# [A ConvNet for the 2020s](https://arxiv.org/abs/2201.xxxxx)
+
+Official PyTorch implementation of **ConvNeXt**, from the following paper:
+
+[A ConvNet for the 2020s](https://arxiv.org/abs/2201.xxxxx). arXiv 2022.\
+[Zhuang Liu](https://liuzhuang13.github.io), [Hanzi Mao](https://hanzimao.me/), [Chao-Yuan Wu](https://chaoyuan.org/), [Christoph Feichtenhofer](https://feichtenhofer.github.io/), [Trevor Darrell](https://people.eecs.berkeley.edu/~trevor/) and [Saining Xie](https://sainingxie.com)\
+Facebook AI Research, UC Berkeley
+
+--- 
+
+<p align="center">
+<img src="https://user-images.githubusercontent.com/8370623/148624004-e9581042-ea4d-4e10-b3bd-42c92b02053b.png" width=100% height=100% 
+class="center">
+</p>
+
+We propose **ConvNeXt**, a pure ConvNet model constructed entirely from standard ConvNet modules. ConvNeXt is accurate, efficient, scalable and very simple in design.
+
+## Catalog
+- [x] ImageNet-1K Training Code  
+- [x] ImageNet-22K Pre-training Code  
+- [x] ImageNet-1K Fine-tuning Code  
+- [x] Downstream Transfer (Detection, Segmentation) Code
+<!-- ✅ ⬜️  -->
+
+## Results and Pre-trained Models
+### ImageNet-1K trained models
+
+| name | resolution |acc@1 | #params | FLOPs | model |
+|:---:|:---:|:---:|:---:| :---:|:---:|
+| ConvNeXt-T | 224x224 | 82.1 | 28M | 4.5G | [model](https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth) |
+| ConvNeXt-S | 224x224 | 83.1 | 50M | 8.7G | [model](https://dl.fbaipublicfiles.com/convnext/convnext_small_1k_224_ema.pth) |
+| ConvNeXt-B | 224x224 | 83.8 | 89M | 15.4G | [model](https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_224_ema.pth) |
+| ConvNeXt-B | 384x384 | 85.1 | 89M | 45.0G | [model](https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_384.pth) |
+| ConvNeXt-L | 224x224 | 84.3 | 198M | 34.4G | [model](https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_224_ema.pth) |
+| ConvNeXt-L | 384x384 | 85.5 | 198M | 101.0G | [model](https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_384.pth) |
+
+### ImageNet-22K trained models
+
+| name | resolution |acc@1 | #params | FLOPs | 22k model | 1k model |
+|:---:|:---:|:---:|:---:| :---:| :---:|:---:|
+| ConvNeXt-B | 224x224 | 85.8 | 89M | 15.4G | [model](https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_224.pth)   | [model](https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_224.pth)
+| ConvNeXt-B | 384x384 | 86.8 | 89M | 47.0G |     -          | [model](https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_384.pth)
+| ConvNeXt-L | 224x224 | 86.6 | 198M | 34.4G | [model](https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_224.pth)  | [model](https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_224.pth)
+| ConvNeXt-L | 384x384 | 87.5 | 198M | 101.0G |    -         | [model](https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_384.pth)
+| ConvNeXt-XL | 224x224 | 87.0 | 350M | 60.9G | [model](https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_224.pth) | [model](https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_224_ema.pth)
+| ConvNeXt-XL | 384x384 | 87.8 | 350M | 179.0G |  -          | [model](https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_384_ema.pth)
+
+
+### ImageNet-1K trained models (isotropic)
+| name | resolution |acc@1 | #params | FLOPs | model |
+|:---:|:---:|:---:|:---:| :---:|:---:|
+| ConvNeXt-S | 224x224 | 78.7 | 22M | 4.3G | [model](https://dl.fbaipublicfiles.com/convnext/convnext_iso_small_1k_224_ema.pth) |
+| ConvNeXt-B | 224x224 | 82.0 | 87M | 16.9G | [model](https://dl.fbaipublicfiles.com/convnext/convnext_iso_base_1k_224_ema.pth) |
+| ConvNeXt-L | 224x224 | 82.6 | 306M | 59.7G | [model](https://dl.fbaipublicfiles.com/convnext/convnext_iso_large_1k_224_ema.pth) |
+
+
+## Installation
+Please check [INSTALL.md](INSTALL.md) for installation instructions. 
+
+## Evaluation
+We give an example evaluation command for a ImageNet-22K pre-trained, then ImageNet-1K fine-tuned ConvNeXt-B:
+
+Single-GPU
+```
+python main.py --model convnext_base --eval true \
+--resume https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_224.pth \
+--input_size 224 --drop_path 0.2 \
+--data_path /path/to/imagenet-1k
+```
+Multi-GPU
+```
+python -m torch.distributed.launch --nproc_per_node=8 main.py \
+--model convnext_base --eval true \
+--resume https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_224.pth \
+--input_size 224 --drop_path 0.2 \
+--data_path /path/to/imagenet-1k
+```
+
+This should give 
+```
+* Acc@1 85.820 Acc@5 97.868 loss 0.563
+```
+
+- For evaluating other model variants, change `--model`, `--resume`, `--input_size` accordingly. You can get the url to pre-trained models from the tables above. 
+- Setting model-specific `--drop_path` is not strictly required in evaluation, as the `DropPath` module in timm behaves the same during evaluation; but it is required in training. See [TRAINING.md](TRAINING.md) or our paper for the values used for different models.
+
+## Training
+See [TRAINING.md](TRAINING.md) for training and fine-tuning instructions.
+
+## Acknowledgement
+This repository is built using the [timm](https://github.com/rwightman/pytorch-image-models) library, [DeiT](https://github.com/facebookresearch/deit) and [BEiT](https://github.com/microsoft/unilm/tree/master/beit) repositories.
+
+## License
+This project is released under the MIT license. Please see the [LICENSE](LICENSE) file for more information.
+
+## Citation
+If you find this repository helpful, please consider citing:
+```
+@Article{liu2021convnet,
+  author  = {Zhuang Liu and Hanzi Mao and Chao-Yuan Wu and Christoph Feichtenhofer and Trevor Darrell and Saining Xie},
+  title   = {A ConvNet for the 2020s},
+  journal = {arXiv preprint arXiv:2201.xxxxx},
+  year    = {2022},
+}
+```