license: apache-2.0
datasets:
- Moai633/7653_plant
language:
- en
metrics:
- accuracy
base_model:
- microsoft/swin-tiny-patch4-window7-224
pipeline_tag: image-classification
library_name: adapter-transformers
tags:
- swin-transformers
- basil
Swin Transformer
This repo is the official implementation of "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows" as well as the follow-ups. It currently includes code and models for the following tasks:
Image Classification: Included in this repo. See get_started.md for a quick start.
Integrated into Huggingface Spaces 🤗 using Gradio. Try out the Web Demo
Using Swin-Transformer as the backbone for self-supervised learning enables us to evaluate the transferring performance of the learnt representations on down-stream tasks, which is missing in previous works due to the use of ViT/DeiT, which has not been well tamed for down-stream tasks.
Pretrained models on ImageNet-1K (Swin-T-IN1K, Swin-S-IN1K, Swin-B-IN1K) and ImageNet-22K (Swin-B-IN22K, Swin-L-IN22K) are provided.
Introduction
Swin Transformer (the name Swin stands for Shifted window) is initially described in arxiv, which capably serves as a
general-purpose backbone for computer vision. It is basically a hierarchical Transformer whose representation is
computed with shifted windows. The shifted windowing scheme brings greater efficiency by limiting self-attention
computation to non-overlapping local windows while also allowing for cross-window connection.
Swin Transformer achieves strong performance on COCO object detection (58.7 box AP and 51.1 mask AP on test-dev) and
ADE20K semantic segmentation (53.5 mIoU on val), surpassing previous models by a large margin.
Main Results on ImageNet with Pretrained Models
| Swin-B | HTC++ | ImageNet-22K | 6x | 56.4 | 49.1 | 160M | 1043G | | Swin-L | HTC++ | ImageNet-22K | 3x | 57.1 | 49.5 | 284M | 1470G | | Swin-L | HTC++* | ImageNet-22K | 3x | 58.0 | 50.4 | 284M | - |
Citing Swin Transformer
@inproceedings{liu2021Swin,
title={Swin Transformer: Hierarchical Vision Transformer using Shifted Windows},
author={Liu, Ze and Lin, Yutong and Cao, Yue and Hu, Han and Wei, Yixuan and Zhang, Zheng and Lin, Stephen and Guo, Baining},
booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
year={2021}
}
Getting Started
- For Image Classification, please see get_started.md for detailed instructions.
- For Object Detection and Instance Segmentation, please see Swin Transformer for Object Detection.
- For Semantic Segmentation, please see Swin Transformer for Semantic Segmentation.
- For Self-Supervised Learning, please see Transformer-SSL.
- For Video Recognition, please see Video Swin Transformer.
Contributing
This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.
When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.
This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact [email protected] with any additional questions or comments.
Trademarks
This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft's Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies.