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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows".
| Date | Stars |
|---|---|
| 2026-07-24 | 16010 |
| 2026-07-25 | 16010 |
| 2026-07-28 | 16010 |
| 2026-07-30 | 16010 |
| 2026-08-06 | 16010 |
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# Swin Transformer [](https://paperswithcode.com/sota/object-detection-on-coco?p=swin-transformer-v2-scaling-up-capacity-and) [](https://paperswithcode.com/sota/instance-segmentation-on-coco?p=swin-transformer-v2-scaling-up-capacity-and) [](https://paperswithcode.com/sota/semantic-segmentation-on-ade20k?p=swin-transformer-v2-scaling-up-capacity-and) [](https://paperswithcode.com/sota/action-classification-on-kinetics-400?p=swin-transformer-v2-scaling-up-capacity-and) This repo is the official implementation of ["Swin Transformer: Hierarchical Vision Transformer using Shifted Windows"](https://arxiv.org/pdf/2103.14030.pdf) 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](get_started.md) for a quick start. > **Object Detection and Instance Segmentation**: See [Swin Transformer for Object Detection](https://github.com/SwinTransformer/Swin-Transformer-Object-Detection). > **Semantic Segmentation**: See [Swin Transformer for Semantic Segmentation](https://github.com/SwinTransformer/Swin-Transformer-Semantic-Segmentation). > **Video Action Recognition**: See [Video Swin Transformer](https://github.com/SwinTransformer/Video-Swin-Transformer). > **Semi-Supervised Object Detection**: See [Soft Teacher](https://github.com/microsoft/SoftTeacher). > **SSL: Contrasitive Learning**: See [Transformer-SSL](https://github.com/SwinTransformer/Transformer-SSL). > **SSL: Masked Image Modeling**: See [get_started.md#simmim-support](https://github.com/microsoft/Swin-Transformer/blob/main/get_started.md#simmim-support). > **Mixture-of-Experts**: See [get_started](get_started.md#mixture-of-experts-support) for more instructions. > **Feature-Distillation**: See [Feature-Distillation](https://github.com/SwinTransformer/Feature-Distillation). ## Updates ***12/29/2022*** 1. **Nvidia**'s [FasterTransformer](https://github.com/NVIDIA/FasterTransformer/blob/main/docs/swin_guide.md) now supports Swin Transformer V2 inference, which have significant speed improvements on `T4 and A100 GPUs`. ***11/30/2022*** 1. Models and codes of **Feature Distillation** are released. Please refer to [Feature-Distillation](https://github.com/SwinTransformer/Feature-Distillation) for details, and the checkpoints (FD-EsViT-Swin-B, FD-DeiT-ViT-B, FD-DINO-ViT-B, FD-CLIP-ViT-B, FD-CLIP-ViT-L). ***09/24/2022*** 1. Merged [SimMIM](https://github.com/microsoft/SimMIM), which is a **Masked Image Modeling** based pre-training approach applicable to Swin and SwinV2 (and also applicable for ViT and ResNet). Please refer to [get started with SimMIM](get_started.md#simmim-support) to play with SimMIM pre-training. 2. Released a series of Swin and SwinV2 models pre-trained using the SimMIM approach (see [MODELHUB for SimMIM](MODELHUB.md#simmim-pretrained-swin-v2-models)), with model size ranging from SwinV2-Small-50M to SwinV2-giant-1B, data size ranging from ImageNet-1K-10% to ImageNet-22K, and iterations from 125k to 500k. You may leverage these models to study the properties of MIM methods. Please look into the [data scaling](https://arxiv.org/abs/2206.04664) paper for more details. ***07/09/2022*** `News`: 1. SwinV2-G achieves `61.4 mIoU` on ADE20K semantic segmentation (+1.5 mIoU over the previous SwinV2-G model), using an additional [feature
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:f17294c1ac4640a2, topic:object-detection, topic:image-classification, readme:object detection