#python #ade20k #image_classification #imagenet #mask_rcnn #mscoco #object_detection #semantic_segmentation #swin_transformer
https://github.com/microsoft/Swin-Transformer
https://github.com/microsoft/Swin-Transformer
GitHub
GitHub - microsoft/Swin-Transformer: This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer…
This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows". - microsoft/Swin-Transformer
#jupyter_notebook #ade20k #cityscapes #coco #image_segmentation #instance_segmentation #oneformer #panoptic_segmentation #semantic_segmentation #transformer #universal_segmentation
https://github.com/SHI-Labs/OneFormer
https://github.com/SHI-Labs/OneFormer
GitHub
GitHub - SHI-Labs/OneFormer: [CVPR 2023] OneFormer: One Transformer to Rule Universal Image Segmentation
[CVPR 2023] OneFormer: One Transformer to Rule Universal Image Segmentation - SHI-Labs/OneFormer
#python #ade20k #image_classification #imagenet #mask_rcnn #mscoco #object_detection #semantic_segmentation #swin_transformer
The Swin Transformer is a powerful tool for computer vision tasks like image classification, object detection, semantic segmentation, and video recognition. It uses a hierarchical structure with shifted windows to efficiently process images, making it more efficient than other models. Here are the key benefits Swin Transformer achieves state-of-the-art results in various tasks such as COCO object detection, ADE20K semantic segmentation, and ImageNet classification.
- **Efficiency** The model supports multiple tasks including image classification, object detection, instance segmentation, semantic segmentation, and video action recognition.
- **Improved Speed** The model is integrated into popular frameworks like Hugging Face Spaces and PaddleClas, making it easy to use and deploy.
Overall, the Swin Transformer offers high accuracy, efficiency, and versatility, making it a valuable tool for various computer vision applications.
https://github.com/microsoft/Swin-Transformer
The Swin Transformer is a powerful tool for computer vision tasks like image classification, object detection, semantic segmentation, and video recognition. It uses a hierarchical structure with shifted windows to efficiently process images, making it more efficient than other models. Here are the key benefits Swin Transformer achieves state-of-the-art results in various tasks such as COCO object detection, ADE20K semantic segmentation, and ImageNet classification.
- **Efficiency** The model supports multiple tasks including image classification, object detection, instance segmentation, semantic segmentation, and video action recognition.
- **Improved Speed** The model is integrated into popular frameworks like Hugging Face Spaces and PaddleClas, making it easy to use and deploy.
Overall, the Swin Transformer offers high accuracy, efficiency, and versatility, making it a valuable tool for various computer vision applications.
https://github.com/microsoft/Swin-Transformer
GitHub
GitHub - microsoft/Swin-Transformer: This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer…
This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows". - microsoft/Swin-Transformer