#python #ade20k #image_classification #imagenet #mask_rcnn #mscoco #object_detection #semantic_segmentation #swin_transformer

https://github.com/microsoft/Swin-Transformer

#python #image_classification #image_recognition #pretrained_models #knowledge_distillation #product_recognition #autoaugment #cutmix #randaugment #gridmask #deit #repvgg #swin_transformer #image_retrieval_system

https://github.com/PaddlePaddle/PaddleClas

#python #deep_learning #image_classification #imagenet #mobilenet #pytorch #regnet #resnet #resnext #senet #shufflenet #swin_transformer

https://github.com/open-mmlab/mmclassification

#python #deeplabv3 #image_segmentation #medical_image_segmentation #pspnet #pytorch #realtime_segmentation #retinal_vessel_segmentation #semantic_segmentation #swin_transformer #transformer #vessel_segmentation

MMSegmentation is an open-source semantic segmentation toolbox based on PyTorch, part of the OpenMMLab project. It offers a unified benchmark for various semantic segmentation methods, a modular design allowing easy customization, and support for multiple popular segmentation frameworks like PSPNet and DeepLabV3+. The toolbox is highly efficient and provides detailed tutorials, advanced guides, and support for numerous datasets and backbones. This makes it a powerful tool for researchers and developers to implement and develop new semantic segmentation methods efficiently. By using MMSegmentation, you can leverage its flexibility, extensive features, and community support to enhance your projects in image segmentation tasks.

https://github.com/open-mmlab/mmsegmentation

#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