cheerss/CrossFormer
CrossFormer++ is a vision transformer enabling cross-scale attention for object detection, instance segmentation, and semantic segmentation.
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This repository contains PyTorch implementations of CrossFormer and CrossFormer++, versatile vision transformer architectures designed to build attention across features of different scales. The core innovations include Cross-scale Embedding Layer (CEL) and Long-Short Distance Attention (L/SDA) modules. The implementation supports multiple vision tasks including classification, object detection with Mask-RCNN and Cascade Mask-RCNN, instance segmentation, and semantic segmentation, with pretrained models across Small, Base, Large, and Huge variants.
Frequently asked
- What is cheerss/CrossFormer?
- CrossFormer++ is a vision transformer enabling cross-scale attention for object detection, instance segmentation, and semantic segmentation.
- Is CrossFormer open source?
- Yes — cheerss/CrossFormer is open source, released under the MIT license.
- What language is CrossFormer written in?
- cheerss/CrossFormer is primarily written in Python.
- How popular is CrossFormer?
- cheerss/CrossFormer has 403 stars on GitHub.
- Where can I find CrossFormer?
- cheerss/CrossFormer is on GitHub at https://github.com/cheerss/CrossFormer.