insitro/ChannelViT
Channel Vision Transformers: An Image Is Worth C x 16 x 16 Words
This project helps scientists and researchers analyze multi-channel images like those from microscopy or satellite imaging. It takes in images with several distinct information channels, even if some channels are missing, and outputs robust representations for image analysis. It is designed for researchers, biologists, materials scientists, or anyone working with complex, multi-channel visual data.
No commits in the last 6 months.
Use this if you need to analyze images from fields like microscopy or satellite imaging, where each image has multiple data channels and some channels might not always be available.
Not ideal if you are primarily working with standard RGB images or single-channel images, as its core benefit is handling complex multi-channel data.
Stars
71
Forks
8
Language
Python
License
—
Category
Last pushed
Feb 22, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/computer-vision/insitro/ChannelViT"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
BR-IDL/PaddleViT
:robot: PaddleViT: State-of-the-art Visual Transformer and MLP Models for PaddlePaddle 2.0+
pathak22/unsupervised-video
[CVPR 2017] Unsupervised deep learning using unlabelled videos on the web
IBM/CrossViT
Official implementation of CrossViT. https://arxiv.org/abs/2103.14899
NVlabs/GCVit
[ICML 2023] Official PyTorch implementation of Global Context Vision Transformers
ViTAE-Transformer/ViTDet
Unofficial implementation for [ECCV'22] "Exploring Plain Vision Transformer Backbones for Object...