LeViT_UNet

For medical image segmentation

Our model is based on LeViT (https://github.com/facebookresearch/LeViT). You'd better gitclone its codes. Thanks for its great job.
There are three models which can be build directly. They are LeViT_UNet_128s, LeViT_UNet_192, and LeViT_UNet_384. You can build one of the models as it follows:

model = Build_LeViT_UNet_192(num_classes=9, pretrained=True)

model.eval()

output = model(torch.randn(1, 1, 224, 224))

If you use this code for a paper please cite:

@article{LeViT-UNet,
  author    = {Guoping Xu and
               Xingrong Wu and
               Xuan Zhang and
               Xinwei He},
  title     = {LeViT-UNet: Make Faster Encoders with Transformer for Medical Image
               Segmentation},
  journal   = {CoRR},
  volume    = {abs/2107.08623},
  year      = {2021},
  url       = {https://arxiv.org/abs/2107.08623},
  eprinttype = {arXiv},
  eprint    = {2107.08623},
  timestamp = {Thu, 22 Jul 2021 11:14:11 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2107-08623.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

For medical image segmentation

Related tags

Overview

LeViT_UNet

Owner

Neural network chess engine trained on Gary Kasparov's games.

pytorch implementation of "Contrastive Multiview Coding", "Momentum Contrast for Unsupervised Visual Representation Learning", and "Unsupervised Feature Learning via Non-Parametric Instance-level Discrimination"

Exploiting a Zoo of Checkpoints for Unseen Tasks

Human Dynamics from Monocular Video with Dynamic Camera Movements

Automatically replace ONNX's RandomNormal node with Constant node.

Adaptive Graph Convolution for Point Cloud Analysis

Pywonderland - A tour in the wonderland of math with python.

ShuttleNet: Position-aware Fusion of Rally Progress and Player Styles for Stroke Forecasting in Badminton (AAAI 2022)

This project helps to colorize grayscale images using multiple exemplars.

Pytorch implementation of Compressive Transformers, from Deepmind

Neural network pruning for finding a sparse computational model for controlling a biological motor task.

The object detection pipeline is based on Ultralytics YOLOv5

Implementation of "A Deep Learning Loss Function based on Auditory Power Compression for Speech Enhancement" by pytorch

Scheduling BilinearRewards

PyTorch implementation of SCAFFOLD (Stochastic Controlled Averaging for Federated Learning, ICML 2020).

PPO Lagrangian in JAX

Position detection system of mobile robot in the warehouse enviroment

rliable is an open-source Python library for reliable evaluation, even with a handful of runs, on reinforcement learning and machine learnings benchmarks.

a reimplementation of LiteFlowNet in PyTorch that matches the official Caffe version

A Vision Transformer approach that uses concatenated query and reference images to learn the relationship between query and reference images directly.