Train neural network for semantic segmentation (deep lab V3) with pytorch in less then 50 lines of code

Overview
Official PyTorch implementation of Less is More: Pay Less Attention in Vision Transformers.

Less is More: Pay Less Attention in Vision Transformers Official PyTorch implementation of Less is More: Pay Less Attention in Vision Transformers. By

null 30 Jan 18, 2022
Deploy a ML inference service on a budget in less than 10 lines of code.

BudgetML is perfect for practitioners who would like to quickly deploy their models to an endpoint, but not waste a lot of time, money, and effort trying to figure out how to do this end-to-end.

null 1.3k Jan 10, 2022
Recall Loss for Semantic Segmentation (This repo implements the paper: Recall Loss for Semantic Segmentation)

Recall Loss for Semantic Segmentation (This repo implements the paper: Recall Loss for Semantic Segmentation) Download Synthia dataset The model uses

null 23 Dec 17, 2021
TorchDistiller - a collection of the open source pytorch code for knowledge distillation, especially for the perception tasks, including semantic segmentation, depth estimation, object detection and instance segmentation.

This project is a collection of the open source pytorch code for knowledge distillation, especially for the perception tasks, including semantic segmentation, depth estimation, object detection and instance segmentation.

yifan liu 102 Jan 17, 2022
Use MATLAB to simulate the signal and extract features. Use PyTorch to build and train deep network to do spectrum sensing.

Deep-Learning-based-Spectrum-Sensing Use MATLAB to simulate the signal and extract features. Use PyTorch to build and train deep network to do spectru

null 5 Jan 17, 2022
The (Official) PyTorch Implementation of the paper "Deep Extraction of Manga Structural Lines"

MangaLineExtraction_PyTorch The (Official) PyTorch Implementation of the paper "Deep Extraction of Manga Structural Lines" Usage model_torch.py [sourc

Miaomiao Li 55 Jan 14, 2022
Learning recognition/segmentation models without end-to-end training. 40%-60% less GPU memory footprint. Same training time. Better performance.

InfoPro-Pytorch The Information Propagation algorithm for training deep networks with local supervision. (ICLR 2021) Revisiting Locally Supervised Lea

null 74 Dec 16, 2021
SparseML is a libraries for applying sparsification recipes to neural networks with a few lines of code, enabling faster and smaller models

SparseML is a toolkit that includes APIs, CLIs, scripts and libraries that apply state-of-the-art sparsification algorithms such as pruning and quantization to any neural network. General, recipe-driven approaches built around these algorithms enable the simplification of creating faster and smaller models for the ML performance community at large.

Neural Magic 793 Jan 19, 2022
Learning Pixel-level Semantic Affinity with Image-level Supervision for Weakly Supervised Semantic Segmentation, CVPR 2018

Learning Pixel-level Semantic Affinity with Image-level Supervision This code is deprecated. Please see https://github.com/jiwoon-ahn/irn instead. Int

Jiwoon Ahn 301 Jan 10, 2022
This is project is the implementation of the DeepShift: Towards Multiplication-Less Neural Networks paper

DeepShift This is project is the implementation of the DeepShift: Towards Multiplication-Less Neural Networks paper, that aims to replace multiplicati

Mostafa Elhoushi 79 Jan 12, 2022
Bayesian-Torch is a library of neural network layers and utilities extending the core of PyTorch to enable the user to perform stochastic variational inference in Bayesian deep neural networks

Bayesian-Torch is a library of neural network layers and utilities extending the core of PyTorch to enable the user to perform stochastic variational inference in Bayesian deep neural networks. Bayesian-Torch is designed to be flexible and seamless in extending a deterministic deep neural network architecture to corresponding Bayesian form by simply replacing the deterministic layers with Bayesian layers.

Intel Labs 96 Jan 13, 2022
A PyTorch implementation of "Predict then Propagate: Graph Neural Networks meet Personalized PageRank" (ICLR 2019).

APPNP ⠀ A PyTorch implementation of Predict then Propagate: Graph Neural Networks meet Personalized PageRank (ICLR 2019). Abstract Neural message pass

Benedek Rozemberczki 287 Jan 14, 2022
Segmentation in Style: Unsupervised Semantic Image Segmentation with Stylegan and CLIP

Segmentation in Style: Unsupervised Semantic Image Segmentation with Stylegan and CLIP Abstract: We introduce a method that allows to automatically se

Daniil Pakhomov 103 Jan 14, 2022
A PyTorch implementation of EventProp [https://arxiv.org/abs/2009.08378], a method to train Spiking Neural Networks

Spiking Neural Network training with EventProp This is an unofficial PyTorch implemenation of EventProp, a method to compute exact gradients for Spiki

Pedro Savarese 33 Nov 4, 2021
(CVPR2021) DANNet: A One-Stage Domain Adaptation Network for Unsupervised Nighttime Semantic Segmentation

DANNet: A One-Stage Domain Adaptation Network for Unsupervised Nighttime Semantic Segmentation CVPR2021(oral) [arxiv] Requirements python3.7 pytorch==

W-zx-Y 54 Jan 17, 2022
Implementation of ICCV2021(Oral) paper - VMNet: Voxel-Mesh Network for Geodesic-aware 3D Semantic Segmentation

VMNet: Voxel-Mesh Network for Geodesic-Aware 3D Semantic Segmentation Created by Zeyu HU Introduction This work is based on our paper VMNet: Voxel-Mes

HU Zeyu 69 Jan 5, 2022