Dynamic Neural Representational Decoders for High-Resolution Semantic Segmentation

Overview

Dynamic Neural Representational Decoders for High-Resolution Semantic Segmentation

Requirements

This repository needs mmsegmentation

Training

To train the model(s) in the paper, run this command:

python tools/train.py ./configs/NRD/ade20k/NRD_r101_512x512_164k_ade20k.py

The batch size is 16 in this work. Please change the 'samples_per_gpu' in configs/base/datasets/.. accordingly

Evaluation

To evaluate my model at single-scale inference, run:

python tools/eval.py ./configs/NRD/ade20k/NRD_r101_512x512_164k_ade20k.py  {path-to-checkpoint-file}   --eval mIoU

Pre-trained Models

Results

Our model achieves the following performance on :

[Semantic segmentation results]

Model name datasets mIoU mIoU (ms)
NRD-r101 ade20k (val) 44.01 45.62
NRD-x101 ade20k (val) 44.34 46.35
NRD-r101 pascal-context(val) 52.31 (59 classes) 54.1 (59 classes)
NRD-r101 pascal-context(val) 47.5 (60 classes) 40.9 (60 classes)
NRD-r50 Cityscapes (val) 79.8 80.8
NRD-r101 Cityscapes (val) 80.7 82.0

Contributing

The code is mostly taken from mmsegmentation mmsegmentation is released under the Apache 2.0 license.

A repository for the paper "Improved Adversarial Systems for 3D Object Generation and Reconstruction".

Improved Adversarial Systems for 3D Object Generation and Reconstruction: This is a repository for the paper "Improved Adversarial Systems for 3D Obje

Edward Smith 188 Dec 25, 2022
EgoNN: Egocentric Neural Network for Point Cloud Based 6DoF Relocalization at the City Scale

EgonNN: Egocentric Neural Network for Point Cloud Based 6DoF Relocalization at the City Scale Paper: EgoNN: Egocentric Neural Network for Point Cloud

19 Sep 20, 2022
Callable PyTrees and filtered JIT/grad transformations => neural networks in JAX.

Equinox Callable PyTrees and filtered JIT/grad transformations = neural networks in JAX Equinox brings more power to your model building in JAX. Repr

Patrick Kidger 909 Dec 30, 2022
A toolset of Python programs for signal modeling and indentification via sparse semilinear autoregressors.

SPAAR Description A toolset of Python programs for signal modeling via sparse semilinear autoregressors. References Vides, F. (2021). Computing Semili

Fredy Vides 0 Oct 30, 2021
PyTorch implementation of the Crafting Better Contrastive Views for Siamese Representation Learning

Crafting Better Contrastive Views for Siamese Representation Learning This is the official PyTorch implementation of the ContrastiveCrop paper: @artic

249 Dec 28, 2022
Implementation of: "Exploring Randomly Wired Neural Networks for Image Recognition"

RandWireNN Unofficial PyTorch Implementation of: Exploring Randomly Wired Neural Networks for Image Recognition. Results Validation result on Imagenet

Seung-won Park 684 Nov 02, 2022
Supervised Contrastive Learning for Downstream Optimized Sequence Representations

SupCL-Seq 📖 Supervised Contrastive Learning for Downstream Optimized Sequence representations (SupCS-Seq) accepted to be published in EMNLP 2021, ext

Hooman Sedghamiz 18 Oct 21, 2022
Code for this paper The Lottery Ticket Hypothesis for Pre-trained BERT Networks.

The Lottery Ticket Hypothesis for Pre-trained BERT Networks Code for this paper The Lottery Ticket Hypothesis for Pre-trained BERT Networks. [NeurIPS

VITA 122 Dec 14, 2022
Pytorch implementation of face attention network

Face Attention Network Pytorch implementation of face attention network as described in Face Attention Network: An Effective Face Detector for the Occ

Hooks 312 Dec 09, 2022
Use Python, OpenCV, and MediaPipe to control a keyboard with facial gestures

CheekyKeys A Face-Computer Interface CheekyKeys lets you control your keyboard using your face. View a fuller demo and more background on the project

69 Nov 09, 2022
Hierarchical probabilistic 3D U-Net, with attention mechanisms (—𝘈𝘵𝘵𝘦𝘯𝘵𝘪𝘰𝘯 𝘜-𝘕𝘦𝘵, 𝘚𝘌𝘙𝘦𝘴𝘕𝘦𝘵) and a nested decoder structure with deep supervision (—𝘜𝘕𝘦𝘵++).

Hierarchical probabilistic 3D U-Net, with attention mechanisms (—𝘈𝘵𝘵𝘦𝘯𝘵𝘪𝘰𝘯 𝘜-𝘕𝘦𝘵, 𝘚𝘌𝘙𝘦𝘴𝘕𝘦𝘵) and a nested decoder structure with deep supervision (—𝘜𝘕𝘦𝘵++). Built in TensorFlow 2.5. Configured for vox

Diagnostic Image Analysis Group 32 Dec 08, 2022
Deep Sea Treasure Environment for Multi-Objective Optimization Research

DeepSeaTreasure Environment Installation In order to get started with this environment, you can install it using the following command: python3 -m pip

imec IDLab 6 Nov 14, 2022
Infrastructure as Code (IaC) for a self-hosted version of Gnosis Safe on AWS

Welcome to Yearn Gnosis Safe! Setting up your local environment Infrastructure Deploying Gnosis Safe Prerequisites 1. Create infrastructure for secret

Numan 16 Jul 18, 2022
Real-Time Multi-Contact Model Predictive Control via ADMM

Here, you can find the code for the paper 'Real-Time Multi-Contact Model Predictive Control via ADMM'. Code is currently being cleared up and optimize

17 Dec 28, 2022
UV matrix decompostion using movielens dataset

UV-matrix-decompostion-with-kfold UV matrix decompostion using movielens dataset upload the 'ratings.dat' file install the following python libraries

2 Oct 18, 2022
Finetuner allows one to tune the weights of any deep neural network for better embeddings on search tasks

Finetuner allows one to tune the weights of any deep neural network for better embeddings on search tasks

Jina AI 794 Dec 31, 2022
Pytorch implemenation of Stochastic Multi-Label Image-to-image Translation (SMIT)

SMIT: Stochastic Multi-Label Image-to-image Translation This repository provides a PyTorch implementation of SMIT. SMIT can stochastically translate a

Biomedical Computer Vision Group @ Uniandes 37 Mar 01, 2022
MoViNets PyTorch implementation: Mobile Video Networks for Efficient Video Recognition;

MoViNet-pytorch Pytorch unofficial implementation of MoViNets: Mobile Video Networks for Efficient Video Recognition. Authors: Dan Kondratyuk, Liangzh

189 Dec 20, 2022