Lite-HRNet: A Lightweight High-Resolution Network

Related tags

Deep Learningrealtimesegmentationsemantic-segmentationcityscapesade20krealtime-segmentationlite-hrnetmmsegmentationlitehrnet
Overview

LiteHRNet Benchmark

🔥 🔥 Based on MMsegmentation 🔥 🔥

Cityscapes

FCN resize concat

config mIoU last mAcc last eval last mIoU best mAcc best eval best download
fcn-resize-concat_litehr18-with-head_512x1024_8x2_160k_cityscapes 71.81 80.6 10 71.81 80.6 10 log | 20210816_121228.log.json
fcn-resize-concat_litehr18-with-head_512x1024_8x2_320k_cityscapes 71.96 80.43 10 71.96 80.43 10 log | 20210816_121228.log.json
fcn-resize-concat_litehr18-with-head_512x1024_8x2_640k_cityscapes 69.29 78.91 8 69.29 78.91 8 log | 20210816_121228.log.json
fcn-resize-concat_litehr18-without-head_512x1024_8x2_160k_cityscapes 68.99 77.63 10 68.99 77.63 10 log | 20210816_121228.log.json
fcn-resize-concat_litehr18-without-head_512x1024_8x2_320k_cityscapes 70.42 78.72 10 70.42 78.72 10 log | 20210816_121228.log.json
fcn-resize-concat_litehr18-without-head_512x1024_8x2_640k_cityscapes 67.12 75.84 7 67.12 75.84 7 log | 20210816_121228.log.json
fcn-resize-concat_litehr30-with-head_512x1024_8x2_160k_cityscapes 73.81 82.42 10 73.81 82.42 10 log | 20210816_121228.log.json
fcn-resize-concat_litehr30-with-head_512x1024_8x2_320k_cityscapes 74.46 82.41 10 74.46 82.41 10 log | 20210816_121228.log.json
fcn-resize-concat_litehr30-with-head_512x1024_8x2_640k_cityscapes 69.15 79.65 6 69.15 79.65 6 log | 20210816_121228.log.json
fcn-resize-concat_litehr30-without-head_512x1024_8x2_160k_cityscapes 72.11 80.72 10 72.11 80.72 10 log | 20210816_121228.log.json
fcn-resize-concat_litehr30-without-head_512x1024_8x2_320k_cityscapes 72.12 80.15 10 72.12 80.15 10 log | 20210816_121228.log.json
fcn-resize-concat_litehr30-without-head_512x1024_8x2_640k_cityscapes 67.31 77.76 5 67.31 77.76 5 log | 20210816_121228.log.json

FCN

config mIoU last mAcc last eval last mIoU best mAcc best eval best download
fcn_litehr18-with-head_512x1024_8x2_160k_cityscapes 71.49 79.95 10 71.49 79.95 10 log | 20210816_121228.log.json
fcn_litehr18-with-head_512x1024_8x2_320k_cityscapes 73.03 81.35 10 73.03 81.35 10 log | 20210816_121228.log.json
fcn_litehr18-with-head_512x1024_8x2_640k_cityscapes 68.06 76.67 8 68.26 77.17 7 log | 20210816_121228.log.json
fcn_litehr18-without-head_512x1024_8x2_160k_cityscapes 69.43 78.15 10 69.43 78.15 10 log | 20210816_121228.log.json
fcn_litehr18-without-head_512x1024_8x2_320k_cityscapes 70.61 78.87 10 70.61 78.87 10 log | 20210816_121228.log.json
fcn_litehr18-without-head_512x1024_8x2_640k_cityscapes 63.83 73.11 4 63.83 73.11 4 log | 20210816_121228.log.json
fcn_litehr30-with-head_512x1024_8x2_160k_cityscapes 72.65 81.36 10 72.65 81.36 10 log | 20210816_121228.log.json
fcn_litehr30-with-head_512x1024_8x2_320k_cityscapes 74.98 83.22 10 74.98 83.22 10 log | 20210816_121228.log.json
fcn_litehr30-with-head_512x1024_8x2_640k_cityscapes 69.11 78.88 6 69.11 78.88 6 log | 20210816_121228.log.json
fcn_litehr30-without-head_512x1024_8x2_160k_cityscapes 72.78 81.37 10 72.78 81.37 10 log | 20210816_121228.log.json
fcn_litehr30-without-head_512x1024_8x2_320k_cityscapes 72.37 80.29 10 72.37 80.29 10 log | 20210816_121228.log.json
fcn_litehr30-without-head_512x1024_8x2_640k_cityscapes 63.53 74.6 4 65.91 75.91 3 log | 20210816_121228.log.json

ADE20k

FCN resize concat

config mIoU last mAcc last eval last mIoU best mAcc best eval best download
fcn-resize-concat_litehr18-with-head_512x512_160k_ade20k 16.15 22.12 2 16.15 22.12 2 log | 20210816_121228.log.json
fcn-resize-concat_litehr18-with-head_512x512_160k_ade20k 24.2 31.67 10 24.2 31.67 10 log | 20210816_121228.log.json
fcn-resize-concat_litehr18-with-head_512x512_160k_ade20k 26.17 34.86 10 26.17 34.86 10 log | 20210816_121228.log.json
fcn-resize-concat_litehr18-without-head_512x512_160k_ade20k 16.89 22.96 2 16.89 22.96 2 log | 20210816_121228.log.json
fcn-resize-concat_litehr18-without-head_512x512_160k_ade20k 24.71 32.46 10 24.71 32.46 10 log | 20210816_121228.log.json
fcn-resize-concat_litehr30-with-head_512x512_160k_ade20k 16.77 22.89 2 16.77 22.89 2 log | 20210816_121228.log.json
fcn-resize-concat_litehr30-with-head_512x512_160k_ade20k 26.81 34.96 10 26.81 34.96 10 log | 20210816_121228.log.json
fcn-resize-concat_litehr30-without-head_512x512_160k_ade20k 16.37 22.7 2 16.37 22.7 2 log | 20210816_121228.log.json
fcn-resize-concat_litehr30-without-head_512x512_160k_ade20k 24.38 32.52 10 24.38 32.52 10 log | 20210816_121228.log.json

FCN

config mIoU last mAcc last eval last mIoU best mAcc best eval best download
fcn_litehr18-with-head_512x512_160k_ade20k 0 0 0 0 0 0 log | 20210816_121228.log.json
fcn_litehr18-with-head_512x512_160k_ade20k 23.82 31.51 10 23.82 31.51 10 log | 20210816_121228.log.json
fcn_litehr18-with-head_512x512_160k_ade20k 24.14 31.81 10 24.14 31.81 10 log | 20210816_121228.log.json
fcn_litehr18-without-head_512x512_160k_ade20k 12.23 17.0 2 12.23 17.0 2 log | 20210816_121228.log.json
fcn_litehr18-without-head_512x512_160k_ade20k 20.82 27.58 10 20.82 27.58 10 log | 20210816_121228.log.json
fcn_litehr18-without-head_512x512_160k_ade20k 21.98 29.06 10 21.98 29.06 10 log | 20210816_121228.log.json
fcn_litehr30-with-head_512x512_160k_ade20k 14.11 19.06 3 14.11 19.06 3 log | 20210816_121228.log.json
fcn_litehr30-with-head_512x512_160k_ade20k 24.06 31.78 10 24.06 31.78 10 log | 20210816_121228.log.json
fcn_litehr30-without-head_512x512_160k_ade20k 14.37 19.21 3 14.37 19.21 3 log | 20210816_121228.log.json
fcn_litehr30-without-head_512x512_160k_ade20k 25.22 32.67 10 25.22 32.67 10 log | 20210816_121228.log.json
Owner
GitHub Repository https://arxiv.org/pdf/2104.06403.pdf
DeepFashion2 is a comprehensive fashion dataset.

DeepFashion2 Dataset DeepFashion2 is a comprehensive fashion dataset. It contains 491K diverse images of 13 popular clothing categories from both comm

switchnorm 1.8k Jan 07, 2023
Official implementation for (Refine Myself by Teaching Myself : Feature Refinement via Self-Knowledge Distillation, CVPR-2021)

FRSKD Official implementation for Refine Myself by Teaching Myself : Feature Refinement via Self-Knowledge Distillation (CVPR-2021) Requirements Pytho

75 Dec 28, 2022
MMdet2-based reposity about lightweight detection model: Nanodet, PicoDet.

Lightweight-Detection-and-KD MMdet2-based reposity about lightweight detection model: Nanodet, PicoDet. This repo also includes detection knowledge di

Egqawkq 12 Jan 05, 2023
My solutions for Stanford University course CS224W: Machine Learning with Graphs Fall 2021 colabs (GNN, GAT, GraphSAGE, GCN)

machine-learning-with-graphs My solutions for Stanford University course CS224W: Machine Learning with Graphs Fall 2021 colabs Course materials can be

Marko Njegomir 7 Dec 14, 2022
SigOpt wrappers for scikit-learn methods

SigOpt + scikit-learn Interfacing This package implements useful interfaces and wrappers for using SigOpt and scikit-learn together Getting Started In

SigOpt 73 Sep 30, 2022
Mask2Former: Masked-attention Mask Transformer for Universal Image Segmentation in TensorFlow 2

Mask2Former: Masked-attention Mask Transformer for Universal Image Segmentation in TensorFlow 2 Bowen Cheng, Ishan Misra, Alexander G. Schwing, Alexan

Phan Nguyen 1 Dec 16, 2021
[WACV 2022] Contextual Gradient Scaling for Few-Shot Learning

CxGrad - Official PyTorch Implementation Contextual Gradient Scaling for Few-Shot Learning Sanghyuk Lee, Seunghyun Lee, and Byung Cheol Song In WACV 2

Sanghyuk Lee 4 Dec 05, 2022
A multi-functional library for full-stack Deep Learning. Simplifies Model Building, API development, and Model Deployment.

chitra What is chitra? chitra (चित्र) is a multi-functional library for full-stack Deep Learning. It simplifies Model Building, API development, and M

Aniket Maurya 210 Dec 21, 2022
PushForKiCad - AISLER Push for KiCad EDA

AISLER Push for KiCad Push your layout to AISLER with just one click for instant

AISLER 31 Dec 29, 2022
Official code for "Towards An End-to-End Framework for Flow-Guided Video Inpainting" (CVPR2022)

E2FGVI (CVPR 2022) English | 简体中文 This repository contains the official implementation of the following paper: Towards An End-to-End Framework for Flo

Media Computing Group @ Nankai University 537 Jan 07, 2023
Garbage Detection system which will detect objects based on whether it is plastic waste or plastics or just garbage.

Garbage Detection using Yolov5 on Jetson Nano 2gb Developer Kit. Garbage detection system which will detect objects based on whether it is plastic was

Rishikesh A. Bondade 2 May 13, 2022
Code for technical report "An Improved Baseline for Sentence-level Relation Extraction".

RE_improved_baseline Code for technical report "An Improved Baseline for Sentence-level Relation Extraction". Requirements torch = 1.8.1 transformers

Wenxuan Zhou 74 Nov 29, 2022
Deep Probabilistic Programming Course @ DIKU

Deep Probabilistic Programming Course @ DIKU

52 May 14, 2022
Companion code for "Bayesian logistic regression for online recalibration and revision of risk prediction models with performance guarantees"

Companion code for "Bayesian logistic regression for online recalibration and revision of risk prediction models with performance guarantees" Installa

0 Oct 13, 2021
an implementation of Revisiting Adaptive Convolutions for Video Frame Interpolation using PyTorch

revisiting-sepconv This is a reference implementation of Revisiting Adaptive Convolutions for Video Frame Interpolation [1] using PyTorch. Given two f

Simon Niklaus 59 Dec 22, 2022
Use deep learning, genetic programming and other methods to predict stock and market movements

StockPredictions Use classic tricks, neural networks, deep learning, genetic programming and other methods to predict stock and market movements. Both

Linda MacPhee-Cobb 386 Jan 03, 2023
TensorFlow implementation of "Attention is all you need (Transformer)"

[TensorFlow 2] Attention is all you need (Transformer) TensorFlow implementation of "Attention is all you need (Transformer)" Dataset The MNIST datase

YeongHyeon Park 4 Jan 05, 2022
Apply AnimeGAN-v2 across frames of a video clip

title emoji colorFrom colorTo sdk app_file pinned AnimeGAN-v2 For Videos 🔥 blue red gradio app.py false AnimeGAN-v2 For Videos Apply AnimeGAN-v2 acro

Nathan Raw 36 Oct 18, 2022
Distance correlation and related E-statistics in Python

dcor dcor: distance correlation and related E-statistics in Python. E-statistics are functions of distances between statistical observations in metric

Carlos Ramos Carreño 108 Dec 27, 2022
Official implementation for paper: A Latent Transformer for Disentangled Face Editing in Images and Videos.

A Latent Transformer for Disentangled Face Editing in Images and Videos Official implementation for paper: A Latent Transformer for Disentangled Face

InterDigital 108 Dec 09, 2022