VIL-100: A New Dataset and A Baseline Model for Video Instance Lane Detection (ICCV 2021)

Related tags

Deep LearningMMA-Net
Overview

Preparation

  1. Please see dataset/README.md to get more details about our datasets-VIL100

  2. Please see INSTALL.md to install environment and evaluation tools

  3. Before training, we should download datasets-VIL100 and models

  4. Put them under this structure

      MMA-Net
           |----INSTALL.md
           |----README.md
           |----dataset
           |------|-----VIL100
           |----models
           |----evaluation
           |----options.py
           |----libs
           |----requirements.txt
           |----train.py
           |----test.py
    

Training and Testing

  1. To train the MMA network, run following command

    python3 train.py --gpu ${GPU-IDS}
  2. To test the MMA network, run following command

    python3 test.py

    The test results will be saved as indexed png file at ${root}/${output}/${valset}.

    Additionally, you can modify some setting parameters in options.py to change training configuration.

Evaluation

  1. generate accuracy, fp, fp

    python evaluate_acc.py      # Please modify `pre_dir_name` and `json_dir_name` in evaluate_acc.py
    
  2. Install CULane evaluation tools, please see INSTALL.md

  3. generate F, mIoUevaluate_acc after the CULane evaluation tools are installed

    1. all pred txt files will be generated under MMA-Net/evaluation/txt/pred_txt after this step

      python generate_iou_pred_txt.py      # Please modify `pre_dir_name` and `json_path` in  `generate_iou_pred_txt.py`
      
    2. results_MMA and temp_MMA will be generated under MMA-Net/evaluation/txt/results_txt after this step.

      results_MMA: evaluation results of each sequence

      temp_MMA: temporary files generated during evaluation, you can ignore them

      python evaluate_iou.py      # `data_root` should be set as your VIL-100 dataset path in `evaluate_iou.py`
      
    3. Attention!! if you want to evaluation results one more time, please delete all folders/files under MMA-Net/evaluation/txt/results_txt .

When are Iterative GPs Numerically Accurate?

When are Iterative GPs Numerically Accurate? This is a code repository for the paper "When are Iterative GPs Numerically Accurate?" by Wesley Maddox,

Wesley Maddox 1 Jan 06, 2022
PyTorch Implementation of Realtime Multi-Person Pose Estimation project.

PyTorch Realtime Multi-Person Pose Estimation This is a pytorch version of Realtime_Multi-Person_Pose_Estimation, origin code is here Realtime_Multi-P

Dave Fang 157 Nov 12, 2022
Drone Task1 - Drone Task1 With Python

Drone_Task1 Matching Results 3.mp4 1.mp4

MLV Lab (Machine Learning and Vision Lab at Korea University) 11 Nov 14, 2022
1st place solution to the Satellite Image Change Detection Challenge hosted by SenseTime

1st place solution to the Satellite Image Change Detection Challenge hosted by SenseTime

Lihe Yang 209 Jan 01, 2023
The Turing Change Point Detection Benchmark: An Extensive Benchmark Evaluation of Change Point Detection Algorithms on real-world data

Turing Change Point Detection Benchmark Welcome to the repository for the Turing Change Point Detection Benchmark, a benchmark evaluation of change po

The Alan Turing Institute 85 Dec 28, 2022
A platform to display the carbon neutralization information for researchers, decision-makers, and other participants in the community.

Welcome to Carbon Insight Carbon Insight is a platform aiming to display the carbon neutralization roadmap for researchers, decision-makers, and other

Microsoft 14 Oct 24, 2022
Real-Time SLAM for Monocular, Stereo and RGB-D Cameras, with Loop Detection and Relocalization Capabilities

ORB-SLAM2 Authors: Raul Mur-Artal, Juan D. Tardos, J. M. M. Montiel and Dorian Galvez-Lopez (DBoW2) 13 Jan 2017: OpenCV 3 and Eigen 3.3 are now suppor

Raul Mur-Artal 7.8k Dec 30, 2022
Code for our EMNLP 2021 paper "Learning Kernel-Smoothed Machine Translation with Retrieved Examples"

KSTER Code for our EMNLP 2021 paper "Learning Kernel-Smoothed Machine Translation with Retrieved Examples" [paper]. Usage Download the processed datas

jiangqn 23 Nov 24, 2022
PyTorch implementation of Federated Learning with Non-IID Data, and federated learning algorithms, including FedAvg, FedProx.

Federated Learning with Non-IID Data This is an implementation of the following paper: Yue Zhao, Meng Li, Liangzhen Lai, Naveen Suda, Damon Civin, Vik

Youngjoon Lee 48 Dec 29, 2022
Blender Add-on that sets a Material's Base Color to one of Pantone's Colors of the Year

Blender PCOY (Pantone Color of the Year) MCMC (Mid-Century Modern Colors) HG71 (House & Garden Colors 1971) Blender Add-ons That Assign a Custom Color

Don Schnitzius 15 Nov 20, 2022
PyTorch Implementation of "Non-Autoregressive Neural Machine Translation"

Non-Autoregressive Transformer Code release for Non-Autoregressive Neural Machine Translation by Jiatao Gu, James Bradbury, Caiming Xiong, Victor O.K.

Salesforce 261 Nov 12, 2022
Rethinking Transformer-based Set Prediction for Object Detection

Rethinking Transformer-based Set Prediction for Object Detection Here are the code for the ICCV paper. The code is adapted from Detectron2 and AdelaiD

Zhiqing Sun 62 Dec 03, 2022
FAMIE is a comprehensive and efficient active learning (AL) toolkit for multilingual information extraction (IE)

FAMIE: A Fast Active Learning Framework for Multilingual Information Extraction

18 Sep 01, 2022
DeepHawkeye is a library to detect unusual patterns in images using features from pretrained neural networks

English | 简体中文 Introduction DeepHawkeye is a library to detect unusual patterns in images using features from pretrained neural networks Reference Pat

CV Newbie 28 Dec 13, 2022
The code for "Deep Level Set for Box-supervised Instance Segmentation in Aerial Images".

Deep Levelset for Box-supervised Instance Segmentation in Aerial Images Wentong Li, Yijie Chen, Wenyu Liu, Jianke Zhu* This code is based on MMdetecti

sunshine.lwt 112 Jan 05, 2023
Deep Reinforced Attention Regression for Partial Sketch Based Image Retrieval.

DARP-SBIR Intro This repository contains the source code implementation for ICDM submission paper Deep Reinforced Attention Regression for Partial Ske

2 Jan 09, 2022
How to Predict Stock Prices Easily Demo

How-to-Predict-Stock-Prices-Easily-Demo How to Predict Stock Prices Easily - Intro to Deep Learning #7 by Siraj Raval on Youtube ##Overview This is th

Siraj Raval 752 Nov 16, 2022
A numpy-based implementation of RANSAC for fundamental matrix and homography estimation. The degeneracy updating and local optimization components are included and optional.

Description A numpy-based implementation of RANSAC for fundamental matrix and homography estimation. The degeneracy updating and local optimization co

AoxiangFan 9 Nov 10, 2022
SatelliteNeRF - PyTorch-based Neural Radiance Fields adapted to satellite domain

SatelliteNeRF PyTorch-based Neural Radiance Fields adapted to satellite domain.

Kai Zhang 46 Nov 20, 2022
A PyTorch implementation of unsupervised SimCSE

A PyTorch implementation of unsupervised SimCSE

99 Dec 23, 2022