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 .

TeST: Temporal-Stable Thresholding for Semi-supervised Learning

TeST: Temporal-Stable Thresholding for Semi-supervised Learning TeST Illustration Semi-supervised learning (SSL) offers an effective method for large-

Xiong Weiyu 1 Jul 14, 2022
Official Repository for "Robust On-Policy Data Collection for Data Efficient Policy Evaluation" (NeurIPS 2021 Workshop on OfflineRL).

Robust On-Policy Data Collection for Data-Efficient Policy Evaluation Source code of Robust On-Policy Data Collection for Data-Efficient Policy Evalua

Autonomous Agents Research Group (University of Edinburgh) 2 Oct 09, 2022
Building Ellee — A GPT-3 and Computer Vision Powered Talking Robotic Teddy Bear With Human Level Conversation Intelligence

Using an object detection and facial recognition system built on MobileNetSSDV2 and Dlib and running on an NVIDIA Jetson Nano, a GPT-3 model, Google Speech Recognition, Amazon Polly and servo motors,

24 Oct 26, 2022
Code associated with the paper "Deep Optics for Single-shot High-dynamic-range Imaging"

Deep Optics for Single-shot High-dynamic-range Imaging Code associated with the paper "Deep Optics for Single-shot High-dynamic-range Imaging" CVPR, 2

Stanford Computational Imaging Lab 40 Dec 12, 2022
A voice recognition assistant similar to amazon alexa, siri and google assistant.

kenyan-Siri Build an Artificial Assistant Full tutorial (video) To watch the tutorial, click on the image below Installation For windows users (run th

Alison Parker 3 Aug 19, 2022
GAN encoders in PyTorch that could match PGGAN, StyleGAN v1/v2, and BigGAN. Code also integrates the implementation of these GANs.

MTV-TSA: Adaptable GAN Encoders for Image Reconstruction via Multi-type Latent Vectors with Two-scale Attentions. This is the official code release fo

owl 37 Dec 24, 2022
Crosslingual Segmental Language Model

Crosslingual Segmental Language Model This repository contains the code from Multilingual unsupervised sequence segmentation transfers to extremely lo

C.M. Downey 1 Jun 13, 2022
A GPU-optional modular synthesizer in pytorch, 16200x faster than realtime, for audio ML researchers.

torchsynth The fastest synth in the universe. Introduction torchsynth is based upon traditional modular synthesis written in pytorch. It is GPU-option

torchsynth 229 Jan 02, 2023
Breaching - Breaching privacy in federated learning scenarios for vision and text

Breaching - A Framework for Attacks against Privacy in Federated Learning This P

Jonas Geiping 139 Jan 03, 2023
neural image generation

pixray Pixray is an image generation system. It combines previous ideas including: Perception Engines which uses image augmentation and iteratively op

dribnet 398 Dec 17, 2022
Code for Transformer Hawkes Process, ICML 2020.

Transformer Hawkes Process Source code for Transformer Hawkes Process (ICML 2020). Run the code Dependencies Python 3.7. Anaconda contains all the req

Simiao Zuo 111 Dec 26, 2022
Fast Differentiable Matrix Sqrt Root

Official Pytorch implementation of ICLR 22 paper Fast Differentiable Matrix Square Root

YueSong 42 Dec 30, 2022
Pytorch implementation of our method for regularizing nerual radiance fields for few-shot neural volume rendering.

InfoNeRF: Ray Entropy Minimization for Few-Shot Neural Volume Rendering Pytorch implementation of our method for regularizing nerual radiance fields f

106 Jan 06, 2023
Pi-NAS: Improving Neural Architecture Search by Reducing Supernet Training Consistency Shift (ICCV 2021)

Π-NAS This repository provides the evaluation code of our submitted paper: Pi-NAS: Improving Neural Architecture Search by Reducing Supernet Training

Jiqi Zhang 18 Aug 18, 2022
TEDSummary is a speech summary corpus. It includes TED talks subtitle (Document), Title-Detail (Summary), speaker name (Meta info), MP4 URL, and utterance id

TEDSummary is a speech summary corpus. It includes TED talks subtitle (Document), Title-Detail (Summary), speaker name (Meta info), MP4 URL

3 Dec 26, 2022
NitroFE is a Python feature engineering engine which provides a variety of modules designed to internally save past dependent values for providing continuous calculation.

NitroFE is a Python feature engineering engine which provides a variety of modules designed to internally save past dependent values for providing continuous calculation.

100 Sep 28, 2022
Code for ACL 2019 Paper: "COMET: Commonsense Transformers for Automatic Knowledge Graph Construction"

To run a generation experiment (either conceptnet or atomic), follow these instructions: First Steps First clone, the repo: git clone https://github.c

Antoine Bosselut 575 Jan 01, 2023
Object Depth via Motion and Detection Dataset

ODMD Dataset ODMD is the first dataset for learning Object Depth via Motion and Detection. ODMD training data are configurable and extensible, with ea

Brent Griffin 172 Dec 21, 2022
Code for ACL 21: Generating Query Focused Summaries from Query-Free Resources

marge This repository releases the code for Generating Query Focused Summaries from Query-Free Resources. Please cite the following paper [bib] if you

Yumo Xu 28 Nov 10, 2022
Official PyTorch Implementation of Unsupervised Learning of Scene Flow Estimation Fusing with Local Rigidity

UnRigidFlow This is the official PyTorch implementation of UnRigidFlow (IJCAI2019). Here are two sample results (~10MB gif for each) of our unsupervis

Liang Liu 28 Nov 16, 2022