CAR-API: Cityscapes Attributes Recognition API

Related tags

Deep LearningCAR-API
Overview

CAR-API: Cityscapes Attributes Recognition API

This is the official api to download and fetch attributes annotations for Cityscapes Dataset.

Content

Installation

You first need to download Cityscapes dataset. You can do so by checking this repo.

I'm showing here a simple working example to download the data but for further issues please refer to the source repo. Or download from the official website

  1. Install Cityscapes scripts and other required packages.
$ pip install -r requirements.txt
  1. Run the following script to download Cityscapes dataset. If you don't have an account, you will need to create an account.
$ csDownload -d [DESTINATION_PATH] PACKAGE_NAME

Note: you can also use -l option to list all possible packages to download. i.e.

$ csDownload -l
  1. After downloading all required packages, set the environment variable CITYSCAPES_DATASET to the location of the dataset. For example, if the dataset is installed in the path /home/user/cityscapes/
$ export CITYSCAPES_DATASET="/home/user/cityscapes/"

Note: you can also export the previous command to your ~/.bashrc file for example.

~/.bashrc ">
$ echo 'export CITYSCAPES_DATASET="/home/user/cityscapes/"' > ~/.bashrc

Note2: we actually need the images only. We do not need the labels as it is stored with the attributes annotations as well.

  1. Run the following to download the json files of CAR compressed as a single zip file extract it and then remove the zip file.
$ python download_CAR.py --url_path "https://DOWNLOAD_LINK_HERE"

To obtain the download link, please email me at kmetwaly511 [at] gmail [dot] com.

At this point, you have 4 json files; namely all.json, train.json, val.json and test.json

PyTorch Example

We provide a pytorch example to read the dataset and retrieve a sample of the dataset in pytorch_dataset_CAR.py. Please, refer to main.It contains a code that goes through the entire dataset.

An output sample of the dataset class is of custom type ModelInputItem. Please refer to the definiton of the class for more details about defined methods and variables.

Citation

If you are planning to use this code or the dataset, please cite the work appropriately as follows.

@misc{car_api,
  title = {{CAR}-{API}: an {API} for {CAR} Dataset},
  key = {{CAR}-{API}},
  howpublished = {\url{http://github.com/kareem-metwaly/car-api}},
  note = {Accessed: 2021-11-16}
}

@misc{metwaly2022car,
  title={{CAR} -- Cityscapes Attributes Recognition A Multi-category Attributes Dataset for Autonomous Vehicles}, 
  author={Kareem Metwaly and Aerin Kim and Elliot Branson and Vishal Monga},
  year={2021},
  eprint={2111.08243},
  archivePrefix={arXiv},
  primaryClass={cs.CV},
  howpublished = {\url{https://arxiv.org/abs/2111.08243}},
  urldate = {2021-11-17},
}
Owner
Kareem Metwaly
Kareem Metwaly
PyTorch implementation of popular datasets and models in remote sensing

PyTorch Remote Sensing (torchrs) (WIP) PyTorch implementation of popular datasets and models in remote sensing tasks (Change Detection, Image Super Re

isaac 222 Dec 28, 2022
[UNMAINTAINED] Automated machine learning for analytics & production

auto_ml Automated machine learning for production and analytics Installation pip install auto_ml Getting started from auto_ml import Predictor from au

Preston Parry 1.6k Jan 02, 2023
Code for the CVPR2022 paper "Frequency-driven Imperceptible Adversarial Attack on Semantic Similarity"

Introduction This is an official release of the paper "Frequency-driven Imperceptible Adversarial Attack on Semantic Similarity" (arxiv link). Abstrac

Leo 21 Nov 23, 2022
Densely Connected Search Space for More Flexible Neural Architecture Search (CVPR2020)

DenseNAS The code of the CVPR2020 paper Densely Connected Search Space for More Flexible Neural Architecture Search. Neural architecture search (NAS)

Jamin Fong 291 Nov 18, 2022
Code for "Learning to Regrasp by Learning to Place"

Learning2Regrasp Learning to Regrasp by Learning to Place, CoRL 2021. Introduction We propose a point-cloud-based system for robots to predict a seque

Shuo Cheng (成硕) 18 Aug 27, 2022
Project code for weakly supervised 3D object detectors using wide-baseline multi-view traffic camera data: WIBAM.

WIBAM (Work in progress) Weakly Supervised Training of Monocular 3D Object Detectors Using Wide Baseline Multi-view Traffic Camera Data 3D object dete

Matthew Howe 10 Aug 24, 2022
Kaggle | 9th place (part of) solution for the Bristol-Myers Squibb – Molecular Translation challenge

Part of the 9th place solution for the Bristol-Myers Squibb – Molecular Translation challenge translating images containing chemical structures into I

Erdene-Ochir Tuguldur 22 Nov 30, 2022
RLDS stands for Reinforcement Learning Datasets

RLDS RLDS stands for Reinforcement Learning Datasets and it is an ecosystem of tools to store, retrieve and manipulate episodic data in the context of

Google Research 135 Jan 01, 2023
Code for the paper "Learning-Augmented Algorithms for Online Steiner Tree"

Learning-Augmented Algorithms for Online Steiner Tree This is the code for the paper "Learning-Augmented Algorithms for Online Steiner Tree". Requirem

0 Dec 09, 2021
Official Code Release for "CLIP-Adapter: Better Vision-Language Models with Feature Adapters"

Official Code Release for "CLIP-Adapter: Better Vision-Language Models with Feature Adapters" Pipeline of CLIP-Adapter CLIP-Adapter is a drop-in modul

peng gao 157 Dec 26, 2022
Learning to Estimate Hidden Motions with Global Motion Aggregation

Learning to Estimate Hidden Motions with Global Motion Aggregation (GMA) This repository contains the source code for our paper: Learning to Estimate

Shihao Jiang (Zac) 221 Dec 18, 2022
MASS (Mueen's Algorithm for Similarity Search) - a python 2 and 3 compatible library used for searching time series sub-sequences under z-normalized Euclidean distance for similarity.

Introduction MASS allows you to search a time series for a subquery resulting in an array of distances. These array of distances enable you to identif

Matrix Profile Foundation 79 Dec 31, 2022
City-seeds - A random generator of cultural characteristics intended to spark ideas and help draw threads

City Seeds This is a random generator of cultural characteristics intended to sp

Aydin O'Leary 2 Mar 12, 2022
A simple and lightweight genetic algorithm for optimization of any machine learning model

geneticml This package contains a simple and lightweight genetic algorithm for optimization of any machine learning model. Installation Use pip to ins

Allan Barcelos 8 Aug 10, 2022
Simulation of self-focusing of laser beams in condensed media

What is it? Program for scientific research, which allows to simulate the phenomenon of self-focusing of different laser beams (including Gaussian, ri

Evgeny Vasilyev 13 Dec 24, 2022
Official implementation of the ICCV 2021 paper "Joint Inductive and Transductive Learning for Video Object Segmentation"

JOINT This is the official implementation of Joint Inductive and Transductive learning for Video Object Segmentation, to appear in ICCV 2021. @inproce

Yunyao 35 Oct 16, 2022
Hypernetwork-Ensemble Learning of Segmentation Probability for Medical Image Segmentation with Ambiguous Labels

Hypernet-Ensemble Learning of Segmentation Probability for Medical Image Segmentation with Ambiguous Labels The implementation of Hypernet-Ensemble Le

Sungmin Hong 6 Jul 18, 2022
Release of the ConditionalQA dataset

ConditionalQA Datasets accompanying the paper ConditionalQA: A Complex Reading Comprehension Dataset with Conditional Answers. Disclaimer This dataset

14 Oct 17, 2022
Implementation of OmniNet, Omnidirectional Representations from Transformers, in Pytorch

Omninet - Pytorch Implementation of OmniNet, Omnidirectional Representations from Transformers, in Pytorch. The authors propose that we should be atte

Phil Wang 48 Nov 21, 2022
YOLOX-RMPOLY

本算法为适应robomaster比赛,而改动自矩形识别的yolox算法。 基于旷视科技YOLOX,实现对不规则四边形的目标检测 TODO 修改onnx推理模型 更改/添加标注: 1.yolox/models/yolox_polyhead.py: 1.1继承yolox/models/yolo_

3 Feb 25, 2022