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
Cross-media Structured Common Space for Multimedia Event Extraction (ACL2020)

Cross-media Structured Common Space for Multimedia Event Extraction Table of Contents Overview Requirements Data Quickstart Citation Overview The code

Manling Li 49 Nov 21, 2022
Official implementation of the paper WAV2CLIP: LEARNING ROBUST AUDIO REPRESENTATIONS FROM CLIP

Wav2CLIP ๐Ÿšง WIP ๐Ÿšง Official implementation of the paper WAV2CLIP: LEARNING ROBUST AUDIO REPRESENTATIONS FROM CLIP ๐Ÿ“„ ๐Ÿ”— Ho-Hsiang Wu, Prem Seetharaman

Descript 240 Dec 13, 2022
๐Ÿ”ฎ Execution time predictions for deep neural network training iterations across different GPUs.

Habitat: A Runtime-Based Computational Performance Predictor for Deep Neural Network Training Habitat is a tool that predicts a deep neural network's

Geoffrey Yu 44 Dec 27, 2022
An example to implement a new backbone with OpenMMLab framework.

Backbone example on OpenMMLab framework English | ็ฎ€ไฝ“ไธญๆ–‡ Introduction This is an template repo about how to use OpenMMLab framework to develop a new bac

Ma Zerun 22 Dec 29, 2022
Angular & Electron desktop UI framework. Angular components for native looking and behaving macOS desktop UI (Electron/Web)

Angular Desktop UI This is a collection for native desktop like user interface components in Angular, especially useful for Electron apps. It starts w

Marc J. Schmidt 49 Dec 22, 2022
Implementation of Neural Distance Embeddings for Biological Sequences (NeuroSEED) in PyTorch

Neural Distance Embeddings for Biological Sequences Official implementation of Neural Distance Embeddings for Biological Sequences (NeuroSEED) in PyTo

Gabriele Corso 56 Dec 23, 2022
Code for "R-GCN: The R Could Stand for Random"

RR-GCN: Random Relational Graph Convolutional Networks PyTorch Geometric code for the paper "R-GCN: The R Could Stand for Random" RR-GCN is an extensi

PreDiCT.IDLab 31 Sep 07, 2022
Alphabetical Letter Recognition

BayeesNetworks-Image-Classification Alphabetical Letter Recognition In these demo we are using "Bayees Networks" Our database is composed by Learning

Mohammed Firass 4 Nov 30, 2021
PyTorch Code of "Memory In Memory: A Predictive Neural Network for Learning Higher-Order Non-Stationarity from Spatiotemporal Dynamics"

Memory In Memory Networks It is based on the paper Memory In Memory: A Predictive Neural Network for Learning Higher-Order Non-Stationarity from Spati

Yang Li 12 May 30, 2022
Data, notebooks, and articles associated with the RSNA AI Deep Learning Lab at RSNA 2021

RSNA AI Deep Learning Lab 2021 Intro Welcome Deep Learners! This document provides all the information you need to participate in the RSNA AI Deep Lea

RSNA 65 Dec 16, 2022
Discretized Integrated Gradients for Explaining Language Models (EMNLP 2021)

Discretized Integrated Gradients for Explaining Language Models (EMNLP 2021) Overview of paths used in DIG and IG. w is the word being attributed. The

INK Lab @ USC 17 Oct 27, 2022
Constrained Language Models Yield Few-Shot Semantic Parsers

Constrained Language Models Yield Few-Shot Semantic Parsers This repository contains tools and instructions for reproducing the experiments in the pap

Microsoft 43 Nov 23, 2022
Official git for "CTAB-GAN: Effective Table Data Synthesizing"

CTAB-GAN This is the official git paper CTAB-GAN: Effective Table Data Synthesizing. The paper is published on Asian Conference on Machine Learning (A

30 Dec 26, 2022
Build upon neural radiance fields to create a scene-specific implicit 3D semantic representation, Semantic-NeRF

Semantic-NeRF: Semantic Neural Radiance Fields Project Page | Video | Paper | Data In-Place Scene Labelling and Understanding with Implicit Scene Repr

Shuaifeng Zhi 243 Jan 07, 2023
Code for Neurips2021 Paper "Topology-Imbalance Learning for Semi-Supervised Node Classification".

Topology-Imbalance Learning for Semi-Supervised Node Classification Introduction Code for NeurIPS 2021 paper "Topology-Imbalance Learning for Semi-Sup

Victor Chen 40 Nov 23, 2022
Official implementation of the network presented in the paper "M4Depth: A motion-based approach for monocular depth estimation on video sequences"

M4Depth This is the reference TensorFlow implementation for training and testing depth estimation models using the method described in M4Depth: A moti

Michaรซl Fonder 76 Jan 03, 2023
Hyperparameters tuning and features selection are two common steps in every machine learning pipeline.

shap-hypetune A python package for simultaneous Hyperparameters Tuning and Features Selection for Gradient Boosting Models. Overview Hyperparameters t

Marco Cerliani 422 Jan 08, 2023
Face2webtoon - Despite its importance, there are few previous works applying I2I translation to webtoon.

Despite its importance, there are few previous works applying I2I translation to webtoon. I collected dataset from naver webtoon ์—ฐ์• ํ˜๋ช… and tried to transfer human faces to webtoon domain.

์ด์ƒ์œค 64 Oct 19, 2022
PyTorch implementation of EfficientNetV2

[NEW!] Check out our latest work involution accepted to CVPR'21 that introduces a new neural operator, other than convolution and self-attention. PyTo

Duo Li 375 Jan 03, 2023
Official Code for "Non-deep Networks"

Non-deep Networks arXiv:2110.07641 Ankit Goyal, Alexey Bochkovskiy, Jia Deng, Vladlen Koltun Overview: Depth is the hallmark of DNNs. But more depth m

Ankit Goyal 567 Dec 12, 2022