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 library for fast transformer implementations

Transformers are very successful models that achieve state of the art performance in many natural language tasks

Idiap Research Institute 1.3k Dec 30, 2022
This repository contains datasets and baselines for benchmarking Chinese text recognition.

Benchmarking-Chinese-Text-Recognition This repository contains datasets and baselines for benchmarking Chinese text recognition. Please see the corres

FudanVI Lab 254 Dec 30, 2022
Transformer model implemented with Pytorch

transformer-pytorch Transformer model implemented with Pytorch Attention is all you need-[Paper] Architecture Self-Attention self_attention.py class

Mingu Kang 12 Sep 03, 2022
Code repository for "Reducing Underflow in Mixed Precision Training by Gradient Scaling" presented at IJCAI '20

Reducing Underflow in Mixed Precision Training by Gradient Scaling This project implements the gradient scaling method to improve the performance of m

Ruizhe Zhao 5 Apr 14, 2022
A Closer Look at Structured Pruning for Neural Network Compression

A Closer Look at Structured Pruning for Neural Network Compression Code used to reproduce experiments in https://arxiv.org/abs/1810.04622. To prune, w

Bayesian and Neural Systems Group 140 Dec 05, 2022
A playable implementation of Fully Convolutional Networks with Keras.

keras-fcn A re-implementation of Fully Convolutional Networks with Keras Installation Dependencies keras tensorflow Install with pip $ pip install git

JihongJu 202 Sep 07, 2022
Visualize Camera's Pose Using Extrinsic Parameter by Plotting Pyramid Model on 3D Space

extrinsic2pyramid Visualize Camera's Pose Using Extrinsic Parameter by Plotting Pyramid Model on 3D Space Intro A very simple and straightforward modu

JEONG HYEONJIN 106 Dec 28, 2022
Official PyTorch implementation of N-ImageNet: Towards Robust, Fine-Grained Object Recognition with Event Cameras (ICCV 2021)

N-ImageNet: Towards Robust, Fine-Grained Object Recognition with Event Cameras Official PyTorch implementation of N-ImageNet: Towards Robust, Fine-Gra

32 Dec 26, 2022
Python codes for Lite Audio-Visual Speech Enhancement.

Lite Audio-Visual Speech Enhancement (Interspeech 2020) Introduction This is the PyTorch implementation of Lite Audio-Visual Speech Enhancement (LAVSE

Shang-Yi Chuang 85 Dec 01, 2022
Commonsense Ability Tests

CATS Commonsense Ability Tests Dataset and script for paper Evaluating Commonsense in Pre-trained Language Models Use making_sense.py to run the exper

XUHUI ZHOU 28 Oct 19, 2022
Style-based Point Generator with Adversarial Rendering for Point Cloud Completion (CVPR 2021)

Style-based Point Generator with Adversarial Rendering for Point Cloud Completion (CVPR 2021) An efficient PyTorch library for Point Cloud Completion.

Microsoft 119 Jan 02, 2023
8-week curriculum for AI Builders

curriculum 8-week curriculum for AI Builders สารบัญ บทที่ 1 - Machine Learning คืออะไร บทที่ 2 - ชุดข้อมูลมหัศจรรย์และถิ่นที่อยู่ บทที่ 3 - Stochastic

AI Builders 134 Jan 03, 2023
A package to predict protein inter-residue geometries from sequence data

trRosetta This package is a part of trRosetta protein structure prediction protocol developed in: Improved protein structure prediction using predicte

Ivan Anishchenko 185 Jan 07, 2023
Deep learning (neural network) based remote photoplethysmography: how to extract pulse signal from video using deep learning tools

Deep-rPPG: Camera-based pulse estimation using deep learning tools Deep learning (neural network) based remote photoplethysmography: how to extract pu

Terbe Dániel 138 Dec 17, 2022
MediaPipeのPythonパッケージのサンプルです。2020/12/11時点でPython実装のある4機能(Hands、Pose、Face Mesh、Holistic)について用意しています。

mediapipe-python-sample MediaPipeのPythonパッケージのサンプルです。 2020/12/11時点でPython実装のある以下4機能について用意しています。 Hands Pose Face Mesh Holistic Requirement mediapipe 0.

KazuhitoTakahashi 217 Dec 12, 2022
tf2onnx - Convert TensorFlow, Keras and Tflite models to ONNX.

tf2onnx converts TensorFlow (tf-1.x or tf-2.x), tf.keras and tflite models to ONNX via command line or python api.

Open Neural Network Exchange 1.8k Jan 08, 2023
Pairwise model for commonlit competition

Pairwise model for commonlit competition To run: - install requirements - create input directory with train_folds.csv and other competition data - cd

abhishek thakur 45 Aug 31, 2022
Code release for "BoxeR: Box-Attention for 2D and 3D Transformers"

BoxeR By Duy-Kien Nguyen, Jihong Ju, Olaf Booij, Martin R. Oswald, Cees Snoek. This repository is an official implementation of the paper BoxeR: Box-A

Nguyen Duy Kien 111 Dec 07, 2022
An implementation of the AlphaZero algorithm for Gomoku (also called Gobang or Five in a Row)

AlphaZero-Gomoku This is an implementation of the AlphaZero algorithm for playing the simple board game Gomoku (also called Gobang or Five in a Row) f

Junxiao Song 2.8k Dec 26, 2022
SAMO: Streaming Architecture Mapping Optimisation

SAMO: Streaming Architecture Mapping Optimiser The SAMO framework provides a method of optimising the mapping of a Convolutional Neural Network model

Alexander Montgomerie-Corcoran 20 Dec 10, 2022