An Artificial Intelligence trying to drive a car by itself on a user created map

Overview

AI-For-Self-Driving-Car

  • Detailed explanation and a case study of the complete AI is given in the Detailed Explanation folder

Sample Installation Instructions -

  • Linux and Max users, please open your terminal. On Mac, the easiest way to open it is to press anywhere cmd + space, and then in the Spotlight Search you enter "terminal". On Linux, you will find it very easily, usually on the left side of your monitor. Then inside the terminal, copy paste and enter each of the following line commands separately:

    1. conda install pytorch==0.3.1 -c pytorch
    2. conda install -c conda-forge kivy
  • And Windows users, please open the anaconda prompt, which you can find this way: Windows Button in the lower left corner -> List of programs -> anaconda -> anaconda prompt Then inside the anaconda prompt, copy paste and enter each of the following line commands separately:

    1. conda install -c peterjc123 pytorch-cpu
    2. conda install -c conda-forge kivy
Owner
Akhil Sahukaru
Akhil Sahukaru
PyTorch implementation of ENet

PyTorch-ENet PyTorch (v1.1.0) implementation of ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation, ported from the lua-torc

David Silva 333 Dec 29, 2022
Instance-Dependent Partial Label Learning

Instance-Dependent Partial Label Learning Installation pip install -r requirements.txt Run the Demo benchmark-random mnist python -u main.py --gpu 0 -

17 Dec 29, 2022
mmdetection version of TinyBenchmark.

introduction This project is an mmdetection version of TinyBenchmark. TODO list: add TinyPerson dataset and evaluation add crop and merge for image du

34 Aug 27, 2022
KITTI-360 Annotation Tool is a framework that developed based on python(cherrypy + jinja2 + sqlite3) as the server end and javascript + WebGL as the front end.

KITTI-360 Annotation Tool is a framework that developed based on python(cherrypy + jinja2 + sqlite3) as the server end and javascript + WebGL as the front end.

86 Dec 12, 2022
In this project we predict the forest cover type using the cartographic variables in the training/test datasets.

Kaggle Competition: Forest Cover Type Prediction In this project we predict the forest cover type (the predominant kind of tree cover) using the carto

Marianne Joy Leano 1 Mar 15, 2022
The devkit of the nuScenes dataset.

nuScenes devkit Welcome to the devkit of the nuScenes and nuImages datasets. Overview Changelog Devkit setup nuImages nuImages setup Getting started w

Motional 1.6k Jan 05, 2023
A high performance implementation of HDBSCAN clustering.

HDBSCAN HDBSCAN - Hierarchical Density-Based Spatial Clustering of Applications with Noise. Performs DBSCAN over varying epsilon values and integrates

2.3k Jan 02, 2023
SGoLAM - Simultaneous Goal Localization and Mapping

SGoLAM - Simultaneous Goal Localization and Mapping PyTorch implementation of the MultiON runner-up entry, SGoLAM: Simultaneous Goal Localization and

10 Jan 05, 2023
This is the code for the paper "Motion-Focused Contrastive Learning of Video Representations" (ICCV'21).

Motion-Focused Contrastive Learning of Video Representations Introduction This is the code for the paper "Motion-Focused Contrastive Learning of Video

11 Sep 23, 2022
MCMC samplers for Bayesian estimation in Python, including Metropolis-Hastings, NUTS, and Slice

Sampyl May 29, 2018: version 0.3 Sampyl is a package for sampling from probability distributions using MCMC methods. Similar to PyMC3 using theano to

Mat Leonard 304 Dec 25, 2022
🐦 Quickly annotate data from the comfort of your Jupyter notebook

🐦 pigeon - Quickly annotate data on Jupyter Pigeon is a simple widget that lets you quickly annotate a dataset of unlabeled examples from the comfort

Anastasis Germanidis 647 Jan 05, 2023
Code repository accompanying the paper "On Adversarial Robustness: A Neural Architecture Search perspective"

On Adversarial Robustness: A Neural Architecture Search perspective Preparation: Clone the repository: https://github.com/tdchaitanya/nas-robustness.g

Chaitanya Devaguptapu 4 Nov 10, 2022
A PyTorch implementation for Unsupervised Domain Adaptation by Backpropagation(DANN), support Office-31 and Office-Home dataset

DANN A PyTorch implementation for Unsupervised Domain Adaptation by Backpropagation Prerequisites Linux or OSX NVIDIA GPU + CUDA (may CuDNN) and corre

8 Apr 16, 2022
Code repository for our paper regarding the L3D dataset.

The Large Labelled Logo Dataset (L3D): A Multipurpose and Hand-Labelled Continuously Growing Dataset Website: https://lhf-labs.github.io/tm-dataset Da

LHF Labs 9 Dec 14, 2022
YOLOv5 🚀 is a family of object detection architectures and models pretrained on the COCO dataset

YOLOv5 🚀 is a family of object detection architectures and models pretrained on the COCO dataset, and represents Ultralytics open-source research int

阿才 73 Dec 16, 2022
Temporally Coherent GAN SIGGRAPH project.

TecoGAN This repository contains source code and materials for the TecoGAN project, i.e. code for a TEmporally COherent GAN for video super-resolution

Duc Linh Nguyen 2 Jan 18, 2022
[EMNLP 2021] MuVER: Improving First-Stage Entity Retrieval with Multi-View Entity Representations

MuVER This repo contains the code and pre-trained model for our EMNLP 2021 paper: MuVER: Improving First-Stage Entity Retrieval with Multi-View Entity

24 May 30, 2022
Pyserini is a Python toolkit for reproducible information retrieval research with sparse and dense representations.

Pyserini Pyserini is a Python toolkit for reproducible information retrieval research with sparse and dense representations. Retrieval using sparse re

Castorini 706 Dec 29, 2022
This is the official PyTorch implementation for "Mesa: A Memory-saving Training Framework for Transformers".

Mesa: A Memory-saving Training Framework for Transformers This is the official PyTorch implementation for Mesa: A Memory-saving Training Framework for

Zhuang AI Group 105 Dec 06, 2022
Gans-in-action - Companion repository to GANs in Action: Deep learning with Generative Adversarial Networks

GANs in Action by Jakub Langr and Vladimir Bok List of available code: Chapter 2: Colab, Notebook Chapter 3: Notebook Chapter 4: Notebook Chapter 6: C

GANs in Action 914 Dec 21, 2022