This solves the autonomous driving issue which is supported by deep learning technology. Given a video, it splits into images and predicts the angle of turning for each frame.

Overview

Self Driving Car

An autonomous car (also known as a driverless car, self-driving car, and robotic car) is a vehicle that is capable of sensing its environment and navigating without human input. Autonomous cars combine a variety of techniques to perceive their surroundings, including radar, laser light, GPS, odometry, and computer vision. Advanced control systems interpret sensory information to identify appropriate navigation paths, as well as obstacles and relevant signage

Objective

Given images of road you need to predict its degree of turning.

Inspiration 🗼

  1. Udacity Self driving car
  2. End to End Learning for Self-Driving Cars

Code Requirements 🦄

  • pip install requirements.txt

Dataset 1

Approximately 45,500 images, 2.2GB. One of the original datasets I made in 2017. Data was recorded around Rancho Palos Verdes and San Pedro California.

Link

Data format is as follows: filename.jpg angle

Dataset 2

Approximately 63,000 images, 3.1GB. Data was recorded around Rancho Palos Verdes and San Pedro California.

Link

Data format is as follows: filename.jpg angle,year-mm-dd hr:min:sec:millisec

if you use second dataset, you need to convert it in the form of filename.jpg angle

Use python train.py to train the model

Use python run.py to run the model on a live webcam feed

Use python run_dataset_C.py to run the model on the dataset

You will see

video

Use python app.py if you want to see running it on flask then enter into the url http://127.0.0.1:5000/.After that you see

image

when you click show demo

image

File Organization 🗄️

├── selfDrivingCar (Current Directory)
        ├── deploy
        ├── static
        ├── templates
        ├── app.py
        ├── driving_data.py
        ├── modelckpt
        ├── model.py
        ├── requirements.txt
        ├── run_dataset_C.py
        ├── steering_wheel_image.jpg
        ├── train.py
        └── Readme.Md

References 🔱

🔗 Links

portfolio linkedin

Owner
Sagor Saha
Machine learning enthusiast
Sagor Saha
This is code of book "Learn Deep Learning with PyTorch"

深度学习入门之PyTorch Learn Deep Learning with PyTorch 非常感谢您能够购买此书,这个github repository包含有深度学习入门之PyTorch的实例代码。由于本人水平有限,在写此书的时候参考了一些网上的资料,在这里对他们表示敬意。由于深度学习的技术在

Xingyu Liao 2.5k Jan 04, 2023
This repository contains the source code for the paper Tutorial on amortized optimization for learning to optimize over continuous domains by Brandon Amos

Tutorial on Amortized Optimization This repository contains the source code for the paper Tutorial on amortized optimization for learning to optimize

Meta Research 144 Dec 26, 2022
Numbering permanent and deciduous teeth via deep instance segmentation in panoramic X-rays

Numbering permanent and deciduous teeth via deep instance segmentation in panoramic X-rays In this repo, you will find the instructions on how to requ

Intelligent Vision Research Lab 4 Jul 21, 2022
A PyTorch implementation of the baseline method in Panoptic Narrative Grounding (ICCV 2021 Oral)

A PyTorch implementation of the baseline method in Panoptic Narrative Grounding (ICCV 2021 Oral)

Biomedical Computer Vision @ Uniandes 52 Dec 19, 2022
Method for facial emotion recognition compitition of Xunfei and Datawhale .

人脸情绪识别挑战赛-第3名-W03KFgNOc-源代码、模型以及说明文档 队名:W03KFgNOc 排名:3 正确率: 0.75564 队员:yyMoming,xkwang,RichardoMu。 比赛链接:人脸情绪识别挑战赛 文章地址:link emotion 该项目分别训练八个模型并生成csv文

6 Oct 17, 2022
Official pytorch code for SSC-GAN: Semi-Supervised Single-Stage Controllable GANs for Conditional Fine-Grained Image Generation(ICCV 2021)

SSC-GAN_repo Pytorch implementation for 'Semi-Supervised Single-Stage Controllable GANs for Conditional Fine-Grained Image Generation'.PDF SSC-GAN:Sem

tyty 4 Aug 28, 2022
Course on computational design, non-linear optimization, and dynamics of soft systems at UIUC.

Computational Design and Dynamics of Soft Systems · This is a repository that contains the source code for generating the lecture notes, handouts, exe

Tejaswin Parthasarathy 4 Jul 21, 2022
Training BERT with Compute/Time (Academic) Budget

Training BERT with Compute/Time (Academic) Budget This repository contains scripts for pre-training and finetuning BERT-like models with limited time

Intel Labs 263 Jan 07, 2023
Clean Machine Learning, a Coding Kata

Kata: Clean Machine Learning From Dirty Code First, open the Kata in Google Colab (or else download it) You can clone this project and launch jupyter-

Neuraxio 13 Nov 03, 2022
an implementation of softmax splatting for differentiable forward warping using PyTorch

softmax-splatting This is a reference implementation of the softmax splatting operator, which has been proposed in Softmax Splatting for Video Frame I

Simon Niklaus 338 Dec 28, 2022
Code to use Augmented Shapiro Wilks Stopping, as well as code for the paper "Statistically Signifigant Stopping of Neural Network Training"

This codebase is being actively maintained, please create and issue if you have issues using it Basics All data files are included under losses and ea

J K Terry 32 Nov 09, 2021
MARE - Multi-Attribute Relation Extraction

MARE - Multi-Attribute Relation Extraction Repository for the paper submission: #TODO: insert link, when available Environment Tested with Ubuntu 18.0

0 May 11, 2021
Implementations of LSTM: A Search Space Odyssey variants and their training results on the PTB dataset.

An LSTM Odyssey Code for training variants of "LSTM: A Search Space Odyssey" on Fomoro. Check out the blog post. Training Install TensorFlow. Clone th

Fomoro AI 95 Apr 13, 2022
This repo contains the code required to train the multivariate time-series Transformer.

Multi-Variate Time-Series Transformer This repo contains the code required to train the multivariate time-series Transformer. Download the data The No

Gregory Duthé 4 Nov 24, 2022
This PyTorch package implements MoEBERT: from BERT to Mixture-of-Experts via Importance-Guided Adaptation (NAACL 2022).

MoEBERT This PyTorch package implements MoEBERT: from BERT to Mixture-of-Experts via Importance-Guided Adaptation (NAACL 2022). Installation Create an

Simiao Zuo 34 Dec 24, 2022
Elastic weight consolidation technique for incremental learning.

Overcoming-Catastrophic-forgetting-in-Neural-Networks Elastic weight consolidation technique for incremental learning. About Use this API if you dont

Shivam Saboo 89 Dec 22, 2022
Parameterized Explainer for Graph Neural Network

PGExplainer This is a Tensorflow implementation of the paper: Parameterized Explainer for Graph Neural Network https://arxiv.org/abs/2011.04573 NeurIP

Dongsheng Luo 89 Dec 12, 2022
Zeyuan Chen, Yangchao Wang, Yang Yang and Dong Liu.

Principled S2R Dehazing This repository contains the official implementation for PSD Framework introduced in the following paper: PSD: Principled Synt

zychen 78 Dec 30, 2022
Contains a bunch of different python programm tasks

py_tasks Contains a bunch of different python programm tasks Armstrong.py - calculate Armsrong numbers in range from 0 to n with / without cache and c

Dmitry Chmerenko 1 Dec 17, 2021
Repo for the Video Person Clustering dataset, and code for the associated paper

Video Person Clustering Repo for the Video Person Clustering dataset, and code for the associated paper. This reporsitory contains the Video Person Cl

Andrew Brown 47 Nov 02, 2022