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 ๐Ÿ—ผ

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

Code Requirements ๐Ÿฆ„

  • 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 ๐Ÿ—„๏ธ

โ”œโ”€โ”€ selfDrivingCar (Current Directory)
        โ”œโ”€โ”€ deploy
        โ”œโ”€โ”€ static
        โ”œโ”€โ”€ templates
        โ”œโ”€โ”€ app.py
        โ”œโ”€โ”€ driving_data.py
        โ”œโ”€โ”€ modelckpt
        โ”œโ”€โ”€ model.py
        โ”œโ”€โ”€ requirements.txt
        โ”œโ”€โ”€ run_dataset_C.py
        โ”œโ”€โ”€ steering_wheel_image.jpg
        โ”œโ”€โ”€ train.py
        โ””โ”€โ”€ Readme.Md

References ๐Ÿ”ฑ

๐Ÿ”— Links

portfolio linkedin

Owner
Sagor Saha
Machine learning enthusiast
Sagor Saha
SEOVER: Sentence-level Emotion Orientation Vector based Conversation Emotion Recognition Model

SEOVER-Master This code is the implementation of paper๏ผš SEOVER: Sentence-level Emotion Orientation Vector based Conversation Emotion Recognition Model

4 Feb 24, 2022
A TensorFlow 2.x implementation of Masked Autoencoders Are Scalable Vision Learners

Masked Autoencoders Are Scalable Vision Learners A TensorFlow implementation of Masked Autoencoders Are Scalable Vision Learners [1]. Our implementati

Aritra Roy Gosthipaty 59 Dec 10, 2022
CLOOB: Modern Hopfield Networks with InfoLOOB Outperform CLIP

CLOOB: Modern Hopfield Networks with InfoLOOB Outperform CLIP Andreas Fรผrst* 1, Elisabeth Rumetshofer* 1, Viet Tran1, Hubert Ramsauer1, Fei Tang3, Joh

Institute for Machine Learning, Johannes Kepler University Linz 133 Jan 04, 2023
Code & Data for the Paper "Time Masking for Temporal Language Models", WSDM 2022

Time Masking for Temporal Language Models This repository provides a reference implementation of the paper: Time Masking for Temporal Language Models

Guy Rosin 12 Jan 06, 2023
Code for the paper "M2m: Imbalanced Classification via Major-to-minor Translation" (CVPR 2020)

M2m: Imbalanced Classification via Major-to-minor Translation This repository contains code for the paper "M2m: Imbalanced Classification via Major-to

79 Oct 13, 2022
Neural Logic Inductive Learning

Neural Logic Inductive Learning This is the implementation of the Neural Logic Inductive Learning model (NLIL) proposed in the ICLR 2020 paper: Learn

36 Nov 28, 2022
Implementation of Squeezenet in pytorch, pretrained models on Cifar 10 data to come

Pytorch Squeeznet Pytorch implementation of Squeezenet model as described in https://arxiv.org/abs/1602.07360 on cifar-10 Data. The definition of Sque

gaurav pathak 86 Oct 28, 2022
A real world application of a Recurrent Neural Network on a binary classification of time series data

What is this This is a real world application of a Recurrent Neural Network on a binary classification of time series data. This project includes data

Josep Maria Salvia Hornos 2 Jan 30, 2022
This is the codebase for Diffusion Models Beat GANS on Image Synthesis.

This is the codebase for Diffusion Models Beat GANS on Image Synthesis.

OpenAI 3k Dec 26, 2022
OpenMMLab's Next Generation Video Understanding Toolbox and Benchmark

Introduction English | ็ฎ€ไฝ“ไธญๆ–‡ MMAction2 is an open-source toolbox for video understanding based on PyTorch. It is a part of the OpenMMLab project. The m

OpenMMLab 2.7k Jan 07, 2023
Learning Continuous Image Representation with Local Implicit Image Function

LIIF This repository contains the official implementation for LIIF introduced in the following paper: Learning Continuous Image Representation with Lo

Yinbo Chen 1k Dec 25, 2022
Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data

Real-ESRGAN Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data Ported from https://github.com/xinntao/Real-ESRGAN Depend

Holy Wu 44 Dec 27, 2022
Code for HLA-Face: Joint High-Low Adaptation for Low Light Face Detection (CVPR21)

HLA-Face: Joint High-Low Adaptation for Low Light Face Detection The official PyTorch implementation for HLA-Face: Joint High-Low Adaptation for Low L

Wenjing Wang 77 Dec 08, 2022
Semantic Image Synthesis with SPADE

Semantic Image Synthesis with SPADE New implementation available at imaginaire repository We have a reimplementation of the SPADE method that is more

NVIDIA Research Projects 7.3k Jan 07, 2023
Meta Learning Backpropagation And Improving It (VSML)

Meta Learning Backpropagation And Improving It (VSML) This is research code for the NeurIPS 2021 publication Kirsch & Schmidhuber 2021. Many concepts

Louis Kirsch 22 Dec 21, 2022
An official implementation of "Exploiting a Joint Embedding Space for Generalized Zero-Shot Semantic Segmentation" (ICCV 2021) in PyTorch.

Exploiting a Joint Embedding Space for Generalized Zero-Shot Semantic Segmentation This is an official implementation of the paper "Exploiting a Joint

CV Lab @ Yonsei University 35 Oct 26, 2022
Pose Transformers: Human Motion Prediction with Non-Autoregressive Transformers

Pose Transformers: Human Motion Prediction with Non-Autoregressive Transformers This is the repo used for human motion prediction with non-autoregress

Idiap Research Institute 26 Dec 14, 2022
Towards Flexible Blind JPEG Artifacts Removal (FBCNN, ICCV 2021)

Towards Flexible Blind JPEG Artifacts Removal (FBCNN, ICCV 2021) Jiaxi Jiang, Kai Zhang, Radu Timofte Computer Vision Lab, ETH Zurich, Switzerland ๐Ÿ”ฅ

Jiaxi Jiang 282 Jan 02, 2023
Code for Neural-GIF: Neural Generalized Implicit Functions for Animating People in Clothing(ICCV21)

NeuralGIF Code for Neural-GIF: Neural Generalized Implicit Functions for Animating People in Clothing(ICCV21) We present Neural Generalized Implicit F

Garvita Tiwari 104 Nov 18, 2022
A Japanese Medical Information Extraction Toolkit

JaMIE: a Japanese Medical Information Extraction toolkit Joint Japanese Medical Problem, Modality and Relation Recognition The Train/Test phrases requ

7 Dec 12, 2022