MAME is a multi-purpose emulation framework.

Related tags

Deep Learningmame
Overview

MAME

Join the chat at https://gitter.im/mamedev/mame

Build status for tiny build only, containing just core parts of project:

OS/Compiler Status
Linux GCC / OSX Clang Build Status
Windows MinGW Build Status
Windows MSVC Build status

Static analysis status for entire build (except for third-party parts of project):

Coverity Scan Status

What is MAME?

MAME is a multi-purpose emulation framework.

MAME's purpose is to preserve decades of software history. As electronic technology continues to rush forward, MAME prevents this important "vintage" software from being lost and forgotten. This is achieved by documenting the hardware and how it functions. The source code to MAME serves as this documentation. The fact that the software is usable serves primarily to validate the accuracy of the documentation (how else can you prove that you have recreated the hardware faithfully?). Over time, MAME (originally stood for Multiple Arcade Machine Emulator) absorbed the sister-project MESS (Multi Emulator Super System), so MAME now documents a wide variety of (mostly vintage) computers, video game consoles and calculators, in addition to the arcade video games that were its initial focus.

How to compile?

If you're on a *NIX or OSX system, it could be as easy as typing

make

for a MAME build,

make SUBTARGET=arcade

for an arcade-only build, or

make SUBTARGET=mess

for MESS build.

See the Compiling MAME page on our documentation site for more information, including prerequisites for Mac OS X and popular Linux distributions.

For recent versions of OSX you need to install Xcode including command-line tools and SDL 2.0.

For Windows users, we provide a ready-made build environment based on MinGW-w64.

Visual Studio builds are also possible, but you still need build environment based on MinGW-w64. In order to generate solution and project files just run:

make vs2017

or use this command to build it directly using msbuild

make vs2017 MSBUILD=1

Where can I find out more?

Contributing

Coding standard

MAME source code should be viewed and edited with your editor set to use four spaces per tab. Tabs are used for initial indentation of lines, with one tab used per indentation level. Spaces are used for other alignment within a line.

Some parts of the code follow Allman style; some parts of the code follow K&R style -- mostly depending on who wrote the original version. Above all else, be consistent with what you modify, and keep whitespace changes to a minimum when modifying existing source. For new code, the majority tends to prefer Allman style, so if you don't care much, use that.

All contributors need to either add a standard header for license info (on new files) or inform us of their wishes regarding which of the following licenses they would like their code to be made available under: the BSD-3-Clause license, the LGPL-2.1, or the GPL-2.0.

License

The MAME project as a whole is distributed under the terms of the GNU General Public License, version 2 or later (GPL-2.0+), since it contains code made available under multiple GPL-compatible licenses. A great majority of files (over 90% including core files) are under the BSD-3-Clause License and we would encourage new contributors to distribute files under this license.

Please note that MAME is a registered trademark of Gregory Ember, and permission is required to use the "MAME" name, logo, or wordmark.

Copyright (C) 1997-2019  MAMEDev and contributors

This program is free software; you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation; either version 2 of the License, or
(at your option) any later version.

This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU General Public License for more details.

You should have received a copy of the GNU General Public License along
with this program; if not, write to the Free Software Foundation, Inc.,
51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.

Please see LICENSE.md for further details.

Owner
Michael Murray
Michael Murray
Fast image augmentation library and an easy-to-use wrapper around other libraries

Albumentations Albumentations is a Python library for image augmentation. Image augmentation is used in deep learning and computer vision tasks to inc

11.4k Jan 09, 2023
Machine learning and Deep learning models, deploy on telegram (the best social media)

Semi Intelligent BOT The project involves : Classifying fake news Classifying objects such as aeroplane, automobile, bird, cat, deer, dog, frog, horse

MohammadReza Norouzi 5 Mar 06, 2022
Source Code of NeurIPS21 paper: Recognizing Vector Graphics without Rasterization

YOLaT-VectorGraphicsRecognition This repository is the official PyTorch implementation of our NeurIPS-2021 paper: Recognizing Vector Graphics without

Microsoft 49 Dec 20, 2022
A web porting for NVlabs' StyleGAN2, to facilitate exploring all kinds characteristic of StyleGAN networks

This project is a web porting for NVlabs' StyleGAN2, to facilitate exploring all kinds characteristic of StyleGAN networks. Thanks for NVlabs' excelle

K.L. 150 Dec 15, 2022
Python 3 module to print out long strings of text with intervals of time inbetween

Python-Fastprint Python 3 module to print out long strings of text with intervals of time inbetween Install: pip install fastprint Sync Usage: from fa

Kainoa Kanter 2 Jun 27, 2022
Attention-based Transformation from Latent Features to Point Clouds (AAAI 2022)

Attention-based Transformation from Latent Features to Point Clouds This repository contains a PyTorch implementation of the paper: Attention-based Tr

12 Nov 11, 2022
《Train in Germany, Test in The USA: Making 3D Object Detectors Generalize》(CVPR 2020)

Train in Germany, Test in The USA: Making 3D Object Detectors Generalize This paper has been accpeted by Conference on Computer Vision and Pattern Rec

Xiangyu Chen 101 Jan 02, 2023
Clairvoyance: a Unified, End-to-End AutoML Pipeline for Medical Time Series

Clairvoyance: A Pipeline Toolkit for Medical Time Series Authors: van der Schaar Lab This repository contains implementations of Clairvoyance: A Pipel

van_der_Schaar \LAB 89 Dec 07, 2022
FaceAnon - Anonymize people in images and videos using yolov5-crowdhuman

Face Anonymizer Blur faces from image and video files in /input/ folder. Require

22 Nov 03, 2022
Graduation Project

Gesture-Detection-and-Depth-Estimation This is my graduation project. (1) In this project, I use the YOLOv3 object detection model to detect gesture i

ChaosAT 1 Nov 23, 2021
YOLOX Win10 Project

Introduction 这是一个用于Windows训练YOLOX的项目,相比于官方项目,做了一些适配和修改: 1、解决了Windows下import yolox失败,No such file or directory: 'xxx.xml'等路径问题 2、CUDA out of memory等显存不

5 Jun 08, 2022
Code repository for the paper "Doubly-Trained Adversarial Data Augmentation for Neural Machine Translation" with instructions to reproduce the results.

Doubly Trained Neural Machine Translation System for Adversarial Attack and Data Augmentation Languages Experimented: Data Overview: Source Target Tra

Steven Tan 1 Aug 18, 2022
Automatic Calibration for Non-repetitive Scanning Solid-State LiDAR and Camera Systems

ACSC Automatic extrinsic calibration for non-repetitive scanning solid-state LiDAR and camera systems. System Architecture 1. Dependency Tested with U

KINO 192 Dec 13, 2022
Small little script to scrape, parse and check for active tor nodes. Can be used as proxies.

TorScrape TorScrape is a small but useful script made in python that scrapes a website for active tor nodes, parse the html and then save the nodes in

5 Dec 04, 2022
Enabling Lightweight Fine-tuning for Pre-trained Language Model Compression based on Matrix Product Operators

Enabling Lightweight Fine-tuning for Pre-trained Language Model Compression based on Matrix Product Operators This is our Pytorch implementation for t

RUCAIBox 12 Jul 22, 2022
Pretrained language model and its related optimization techniques developed by Huawei Noah's Ark Lab.

Pretrained Language Model This repository provides the latest pretrained language models and its related optimization techniques developed by Huawei N

HUAWEI Noah's Ark Lab 2.6k Jan 01, 2023
Using Machine Learning to Create High-Res Fine Art

BIG.art: Using Machine Learning to Create High-Res Fine Art How to use GLIDE and BSRGAN to create ultra-high-resolution paintings with fine details By

Robert A. Gonsalves 13 Nov 27, 2022
Official implementation of "DSP: Dual Soft-Paste for Unsupervised Domain Adaptive Semantic Segmentation"

DSP Official implementation of "DSP: Dual Soft-Paste for Unsupervised Domain Adaptive Semantic Segmentation". Accepted by ACM Multimedia 2021. Authors

20 Oct 24, 2022
This repository contains the DendroMap implementation for scalable and interactive exploration of image datasets in machine learning.

DendroMap DendroMap is an interactive tool to explore large-scale image datasets used for machine learning. A deep understanding of your data can be v

DIV Lab 33 Dec 30, 2022
Unsupervised Feature Loss (UFLoss) for High Fidelity Deep learning (DL)-based reconstruction

Unsupervised Feature Loss (UFLoss) for High Fidelity Deep learning (DL)-based reconstruction Official github repository for the paper High Fidelity De

28 Dec 16, 2022