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
Alignment Attention Fusion framework for Few-Shot Object Detection

AAF framework Framework generalities This repository contains the code of the AAF framework proposed in this paper. The main idea behind this work is

Pierre Le Jeune 20 Dec 16, 2022
CAR-API: Cityscapes Attributes Recognition API

CAR-API: Cityscapes Attributes Recognition API This is the official api to download and fetch attributes annotations for Cityscapes Dataset. Content I

Kareem Metwaly 5 Dec 22, 2022
[ICCV 2021] Amplitude-Phase Recombination: Rethinking Robustness of Convolutional Neural Networks in Frequency Domain

Amplitude-Phase Recombination (ICCV'21) Official PyTorch implementation of "Amplitude-Phase Recombination: Rethinking Robustness of Convolutional Neur

Guangyao Chen 53 Oct 05, 2022
Official repository for the paper "Can You Learn an Algorithm? Generalizing from Easy to Hard Problems with Recurrent Networks"

Easy-To-Hard The official repository for the paper "Can You Learn an Algorithm? Generalizing from Easy to Hard Problems with Recurrent Networks". Gett

Avi Schwarzschild 52 Sep 08, 2022
A simple interface for editing natural photos with generative neural networks.

Neural Photo Editor A simple interface for editing natural photos with generative neural networks. This repository contains code for the paper "Neural

Andy Brock 2.1k Dec 29, 2022
A Loss Function for Generative Neural Networks Based on Watson’s Perceptual Model

This repository contains the similarity metrics designed and evaluated in the paper, and instructions and code to re-run the experiments. Implementation in the deep-learning framework PyTorch

Steffen 86 Dec 27, 2022
ALL Snow Removed: Single Image Desnowing Algorithm Using Hierarchical Dual-tree Complex Wavelet Representation and Contradict Channel Loss (HDCWNet)

ALL Snow Removed: Single Image Desnowing Algorithm Using Hierarchical Dual-tree Complex Wavelet Representation and Contradict Channel Loss (HDCWNet) (

Wei-Ting Chen 49 Dec 27, 2022
Neuron Merging: Compensating for Pruned Neurons (NeurIPS 2020)

Neuron Merging: Compensating for Pruned Neurons Pytorch implementation of Neuron Merging: Compensating for Pruned Neurons, accepted at 34th Conference

Woojeong Kim 33 Dec 30, 2022
Wordle Env: A Daily Word Environment for Reinforcement Learning

Wordle Env: A Daily Word Environment for Reinforcement Learning Setup Steps: git pull [email&#

2 Mar 28, 2022
Using NumPy to solve the equations of fluid mechanics together with Finite Differences, explicit time stepping and Chorin's Projection methods

Computational Fluid Dynamics in Python Using NumPy to solve the equations of fluid mechanics 🌊 🌊 🌊 together with Finite Differences, explicit time

Felix Köhler 4 Nov 12, 2022
natural image generation using ConvNets

The Eyescream Project Generating Natural Images using Neural Networks. For our research summary on this work, please read the Arxiv paper: http://arxi

Meta Archive 601 Nov 23, 2022
[CVPR 2022] Deep Equilibrium Optical Flow Estimation

Deep Equilibrium Optical Flow Estimation This is the official repo for the paper Deep Equilibrium Optical Flow Estimation (CVPR 2022), by Shaojie Bai*

CMU Locus Lab 136 Dec 18, 2022
Fuzzing JavaScript Engines with Aspect-preserving Mutation

DIE Repository for "Fuzzing JavaScript Engines with Aspect-preserving Mutation" (in S&P'20). You can check the paper for technical details. Environmen

gts3.org (<a href=[email protected])"> 190 Dec 11, 2022
MODALS: Modality-agnostic Automated Data Augmentation in the Latent Space

Update (20 Jan 2020): MODALS on text data is avialable MODALS MODALS: Modality-agnostic Automated Data Augmentation in the Latent Space Table of Conte

38 Dec 15, 2022
Leaderboard and Visualization for RLCard

RLCard Showdown This is the GUI support for the RLCard project and DouZero project. RLCard-Showdown provides evaluation and visualization tools to hel

Data Analytics Lab at Texas A&M University 246 Dec 26, 2022
PyTorch Implementation of VAENAR-TTS: Variational Auto-Encoder based Non-AutoRegressive Text-to-Speech Synthesis.

VAENAR-TTS - PyTorch Implementation PyTorch Implementation of VAENAR-TTS: Variational Auto-Encoder based Non-AutoRegressive Text-to-Speech Synthesis.

Keon Lee 67 Nov 14, 2022
Cmsc11 arcade - Final Project for CMSC11

cmsc11_arcade Final Project for CMSC11 Developers: Limson, Mark Vincent Peñafiel

Gregory 1 Jan 18, 2022
基于Pytorch实现优秀的自然图像分割框架!(包括FCN、U-Net和Deeplab)

语义分割学习实验-基于VOC数据集 usage: 下载VOC数据集,将JPEGImages SegmentationClass两个文件夹放入到data文件夹下。 终端切换到目标目录,运行python train.py -h查看训练 (torch) Li Xiang 28 Dec 21, 2022

AttentionGAN for Unpaired Image-to-Image Translation & Multi-Domain Image-to-Image Translation

AttentionGAN-v2 for Unpaired Image-to-Image Translation AttentionGAN-v2 Framework The proposed generator learns both foreground and background attenti

Hao Tang 530 Dec 27, 2022
The implemetation of Dynamic Nerual Garments proposed in Siggraph Asia 2021

DynamicNeuralGarments Introduction This repository contains the implemetation of Dynamic Nerual Garments proposed in Siggraph Asia 2021. ./GarmentMoti

42 Dec 27, 2022