Script that attempts to force M1 macs into RGB mode when used with monitors that are defaulting to YPbPr.

Overview

fix_m1_rgb

Script that attempts to force M1 macs into RGB mode when used with monitors that are defaulting to YPbPr.

No warranty provided for using this script. Use at your own risk.

Instructions

Prerequisites

  1. Make sure you are on Mac OS X 11.4 or higher. Upgrade if you haven't.
  2. Open System Preferences > Displays > Rotate the monitor that's in YPbPr mode in order to force it to write to the relevant plist file. You can unrotate it immediately or allow it to auto-revert.

Running the Script

From your Terminal, run:

# Download the script
curl -o ~/Downloads/fix_m1_rgb.py https://raw.githubusercontent.com/sudowork/fix_m1_rgb/main/fix_m1_rgb.py
# Run a dry run and validate the results
python3 ~/Downloads/fix_m1_rgb.py --dry-run
# Once the results are validated, apply the changes.
# Note: You may be prompted for your password in order to backup and modify files under /Library.
python3 ~/Downloads/fix_m1_rgb.py

Restart your computer after you're done, and if all worked out well, then your monitor should be in RGB mode.

Note: The script will backup your original plist files. In addition, the script does not try to discriminate between various displays, so it will write the PixelEncoding and Range values for all displays with a LinkDescription field.

Kudos

Kudos to @GetVladimir for identifying the plist changes that need to be made.

Owner
Kevin Gao
Kevin Gao
Pretraining Representations For Data-Efficient Reinforcement Learning

Pretraining Representations For Data-Efficient Reinforcement Learning Max Schwarzer, Nitarshan Rajkumar, Michael Noukhovitch, Ankesh Anand, Laurent Ch

Mila 40 Dec 11, 2022
Pytorch domain adaptation package

DomainAdaptation This package is created to tackle the problem of domain shifts when dealing with two domains of different feature distributions. In d

Institute of Computational Perception 7 Oct 22, 2022
A medical imaging framework for Pytorch

Welcome to MedicalTorch MedicalTorch is an open-source framework for PyTorch, implementing an extensive set of loaders, pre-processors and datasets fo

Christian S. Perone 799 Jan 03, 2023
Experiment about Deep Person Re-identification with EfficientNet-v2

We evaluated the baseline with Resnet50 and Efficienet-v2 without using pretrained models. Also Resnet50-IBN-A and Efficientnet-v2 using pretrained on ImageNet. We used two datasets: Market-1501 and

lan.nguyen2k 77 Jan 03, 2023
Learning where to learn - Gradient sparsity in meta and continual learning

Learning where to learn - Gradient sparsity in meta and continual learning In this paper, we investigate gradient sparsity found by MAML in various co

Johannes Oswald 28 Dec 09, 2022
Source code for Task-Aware Variational Adversarial Active Learning

Contrastive Coding for Active Learning under Class Distribution Mismatch Official PyTorch implementation of ["Contrastive Coding for Active Learning u

27 Nov 23, 2022
Weighing Counts: Sequential Crowd Counting by Reinforcement Learning

LibraNet This repository includes the official implementation of LibraNet for crowd counting, presented in our paper: Weighing Counts: Sequential Crow

Hao Lu 18 Nov 05, 2022
This repository contains the code for the paper ``Identifiable VAEs via Sparse Decoding''.

Sparse VAE This repository contains the code for the paper ``Identifiable VAEs via Sparse Decoding''. Data Sources The datasets used in this paper wer

Gemma Moran 17 Dec 12, 2022
An open source python library for automated feature engineering

"One of the holy grails of machine learning is to automate more and more of the feature engineering process." ― Pedro Domingos, A Few Useful Things to

alteryx 6.4k Jan 03, 2023
A Python library for generating new text from existing samples.

ReMarkov is a Python library for generating text from existing samples using Markov chains. You can use it to customize all sorts of writing from birt

8 May 17, 2022
A curated list of neural network pruning resources.

A curated list of neural network pruning and related resources. Inspired by awesome-deep-vision, awesome-adversarial-machine-learning, awesome-deep-learning-papers and Awesome-NAS.

Yang He 1.7k Jan 09, 2023
A multilingual version of MS MARCO passage ranking dataset

mMARCO A multilingual version of MS MARCO passage ranking dataset This repository presents a neural machine translation-based method for translating t

75 Dec 27, 2022
DA2Lite is an automated model compression toolkit for PyTorch.

DA2Lite (Deep Architecture to Lite) is a toolkit to compress and accelerate deep network models. ⭐ Star us on GitHub — it helps!! Frameworks & Librari

Sinhan Kang 7 Mar 22, 2022
Official implementation of VQ-Diffusion

Official implementation of VQ-Diffusion: Vector Quantized Diffusion Model for Text-to-Image Synthesis

Microsoft 592 Jan 03, 2023
A very lightweight monitoring system for Raspberry Pi clusters running Kubernetes.

OMNI A very lightweight monitoring system for Raspberry Pi clusters running Kubernetes. Why? When I finished my Kubernetes cluster using a few Raspber

Matias Godoy 148 Dec 29, 2022
Run Effective Large Batch Contrastive Learning on Limited Memory GPU

Gradient Cache Gradient Cache is a simple technique for unlimitedly scaling contrastive learning batch far beyond GPU memory constraint. This means tr

Luyu Gao 198 Dec 29, 2022
A large-image collection explorer and fast classification tool

IMAX: Interactive Multi-image Analysis eXplorer This is an interactive tool for visualize and classify multiple images at a time. It written in Python

Matias Carrasco Kind 23 Dec 16, 2022
Spherical CNNs

Spherical CNNs Equivariant CNNs for the sphere and SO(3) implemented in PyTorch Overview This library contains a PyTorch implementation of the rotatio

Jonas Köhler 893 Dec 28, 2022
Implementations of orthogonal and semi-orthogonal convolutions in the Fourier domain with applications to adversarial robustness

Orthogonalizing Convolutional Layers with the Cayley Transform This repository contains implementations and source code to reproduce experiments for t

CMU Locus Lab 36 Dec 30, 2022
Sequence-tagging using deep learning

Classification using Deep Learning Requirements PyTorch version = 1.9.1+cu111 Python version = 3.8.10 PyTorch-Lightning version = 1.4.9 Huggingface

Vineet Kumar 2 Dec 20, 2022