Search Results for "source code computer" - Page 12
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Showing 444 open source projects for "source code computer"
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Lucid
A collection of infrastructure and tools for research
Lucid is a collection of infrastructure and tools for research in neural network interpretability. Lucid is research code, not production code. We provide no guarantee it will work for your use case. Lucid is maintained by volunteers who are unable to provide significant technical support. Start visualizing neural networks with no setup. The following notebooks run right from your browser, thanks to Collaboratory. It's a Jupyter notebook environment that requires no setup to use and runs... -
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Gluon CV Toolkit
Gluon CV Toolkit
GluonCV provides implementations of state-of-the-art (SOTA) deep learning algorithms in computer vision. It aims to help engineers, researchers, and students quickly prototype products, validate new ideas and learn computer vision. It features training scripts that reproduce SOTA results reported in latest papers, a large set of pre-trained models, carefully designed APIs and easy-to-understand implementations and community support. From fundamental image classification, object detection,... -
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Keepsake
Version control for machine learning
Keepsake is a Python library that uploads files and metadata (like hyperparameters) to Amazon S3 or Google Cloud Storage. You can get the data back out using the command-line interface or a notebook. -
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Semantic Segmentation in PyTorch
Semantic segmentation models, datasets & losses implemented in PyTorch
Semantic segmentation models, datasets and losses implemented in PyTorch. PyTorch and Torchvision needs to be installed before running the scripts, together with PIL and opencv for data-preprocessing and tqdm for showing the training progress. PyTorch v1.1 is supported (using the new supported tensoboard); can work with earlier versions, but instead of using tensoboard, use tensoboardX. Poly learning rate, where the learning rate is scaled down linearly from the starting value down to zero... -
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PyTorch SimCLR
PyTorch implementation of SimCLR: A Simple Framework
For quite some time now, we know about the benefits of transfer learning in Computer Vision (CV) applications. Nowadays, pre-trained Deep Convolution Neural Networks (DCNNs) are the first go-to pre-solutions to learn a new task. These large models are trained on huge supervised corpora, like the ImageNet. And most important, their features are known to adapt well to new problems. This is particularly interesting when annotated training data is scarce. In situations like this, we take the... -
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BudgetML
Deploy a ML inference service on a budget in 10 lines of code
Deploy a ML inference service on a budget in less than 10 lines of code. BudgetML is perfect for practitioners who would like to quickly deploy their models to an endpoint, but not waste a lot of time, money, and effort trying to figure out how to do this end-to-end. We built BudgetML because it's hard to find a simple way to get a model in production fast and cheaply. Deploying from scratch involves learning too many different concepts like SSL certificate generation, Docker, REST,... -
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TTS
Deep learning for text to speech
TTS is a library for advanced Text-to-Speech generation. It's built on the latest research, was designed to achieve the best trade-off among ease-of-training, speed, and quality. TTS comes with pre-trained models, tools for measuring dataset quality, and is already used in 20+ languages for products and research projects. Released models in PyTorch, Tensorflow and TFLite. Tools to curate Text2Speech datasets underdataset_analysis. Demo server for model testing. Notebooks for extensive model... -
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This Matlab code is used for demonstration of the effect of CDF-TS as a preprocessing method to transform data. Written by Ye Zhu, Deakin University, April 2021, version 1.0. This software is under GNU General Public License version 3.0 (GPLv3) This code is a demo of method described by the following publication: Zhu, Y., Ting, K.M., Carman, M. and Angelova, M., 2021, April. CDF Transform-and-Shift: An effective way to deal with datasets of inhomogeneous cluster densities. Pattern...
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Awesome AI-ML-DL
Awesome Artificial Intelligence, Machine Learning and Deep Learning
Awesome Artificial Intelligence, Machine Learning and Deep Learning as we learn it. Study notes and a curated list of awesome resources of such topics. This repo is dedicated to engineers, developers, data scientists and all other professions that take interest in AI, ML, DL and related sciences. To make learning interesting and to create a place to easily find all the necessary material. Please contribute, watch, star, fork and share the repo with others in your community. -
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TensorLayer
Deep learning and reinforcement learning library for scientists
...It provides an extensive collection of customizable neural layers to build advanced AI models quickly, based on this, the community open-sourced mass tutorials and applications. TensorLayer is awarded the 2017 Best Open Source Software by the ACM Multimedia Society. This project can also be found at OpenI and Gitee. 3.0.0 has been pre-released, the current version supports TensorFlow, MindSpore and PaddlePaddle (partial) as the backends, allowing users to run the code on different hardware like Nvidia-GPU and Huawei-Ascend. In the future, it will support TensorFlow, MindSpore, PaddlePaddle, PyTorch and other backends. ... -
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Cloud Annotations
A fast, easy and collaborative open source image annotation tool
Learn computer vision & AI by building real-world applications. Learn to build and train computer vision models—then show off your skills in an interactive web application. Build impressive applications and learn coveted skills. The examples below were created by the Skills Network Team—right here in CV Studio. Create your own project dataset by uploading images and videos. Coming soon, you'll be able to use a pre-compiled dataset so you can hit the ground running. Creating image annotations... -
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transferlearning-tutorial
Tutorial on applied transfer learning
A bilingual (English/Chinese) concise tutorial on applied transfer learning, hosted by Jindong Wang. It includes LaTeX source for a compact handbook covering theory, algorithms, surveys, and code in MATLAB and Python. -
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OpenPose
Real-time multi-person keypoint detection library for body, face, etc.
OpenPose has represented the first real-time multi-person system to jointly detect human body, hand, facial, and foot keypoints (in total 135 keypoints) on single images. It is authored by Ginés Hidalgo, Zhe Cao, Tomas Simon, Shih-En Wei, Yaadhav Raaj, Hanbyul Joo, and Yaser Sheikh. It is maintained by Ginés Hidalgo and Yaadhav Raaj. OpenPose would not be possible without the CMU Panoptic Studio dataset. We would also like to thank all the people who has helped OpenPose in any way. 15, 18 or... -
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GIMP ML
AI for GNU Image Manipulation Program
This repository introduces GIMP3-ML, a set of Python plugins for the widely popular GNU Image Manipulation Program (GIMP). It enables the use of recent advances in computer vision to the conventional image editing pipeline. Applications from deep learning such as monocular depth estimation, semantic segmentation, mask generative adversarial networks, image super-resolution, de-noising and coloring have been incorporated with GIMP through Python-based plugins. Additionally, operations on... -
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Lambda Networks
Implementation of LambdaNetworks, a new approach to image recognition
Implementation of λ Networks, a new approach to image recognition that reaches SOTA on ImageNet. The new method utilizes λ layer, which captures interactions by transforming contexts into linear functions, termed lambdas, and applying these linear functions to each input separately. Shinel94 has added a Keras implementation! It won't be officially supported in this repository, so either copy / paste the code under ./lambda_networks/tfkeras.py or make sure to install tensorflow and keras... -
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fastNLP
fastNLP: A Modularized and Extensible NLP Framework
fastNLP is a lightweight framework for natural language processing (NLP), the goal is to quickly implement NLP tasks and build complex models. A unified Tabular data container simplifies the data preprocessing process. Built-in Loader and Pipe for multiple datasets, eliminating the need for preprocessing code. Various convenient NLP tools, such as Embedding loading (including ELMo and BERT), intermediate data cache, etc.. Provide a variety of neural network components and recurrence models... -
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Effective TensorFlow 2
TensorFlow tutorials and best practices
Effective Tensorflow is an open-source repository that provides tutorials and best practices for developing machine learning models using the TensorFlow framework. The project focuses on helping developers write efficient, maintainable, and reliable TensorFlow code when building deep learning systems. It includes practical guidelines that explain common pitfalls in neural network training, such as numerical instability and gradient-related issues. -
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wav2letter++
Facebook AI research's automatic speech recognition toolkit
First, install Flashlight (using the 0.3 branch is required) with the ASR application. This repository includes recipes to reproduce the following research papers as well as pre-trained models. All results reproduction must use Flashlight <= 0.3.2 for exact reproducibility. At least one of LZMA, BZip2, or Z is required for LM compression with KenLM. It is highly recommended to build KenLM with position-independent code (-fPIC) enabled, to enable python compatibility. After installing, run... -
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Computer Vision Pretrained Models
A collection of computer vision pre-trained models
A pre-trained model is a model created by someone else to solve a similar problem. Instead of building a model from scratch to solve a similar problem, we can use the model trained on other problem as a starting point. A pre-trained model may not be 100% accurate in your application. For example, if you want to build a self-learning car. You can spend years building a decent image recognition algorithm from scratch or you can take the inception model (a pre-trained model) from Google which... -
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Isolation‐based anomaly detection
Isolation‐based anomaly detection using nearest‐neighbor ensembles
This site provides the source code of Isolation‐based anomaly detection (iNNE). https://onlinelibrary.wiley.com/doi/abs/10.1111/coin.12156 Bandaragoda, T.R., Ting, K.M., Albrecht, D., Liu, F.T., Zhu, Y. and Wells, J.R., 2018. Isolation‐based anomaly detection using nearest‐neighbor ensembles. Computational Intelligence, 34(4), pp.968-998. -
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Turi Create
Simplifies the development of custom machine learning models
...If you want to build Turi Create from source, see BUILD.md. Turi Create does not require a GPU, but certain models can be accelerated 9-13x by utilizing a GPU. -
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TensorFlow 2.0 Tutorials
TensorFlow 2.x version's Tutorials and Examples
...Each section of the repository includes runnable code and structured experiments designed to illustrate how different architectures and algorithms function in real applications. The tutorials use well-known benchmark datasets such as MNIST, CIFAR, and Fashion-MNIST to demonstrate practical model training and evaluation workflows. -
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ML.NET Samples
Samples for ML.NET, an open source and cross-platform machine learning
ML.NET is a cross-platform open-source machine learning framework that makes machine learning accessible to .NET developers. In this GitHub repo, we provide samples that will help you get started with ML.NET and how to infuse ML into existing and new .NET apps. We're working on simplifying ML.NET usage with additional technologies that automate the creation of the model for you so you don't need to write the code by yourself to train a model, you simply need to provide your datasets. ... -
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Stable Baselines
A fork of OpenAI Baselines, implementations of reinforcement learning
Stable Baselines is a set of improved implementations of reinforcement learning algorithms based on OpenAI Baselines. You can read a detailed presentation of Stable Baselines in the Medium article. These algorithms will make it easier for the research community and industry to replicate, refine, and identify new ideas, and will create good baselines to build projects on top of. We expect these tools will be used as a base around which new ideas can be added, and as a tool for comparing a new... -
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MMdnn
Tools to help users inter-operate among deep learning frameworks
MMdnn is a set of tools to help users inter-operate among different deep learning frameworks. E.g. model conversion and visualization. Convert models between Caffe, Keras, MXNet, Tensorflow, CNTK, PyTorch Onnx and CoreML. MMdnn is a comprehensive and cross-framework tool to convert, visualize and diagnose deep learning (DL) models. The "MM" stands for model management, and "dnn" is the acronym of deep neural network. We implement a universal converter to convert DL models between frameworks,...
