Search Results for "train" - Page 10
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Showing 297 open source projects for "train"
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SageMaker MXNet Training Toolkit
Toolkit for running MXNet training scripts on SageMaker
...You can also train and deploy models with Amazon algorithms, which are scalable implementations of core machine learning algorithms that are optimized for SageMaker and GPU training. If you have your own algorithms built into SageMaker compatible Docker containers, you can train and host models using these as well. -
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Tashkeela processed
Tashkeela dataset cleaned and normalized.
...The sentence segmentation is done at usual punctuations such as dots, commas, interrogation/exclamation marks, and line end as well. The partition process is done by shuffling groups of sentences then dividing each group into three parts (Train/Val/Test) and storing them in individual files. The original Tashkeela dataset is available at https://sourceforge.net/projects/tashkeela/ -
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DeepCluster
Deep Clustering for Unsupervised Learning of Visual Features
DeepCluster is a classic self-supervised clustering-based representation learning algorithm that iteratively groups image features and uses the cluster assignments as pseudo-labels to train the network. In each round, features produced by the network are clustered (e.g. k-means), and the cluster IDs become supervision targets in the next epoch, encouraging the model to refine its representation to better separate semantic groups. This alternating “cluster & train” scheme helps the model gradually discover meaningful structure without labels. ... -
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GluonNLP
NLP made easy
GluonNLP is a toolkit that helps you solve NLP problems. It provides easy-to-use tools that helps you load the text data, process the text data, and train models. To facilitate both the engineers and researchers, we provide command-line-toolkits for downloading and processing the NLP datasets. Gluon NLP makes it easy to evaluate and train word embeddings. Here are examples to evaluate the pre-trained embeddings included in the Gluon NLP toolkit as well as example scripts for training embeddings on custom datasets. ... -
Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
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SQLFlow
SQL compiler bridging databases and machine learning workflows
SQLFlow is an open source project designed to bridge the gap between traditional SQL-based data processing and modern machine learning workflows by extending SQL syntax with AI capabilities. It acts as a compiler that translates SQL programs into executable workflows, enabling users to train, evaluate, and deploy machine learning models directly from SQL statements. It integrates with multiple database engines such as MySQL, Hive, and MaxCompute, while also supporting machine learning frameworks like TensorFlow and XGBoost. By embedding machine learning operations into SQL, it removes the need for users to switch between programming languages such as Python or R, simplifying the overall workflow. ... -
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Age and Gender Estimation
Keras implementation of a CNN network for age and gender estimation
Keras implementation of a CNN network for age and gender estimation. This is a Keras implementation of a CNN for estimating age and gender from a face image [1, 2]. In training, the IMDB-WIKI dataset is used. Because the face images in the UTKFace dataset is tightly cropped (there is no margin around the face region), faces should also be cropped in demo.py if weights trained by the UTKFace dataset is used. Please set the margin argument to 0 for tight cropping. You can evaluate a trained... -
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HyperGAN
Composable GAN framework with api and user interface
A composable GAN built for developers, researchers, and artists. HyperGAN builds generative adversarial networks in PyTorch and makes them easy to train and share. HyperGAN is currently in pre-release and open beta. Everyone will have different goals when using hypergan. HyperGAN is currently beta. We are still searching for a default cross-data-set configuration. Each of the examples supports search. Automated search can help find good configurations. If you are unsure, you can start with the 2d-distribution.py. ... -
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Delta ML
Deep learning based natural language and speech processing platform
...DELTA is mainly implemented using TensorFlow and Python 3. DELTA has been used for developing several state-of-the-art algorithms for publications and delivering real production to serve millions of users. It helps you to train, develop, and deploy NLP and/or speech models. Use configuration files to easily tune parameters and network structures. What you see in training is what you get in serving: all data processing and features extraction are integrated into a model graph. Text classification, named entity recognition, question and answering, text summarization, etc. ... -
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Linux-Intelligent-Ocr-Solution
Easy-OCR solution and Tesseract trainer for GNU/Linux
Linux-intelligent-ocr-solution Lios is a free and open source software for converting print in to text using either scanner or a camera, It can also produce text out of scanned images from other sources such as Pdf, Image, Folder containing Images or screenshot. Program is given total accessibility for visually impaired. A Tesseract Trainer GUI is also shipped with this package. Forum : https://groups.google.com/forum/#!forum/lios Video Tutorial :... -
PRTG Catches Network Issues Before They Cause DowntimeReactive troubleshooting usually means hearing about a problem from frustrated users, not your monitoring tool. PRTG sets threshold-based alerts across devices, servers and applications, notifying your team by email, SMS or push the moment a metric crosses a set limit. That means catching a failing disk or overloaded server before it becomes an outage and getting time back from firefighting. Start a free trial and set your first alerts today.
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MMdnn
Tools to help users inter-operate among deep learning frameworks
...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, which means you can train a model with one framework and deploy it with another. During the model conversion, we generate some code snippets to simplify later retraining or inference. We provide a model collection to help you find some popular models. We provide a model visualizer to display the network architecture more intuitively. We provide some guidelines to help you deploy DL models to another hardware platform. -
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AdaNet
Fast and flexible AutoML with learning guarantees
...At each iteration, it measures the ensemble loss for each candidate, and selects the best one to move onto the next iteration. Adaptive neural architecture search and ensemble learning in a single train call. Regression, binary and multi-class classification, and multi-head task support. A tf.estimator.Estimator API for training, evaluation, prediction, and serving models. -
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TFKit
Handling multiple nlp task in one pipeline
...It leverages the use of transformers on many tasks with different models in this all-in-one framework. All you need is a little change of config. You can use tfkit for model training and evaluation with tfkit-train and tfkit-eval. The key to combine different task together is to make different task with same data format. All data will be in csv format - tfkit will use csv for all task, normally it will have two columns, first columns is the input of models, the second column is the output of models. Plane text with no tokenization - there is no need to tokenize text before training, or do re-calculating for tokenization, tfkit will handle it for you. ... -
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DETR
End-to-end object detection with transformers
PyTorch training code and pretrained models for DETR (DEtection TRansformer). We replace the full complex hand-crafted object detection pipeline with a Transformer, and match Faster R-CNN with a ResNet-50, obtaining 42 AP on COCO using half the computation power (FLOPs) and the same number of parameters. Inference in 50 lines of PyTorch. What it is. Unlike traditional computer vision techniques, DETR approaches object detection as a direct set prediction problem. It consists of a set-based... -
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SageMaker Containers
Create SageMaker-compatible Docker containers
Amazon SageMaker is a fully managed service for data science and machine learning (ML) workflows. You can use Amazon SageMaker to simplify the process of building, training, and deploying ML models. To train a model, you can include your training script and dependencies in a Docker container that runs your training code. A container provides an effectively isolated environment, ensuring a consistent runtime and reliable training process. The SageMaker Training Toolkit can be easily added to any Docker container, making it compatible with SageMaker for training models. ... -
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GPT2 for Multiple Languages
GPT2 for Multiple Languages, including pretrained models
...The contents in this repository are for academic research purpose, and we do not provide any conclusive remarks. Research supported with Cloud TPUs from Google's TensorFlow Research Cloud (TFRC) Simplifed GPT2 train scripts（based on Grover, supporting TPUs). Ported bert tokenizer, multilingual corpus compatible. 1.5B GPT2 pretrained Chinese model (~15G corpus, 10w steps). Batteries-included Colab demo. 1.5B GPT2 pretrained Chinese model (~30G corpus, 22w steps). -
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PyTorch GAN Zoo
A mix of GAN implementations including progressive growing
...The project provides modular implementations of popular GAN architectures, including Progressive Growing of GANs (PGAN), DCGAN, and an experimental StyleGAN version. It is built to support both researchers and developers who want to train, evaluate, and extend GANs efficiently across diverse datasets such as CelebA-HQ, FashionGen, DTD, and CIFAR-10. In addition to core GAN training, the repository includes tools for model evaluation, such as Inception Score and SWD metrics, as well as advanced features like GDPP for diverse generation and AC-GAN conditioning for class-specific synthesis. ... -
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End-to-End Negotiator
Deal or No Deal? End-to-End Learning for Negotiation Dialogues
End-to-End Negotiator is a PyTorch-based research framework developed by Facebook AI Research to train neural agents capable of conducting strategic negotiations in natural language. The project implements the models presented in two key papers: “Deal or No Deal? End-to-End Learning for Negotiation Dialogues” and “Hierarchical Text Generation and Planning for Strategic Dialogue”. It enables agents to plan, reason, and communicate effectively to maximize outcomes in multi-turn negotiations over shared resources. ... -
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textgenrnn
Easily train your own text-generating neural network
...Configure RNN size, the number of RNN layers, and whether to use bidirectional RNNs. Train on any generic input text file, including large files. Train models on a GPU and then use them to generate text with a CPU. Utilize a powerful CuDNN implementation of RNNs when trained on the GPU, which massively speeds up training time as opposed to typical LSTM implementations. Train the model using contextual labels, allowing it to learn faster and produce better results in some cases. -
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TensorFlow Object Counting API
The TensorFlow Object Counting API is an open source framework
The TensorFlow Object Counting API is an open source framework built on top of TensorFlow and Keras that makes it easy to develop object counting systems. Please contact if you need professional object detection & tracking & counting project with super high accuracy and reliability! You can train TensorFlow models with your own training data to built your own custom object counter system! If you want to learn how to do it, please check one of the sample projects, which cover some of the theory of transfer learning and show how to apply it in useful projects. The development is on progress! The API will be updated soon, the more talented and light-weight API will be available in this repo! ... -
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SageMaker Chainer Containers
Docker container for running Chainer scripts to train and host Chainer
SageMaker Chainer Containers is an open-source library for making the Chainer framework run on Amazon SageMaker. This repository also contains Dockerfiles which install this library, Chainer, and dependencies for building SageMaker Chainer images. Amazon SageMaker utilizes Docker containers to run all training jobs & inference endpoints. The Docker images are built from the Dockerfiles specified in Docker/. The Docker files are grouped based on Chainer version and separated based on Python... -
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NLP Best Practices
Natural Language Processing Best Practices & Examples
In recent years, natural language processing (NLP) has seen quick growth in quality and usability, and this has helped to drive business adoption of artificial intelligence (AI) solutions. In the last few years, researchers have been applying newer deep learning methods to NLP. Data scientists started moving from traditional methods to state-of-the-art (SOTA) deep neural network (DNN) algorithms which use language models pretrained on large text corpora. This repository contains examples and... -
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Affine Transformation of Virtual Object
Transformation virtual 3D object using a finger gesture-based system
Affine transformation virtual 3D object using a finger gesture-based interactive system in the virtual environment. A convolutional neural network (CNN) based thumb and index fingertip detection system are presented here for seamless interaction with a virtual 3D object in the virtual environment. First, a two-stage CNN is employed to detect the hand and fingertips, and using the information of the fingertip position, the scale, rotation, translation, and in general, the affine... -
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jieba
Stuttering Chinese word segmentation
...The search engine mode, on the basis of the precise mode, divides the long words again to improve the recall rate, which is suitable for word segmentation in search engines. The paddle mode uses the PaddlePaddle deep learning framework to train the sequence labeling (bidirectional GRU) network model to achieve word segmentation. Also supports part-of-speech tagging. To use paddle mode, you need to install paddlepaddle-tiny, pip install paddlepaddle-tiny==1.6.1. Currently paddle mode supports jieba v0.40 and above. For versions below jieba v0.40, please upgrade jieba, pip install jieba --upgrade. -
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Image Super-Resolution (ISR)
Super-scale your images and run experiments with Residual Dense
The goal of this project is to upscale and improve the quality of low-resolution images. This project contains Keras implementations of different Residual Dense Networks for Single Image Super-Resolution (ISR) as well as scripts to train these networks using content and adversarial loss components. Docker scripts and Google Colab notebooks are available to carry training and prediction. Also, we provide scripts to facilitate training on the cloud with AWS and Nvidia-docker with only a few commands. When training your own model, start with only PSNR loss (50+ epochs, depending on the dataset) and only then introduce GANS and feature loss. ... -
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12306 Ticket Buying Assistant
12306 Smart ticket swiping, ticket booking
12306 is an automation and ticket-purchasing tool designed to interact with China’s official railway booking system, providing users with a programmatic way to search, reserve, and purchase train tickets. The project replicates the workflow of the official platform while adding automation features that help users secure tickets in high-demand scenarios. It supports querying schedules, checking seat availability, and performing booking actions through scripts, which reduces the need for manual interaction. The system is particularly useful during peak travel periods when tickets sell out quickly, as it can continuously monitor availability and attempt purchases automatically. ...
