Search Results for "model train design" - Page 7
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Showing 212 open source projects for "model train design"
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1
ConvNeXt
Code release for ConvNeXt model
...The repository provides pretrained models, training recipes, and ablation studies demonstrating how incremental design choices collectively yield state-of-the-art performance. -
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Guided Diffusion
Codebase for Diffusion Models Beat GANS on Image Synthesis
...It is derived from OpenAI’s improved-diffusion work, enhanced to include guided generation where a classifier (or other guidance mechanism) can steer sampling toward desired classes or attributes. The code provides model definitions (UNet, diffusion schedules), sampling and training scripts, and utilities for guidance and evaluation. A key insight is that combining diffusion sampling with classifier gradients allows fine control over the generated images, trading off diversity vs fidelity. The repository includes scripts such as image_train.py, image_sample.py, and classifier_train.py to train diffusion models, generate samples, and train guiding classifiers. ... -
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PyTorch Transfer-Learning-Library
Transfer Learning Library for Domain Adaptation, Task Adaptation, etc.
TLlib is an open-source and well-documented library for Transfer Learning. It is based on pure PyTorch with high performance and friendly API. Our code is pythonic, and the design is consistent with torchvision. You can easily develop new algorithms or readily apply existing algorithms. We appreciate all contributions. If you are planning to contribute back bug-fixes, please do so without any further discussion. If you plan to contribute new features, utility functions or extensions, please... -
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Fairseq
Facebook AI Research Sequence-to-Sequence Toolkit written in Python
Fairseq(-py) is a sequence modeling toolkit that allows researchers and developers to train custom models for translation, summarization, language modeling and other text generation tasks. We provide reference implementations of various sequence modeling papers. Recent work by Microsoft and Google has shown that data parallel training can be made significantly more efficient by sharding the model parameters and optimizer state across data parallel workers. -
Grafana: The open and composable observability platformGrafana is the open source analytics & monitoring solution for every database.
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LaMDA-pytorch
Open-source pre-training implementation of Google's LaMDA in PyTorch
Open-source pre-training implementation of Google's LaMDA research paper in PyTorch. The totally not sentient AI. This repository will cover the 2B parameter implementation of the pre-training architecture as that is likely what most can afford to train. You can review Google's latest blog post from 2022 which details LaMDA here. You can also view their previous blog post from 2021 on the model. -
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DomE
Implements a reference architecture for creating information systems
DomE Experiment is an implementation of a reference architecture for creating information systems from the automated evolution of the domain model. The architecture comprises elements that guarantee user access through automatically generated interfaces for various devices, integration with external information sources, data and operations security, automatic generation of analytical information, and automatic control of business processes. All these features are generated from the domain model, which is, in turn, continuously evolved from interactions with the user or autonomously by the system itself. ... -
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Catalyst
Accelerated deep learning R&D
...It allows you to write compact but full-featured Deep Learning pipelines with just a few lines of code. With Catalyst you get a full set of features including a training loop with metrics, model checkpointing and more, all without the boilerplate. Catalyst is focused on reproducibility, rapid experimentation, and codebase reuse so you can break the cycle of writing another regular train loop and make something totally new. Catalyst is compatible with Python 3.6+. PyTorch 1.1+, and has been tested on Ubuntu 16.04/18.04/20.04, macOS 10.15, Windows 10 and Windows Subsystem for Linux. ... -
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Guild AI
Experiment tracking, ML developer tools
Guild AI is an open-source experiment tracking toolkit designed to bring systematic control to machine learning workflows, enabling users to build better models faster. It automatically captures every detail of training runs as unique experiments, facilitating comprehensive tracking and analysis. Users can compare and analyze runs to deepen their understanding and incrementally improve models. Guild AI simplifies hyperparameter tuning by applying state-of-the-art algorithms through... -
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YOLOv3
Object detection architectures and models pretrained on the COCO data
Fast, precise and easy to train, YOLOv5 has a long and successful history of real time object detection. Treat YOLOv5 as a university where you'll feed your model information for it to learn from and grow into one integrated tool. You can get started with less than 6 lines of code. with YOLOv5 and its Pytorch implementation. Have a go using our API by uploading your own image and watch as YOLOv5 identifies objects using our pretrained models. -
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YOLOX
YOLOX is a high-performance anchor-free YOLO, exceeding yolov3~v5
...Except special cases, we always recommend using our COCO pre-trained weights for initializing the model. As YOLOX is an anchor-free detector with only several hyper-parameters, most of the time good results can be obtained with no changes to the models or training settings. -
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Texar-PyTorch
Integrating the Best of TF into PyTorch, for Machine Learning
...Texar-PyTorch integrates many of the best features of TensorFlow into PyTorch, delivering highly usable and customizable modules superior to PyTorch native ones. Texar-PyTorch (this repo) and Texar-TF have mostly the same interfaces. Both further combine the best design of TF and PyTorch. Data processing, model architectures, loss functions, training and inference algorithms, evaluation, etc. -
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Voice Cloning App
A Python/Pytorch app for easily synthesising human voices
...Local & remote training. Easy train start/stop. Data importing/exporting. -
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Big Sleep
A simple command line tool for text to image generation
A simple command line tool for text to image generation, using OpenAI's CLIP and a BigGAN. Ryan Murdock has done it again, combining OpenAI's CLIP and the generator from a BigGAN! This repository wraps up his work so it is easily accessible to anyone who owns a GPU. You will be able to have the GAN dream-up images using natural language with a one-line command in the terminal. User-made notebook with bug fixes and added features, like google drive integration. Images will be saved to... -
14
igel
Machine learning tool that allows you to train and test models
A delightful machine learning tool that allows you to train/fit, test, and use models without writing code. The goal of the project is to provide machine learning for everyone, both technical and non-technical users. I sometimes needed a tool sometimes, which I could use to fast create a machine learning prototype. Whether to build some proof of concept, create a fast draft model to prove a point or use auto ML. -
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MaskFormer
Per-Pixel Classification is Not All You Need for Semantic Segmentation
...Built on top of Detectron2, it supports a wide range of datasets including ADE20K, Cityscapes, COCO-Stuff, and Mapillary Vistas, and provides pretrained baselines for each. The model achieves strong performance and scalability while simplifying training and evaluation workflows. Its successor, Mask2Former, extends the same meta-architecture to achieve state-of-the-art results across all major segmentation benchmarks. MaskFormer’s modular design, dataset integration, and compatibility with existing Detectron2 models make it an essential research tool. -
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GPT Neo
An implementation of model parallel GPT-2 and GPT-3-style models
An implementation of model & data parallel GPT3-like models using the mesh-tensorflow library. If you're just here to play with our pre-trained models, we strongly recommend you try out the HuggingFace Transformer integration. Training and inference is officially supported on TPU and should work on GPU as well. This repository will be (mostly) archived as we move focus to our GPU-specific repo, GPT-NeoX. NB, while neo can technically run a training step at 200B+ parameters, it is very... -
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TorchGAN
Research Framework for easy and efficient training of GANs
The torchgan package consists of various generative adversarial networks and utilities that have been found useful in training them. This package provides an easy-to-use API which can be used to train popular GANs as well as develop newer variants. The core idea behind this project is to facilitate easy and rapid generative adversarial model research. TorchGAN is a Pytorch-based framework for designing and developing Generative Adversarial Networks. This framework has been designed to provide building blocks for popular GANs and also to allow customization for cutting-edge research. ... -
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Kashgari
Kashgari is a production-level NLP Transfer learning framework
Kashgari is a simple and powerful NLP Transfer learning framework, build a state-of-art model in 5 minutes for named entity recognition (NER), part-of-speech tagging (PoS), and text classification tasks. -
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ReinventCommunity
Jupyter Notebook tutorials for REINVENT 3.2
This repository is a collection of useful jupyter notebooks, code snippets and example JSON files illustrating the use of Reinvent 3.2. -
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Transformer TTS
Implementation of a Transformer based neural network
...The system separates alignment learning and acoustic modeling: an autoregressive Transformer is used as an aligner to extract phoneme-to-frame durations, while a non-autoregressive “ForwardTransformer” generates mel-spectrograms conditioned on text and durations. This design addresses common autoregressive issues such as repetition, skipped words, and unstable attention, and results in robust, fast synthesis where all frames are predicted in parallel. The repository ships with tooling to build datasets (especially LJSpeech) and create training data, plus scripts to train both the aligner and the TTS model, monitor training with TensorBoard, and resume or reset training runs. -
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Old Photo Restoration
Bringing Old Photo Back to Life (CVPR 2020 oral)
...Unlike conventional restoration tasks that can be solved through supervised learning, the degradation in real photos is complex and the domain gap between synthetic images and real old photos makes the network fail to generalize. Therefore, we propose a novel triplet domain translation network by leveraging real photos along with massive synthetic image pairs. Specifically, we train two variational autoencoders (VAEs) to respectively transform old photos and clean photos into two latent spaces. And the translation between these two latent spaces is learned with synthetic paired data. This translation generalizes well to real photos because the domain gap is closed in the compact latent space. Besides, to address multiple degradations mixed in one old photo, we design a global branch with a partial nonlocal block targeting to the structured defects, such as scratches and dust spots. -
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AliceMind
ALIbaba's Collection of Encoder-decoders from MinD
This repository provides pre-trained encoder-decoder models and its related optimization techniques developed by Alibaba's MinD (Machine IntelligeNce of Damo) Lab. Pre-trained models for natural language understanding (NLU). We extend BERT to a new model, StructBERT, by incorporating language structures into pre-training. Specifically, we pre-train StructBERT with two auxiliary tasks to make the most of the sequential order of words and sentences, which leverage language structures at the word and sentence levels, respectively. Pre-trained models for natural language generation (NLG). ... -
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VITS
Conditional Variational Autoencoder with Adversarial Learning
VITS is a foundational research implementation of “VITS: Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech,” a well-known neural TTS architecture. Unlike traditional two-stage systems that separately train an acoustic model and a vocoder, VITS trains an end-to-end model that maps text directly to waveform using a conditional variational autoencoder combined with normalizing flows and adversarial training. This architecture enables parallel generation (fast inference) while achieving speech quality that rivals or surpasses many two-stage systems. ... -
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FARM
Fast & easy transfer learning for NLP
...Modular design of language models and prediction heads. Switch between heads or combine them for multitask learning. Full Compatibility with HuggingFace Transformers' models and model hub. Smooth upgrading to newer language models. Integration of custom datasets via Processor class. Powerful experiment tracking & execution. -
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DrQA
Reading Wikipedia to Answer Open-Domain Questions
...The retriever relies on classic IR features (like TF-IDF and n-gram statistics) to remain lightweight and scalable to millions of documents. The reader is a neural model trained on supervised QA data to estimate start and end positions within a paragraph, and it can be adapted to new domains through fine-tuning or distant supervision. The repository includes scripts to build the Wikipedia index, train the reader, and evaluate end-to-end performance. DrQA popularized a practical recipe for combining IR and neural reading, and it remains a strong baseline for open-domain QA research and production prototypes.
