Search Results for "model train design" - Page 11
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Showing 286 open source projects for "model train design"
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DCVGAN
DCVGAN: Depth Conditional Video Generation, ICIP 2019.
This paper proposes a new GAN architecture for video generation with depth videos and color videos. The proposed model explicitly uses the information of depth in a video sequence as additional information for a GAN-based video generation scheme to make the model understands scene dynamics more accurately. The model uses pairs of color video and depth video for training and generates a video using the two steps. Generate the depth video to model the scene dynamics based on the geometrical... -
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PyTorch-BigGraph
Generate embeddings from large-scale graph-structured data
...The toolkit includes evaluation metrics and export tools so learned embeddings can be used in downstream nearest-neighbor search, recommendation, or analytics. In practice, PBG’s design lets practitioners train high-quality graph embeddings. -
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MMF
A modular framework for vision & language multimodal research
...Through our modular design, you can use specific components from MMF that you care about. Our configuration system allows MMF to easily adapt to your needs. -
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maskrcnn-benchmark
Fast, modular reference implementation of Instance Segmentation
Mask R-CNN Benchmark is a PyTorch-based framework that provides high-performance implementations of object detection, instance segmentation, and keypoint detection models. Originally built to benchmark Mask R-CNN and related models, it offers a clean, modular design to train and evaluate detection systems efficiently on standard datasets like COCO. The framework integrates critical components—region proposal networks (RPNs), RoIAlign layers, mask heads, and backbone architectures such as ResNet and FPN—optimized for both accuracy and speed. It supports multi-GPU distributed training, mixed precision, and custom data loaders for new datasets. ... -
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CakeChat
CakeChat: Emotional Generative Dialog System
CakeChat is a backend for chatbots that are able to express emotions via conversations. The code is flexible and allows to condition model's responses by an arbitrary categorical variable. For example, you can train your own persona-based neural conversational model or create an emotional chatting machine. Hierarchical Recurrent Encoder-Decoder (HRED) architecture for handling deep dialog context. Multilayer RNN with GRU cells. The first layer of the utterance-level encoder is always bidirectional. By default, CuDNNGRU implementation is used for ~25% acceleration during inference. ... -
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UnsupervisedMT
Phrase-Based & Neural Unsupervised Machine Translation
...Training relies on denoising auto-encoding and back-translation, with on-the-fly, multithreaded generation of synthetic parallel data to continually refresh supervision signals. The project also provides scripts to fetch and preprocess monolingual data, learn BPE codes, and train cross-lingual embeddings that bootstrap unsupervised alignment between languages. Beyond the core EMNLP 2018 setup, the codebase exposes additional, optional capabilities such as multi-language training, language model pretraining with shared parameters, and adversarial training. -
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JSON2YOLO
Convert JSON annotations into YOLO format.
Explore our state-of-the-art AI architecture to train and deploy your highly accurate AI models like a pro. This directory contains label import/export software developed by Ultralytics LLC, and is freely available for redistribution under the GPL-3.0 license. Ultralytics is a U.S.-based particle physics and AI startup with over 6 years of expertise supporting government, academic, and business clients. We offer a wide range of vision AI services, spanning from simple expert advice up to the... -
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automl-gs
Provide an input CSV and a target field to predict, generate a model
Give an input CSV file and a target field you want to predict to automl-gs, and get a trained high-performing machine learning or deep learning model plus native Python code pipelines allowing you to integrate that model into any prediction workflow. No black box: you can see exactly how the data is processed, and how the model is constructed, and you can make tweaks as necessary. automl-gs is an AutoML tool which, unlike Microsoft's NNI, Uber's Ludwig, and TPOT, offers a zero code/model... -
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Django REST Pandas
Serves up Pandas dataframes via the Django REST Framework
Django REST Pandas (DRP) provides a simple way to generate and serve pandas DataFrames via the Django REST Framework. The resulting API can serve up CSV (and a number of other formats for consumption by a client-side visualization tool like d3.js. The design philosophy of DRP enforces a strict separation between data and presentation. This keeps the implementation simple, but also has the nice side effect of making it trivial to provide the source data for your visualizations. This... -
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Teach Me Quantum
Practical Course on Quantum Information Science and Quantum Computing
A university-level course on Quantum Computing and Quantum Information Science that incorporates IBM Q Experience and Qiskit. This course is adequate for general audiences without prior knowledge on Quantum Mechanics and Quantum Computing (see prior knowledge), has an estimated average duration of 10 weeks at 3h/week (see duration), and is meant to be the entrypoint into the Quantum World. -
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Exposure
Learning infinite-resolution image processing with GAN and RL
Learning infinite-resolution image processing with GAN and RL from unpaired image datasets, using a differentiable photo editing model. ACM Transactions on Graphics (presented at SIGGRAPH 2018) Exposure is originally designed for RAW photos, which assumes 12+ bit color depth and linear "RGB" color space (or whatever we get after demosaicing). jpg and png images typically have only 8-bit color depth (except 16-bit pngs) and the lack of information (dynamic range/activation resolution) may... -
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DIGITS
Deep Learning GPU training system
The NVIDIA Deep Learning GPU Training System (DIGITS) puts the power of deep learning into the hands of engineers and data scientists. DIGITS can be used to rapidly train the highly accurate deep neural network (DNNs) for image classification, segmentation and object detection tasks. DIGITS simplifies common deep learning tasks such as managing data, designing and training neural networks on multi-GPU systems, monitoring performance in real-time with advanced visualizations, and selecting the best performing model from the results browser for deployment. ... -
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DC-TTS
TensorFlow Implementation of DC-TTS: yet another text-to-speech model
...It follows the “Efficiently Trainable Text-to-Speech System Based on Deep Convolutional Networks with Guided Attention” paper, but the author adapts and extends the design to make it practical for real experiments. The model is split into two networks: Text2Mel, which maps text to mel-spectrograms, and SSRN (spectrogram super-resolution network), which converts low-resolution mel-spectrograms into high-resolution magnitude spectrograms suitable for waveform synthesis. Training scripts, data loaders, and hyperparameter configurations are provided to reproduce results on several datasets, including LJ Speech for English, a Korean single-speaker dataset, and audiobook data from Nick Offerman and Kate Winslet. -
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Rekall
Rekall Memory Forensic Framework
...Used well, it compresses what would be hours of manual sleuthing into scripted passes over a consistent object model. -
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Learning to Learn in TensorFlow
Learning to Learn in TensorFlow
...The repository provides code for training and evaluating learned optimizers that can generalize across different problem types, such as quadratic functions and image classification tasks (MNIST and CIFAR-10). Using TensorFlow, it defines a meta-optimizer model that learns by observing and adapting to the optimization trajectories of other models. The project allows users to compare performance between traditional optimizers and the learned optimizer (L2L) on various benchmarks, demonstrating how optimization strategies can be learned through experience. The design supports both single-variable and high-dimensional problems, and includes tools for evaluating how well a learned optimizer performs on unseen tasks. -
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SnowyOwl
RNA-Seq based gene prediction pipeline for fungal genomes
SnowyOwl is a gene prediction pipeline that uses RNA-Seq data to train and provide hints for the generation of Hidden Markov Model (HMM)-based gene predictions, and to evaluate the resulting models. The pipeline has been validated and streamlined by comparing its predictions to manually curated gene models in three fungal genomes, and its results show substantial increases in sensitivity and selectivity over previous gene predictions. -
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PrettyTensor
Pretty Tensor: Fluent Networks in TensorFlow
...Pretty Tensor preserves full compatibility with TensorFlow’s core functionality while providing syntactic sugar for defining complex architectures such as convolutional and recurrent networks. The library’s design emphasizes flexibility and modularity, supporting advanced features like default scopes, parameter templates, and variable reuse. It also allows easy integration with custom operations and third-party libraries, making it ideal for both research experimentation and production-grade modeling. By combining TensorFlow’s power with an intuitive builder-style API, Pretty Tensor accelerates model development without sacrificing transparency or control. -
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Question Answering Corpus
Question answering dataset in "Teaching Machines to Read & Comprehend"
RC-Data is a dataset generation framework created by Google DeepMind to produce large-scale reading comprehension question-answer pairs from CNN and Daily Mail news articles. The dataset, introduced in the 2015 paper “Teaching Machines to Read and Comprehend” (Hermann et al., NIPS 2015), was among the first large corpora designed to train and evaluate machine reading and comprehension models. The repository provides scripts for downloading archived CNN and Daily Mail articles from the... -
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Django Extra Views
Django's class-based generic views are awesome
Django's class-based generic views are awesome, let's have more of them. Django-extra-views is a Django package that introduces additional class-based views in order to simplify common design patterns such as those found in the Django admin interface. Supported Python and Django versions: Python 3.5+, Django 2.1–3.2, see tox.ini for an up-to-date list. Add support for pagination in ModelFormSetView and its derivatives, the goal being to be able to mimic the change_list view in Django's... -
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SeqSelector
Tools to select sequences for capture enrichment of next-gen libraries
The SeqSelector toolset is a suite of user-friendly, platform independent python scripts to facilitate selection of sequences for targeted enrichment of next-generation libraries through hybridization-based sequence capture. The scripts require no knowledge of programming, and can be applied to genome sequences of model or non-model species. We suggest a workflow in which genes of interest are first identified from previous studies and publicly available datasets of functional gene annotation. Once a list of candidate genes has been identified, their sequences are selected from the reference genome. These sequences are used as a query during a BLAST search of the unannotated genome of a non-model species, and then the corresponding sequences are returned, which can be used to design baits for hybridization-based sequence capture. -
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HMMLab
A Hidden Markov Model editor with support to HTK
HMMLab is a Hidden Markov Model editor oriented on HMMs for speach recognition. It can create, edit, train and visualize HMMs. HMMLab supports loading/saving HMMs from/to HTK files. -
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candleeggs
provides a natural language interface to Python
The problem with even high level programming languages is that they don't allow users who have no interest in programming safe access to the services their os/device combination provides. The idea is to let any user talk to or type into their device and without any knowledge of the physicality or design currently necessary to use their device fully, be able to do just that. This thing will try to follow the GNU toolchain model, using functions that are unique objects and can be glued together by the overriding system, as well as my knowledge of teaching and linguistics to provide a simple, enjoyable interface to allow chimps in a zoo to get what they want out of their devices. ... -
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Xonet
Multipurpose network application.
Xonet is a network application that allow the user to interact with network devices through a graphical interface. You can design your own packets, send them and capture the answers. It also has the functionality to create your own network tools based on network packets. -
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Bamboo Engine
Game framework on top of Python, Panda3D and Twisted
Bamboo intends to be a complete end-to-end game framework for client/server applications using Twisted for data exchange, Panda3D for rendering and coded in Python. Support for PyPy/CPython may be considered at a later point. An Extreme/Agile Development model is in use to allow for emergent design (IE: changing requirements). Release is updated whenever a feature is added and all tests pass cleanly 100% -
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A graphical IDE and Model Driven Architecture for designing Overlay Networks and Peer-to-Peer systems.
