Search Results for "interface"
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Showing 15 open source projects for "interface"
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Video-subtitle-extractor
A GUI tool for extracting hard-coded subtitle (hardsub) from videos
Video hard subtitle extraction, generate srt file. There is no need to apply for a third-party API, and text recognition can be implemented locally. A deep learning-based video subtitle extraction framework, including subtitle region detection and subtitle content extraction. A GUI tool for extracting hard-coded subtitles (hardsub) from videos and generating srt files. Use local OCR recognition, no need to set up and call any API, and do not need to access online OCR services such as Baidu... -
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DeepCTR
Package of deep-learning based CTR models
DeepCTR is a Easy-to-use,Modular and Extendible package of deep-learning based CTR models along with lots of core components layers which can be used to easily build custom models. You can use any complex model with model.fit(), and model.predict(). Provide tf.keras.Model like interface for quick experiment. Provide tensorflow estimator interface for large scale data and distributed training. It is compatible with both tf 1.x and tf 2.x. With the great success of deep learning,DNN-based techniques have been widely used in CTR prediction task. The data in CTR estimation task usually includes high sparse,high cardinality categorical features and some dense numerical features. ... -
3
AutoKeras
AutoML library for deep learning
...If you followed previous steps to use virtualenv to install tensorflow, you can just activate the virtualenv. Currently, AutoKeras is only compatible with Python >= 3.7 and TensorFlow >= 2.8.0. AutoKeras supports several tasks with extremely simple interface. AutoKeras would search for the best detailed configuration for you. Moreover, you can override the base classes to create your own block. -
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Albumentations
Fast image augmentation library and an easy-to-use wrapper
...Albumentations efficiently implements a rich variety of image transform operations that are optimized for performance, and does so while providing a concise, yet powerful image augmentation interface for different computer vision tasks, including object classification, segmentation, and detection. Albumentations supports different computer vision tasks such as classification, semantic segmentation, instance segmentation, object detection, and pose estimation. Albumentations works well with data from different domains: photos, medical images, satellite imagery, manufacturing and industrial applications, Generative Adversarial Networks. ... -
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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VibeTensor
Our first fully AI generated deep learning system
...What makes VibeTensor remarkable is that every major component, from core libraries and dispatch systems to CUDA runtime support, caching allocators, and language bindings, was created and validated by coding agents using automated builds and tests rather than manual line-by-line human coding. The system includes both a Python frontend via a torch-like API and an experimental Node.js/TypeScript interface. -
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OpenVINO Training Extensions
Trainable models and NN optimization tools
OpenVINO™ Training Extensions provide a convenient environment to train Deep Learning models and convert them using the OpenVINO™ toolkit for optimized inference. When ote_cli is installed in the virtual environment, you can use the ote command line interface to perform various actions for templates related to the chosen task type, such as running, training, evaluating, exporting, etc. ote train trains a model (a particular model template) on a dataset and saves results in two files. ote optimize optimizes a pre-trained model using NNCF or POT depending on the model format. NNCF optimization used for trained snapshots in a framework-specific format. ... -
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Thinc
A refreshing functional take on deep learning
Thinc is a lightweight deep learning library that offers an elegant, type-checked, functional-programming API for composing models, with support for layers defined in other frameworks such as PyTorch, TensorFlow and MXNet. You can use Thinc as an interface layer, a standalone toolkit or a flexible way to develop new models. Previous versions of Thinc have been running quietly in production in thousands of companies, via both spaCy and Prodigy. We wrote the new version to let users compose, configure and deploy custom models built with their favorite framework. Switch between PyTorch, TensorFlow and MXNet models without changing your application, or even create mutant hybrids using zero-copy array interchange. ... -
8
Jittor
Jittor is a high-performance deep learning framework
...The front-end language is Python. Module Design and Dynamic Graph Execution is used in the front-end, which is the most popular design for deep learning framework interface. The back-end is implemented by high-performance languages, such as CUDA, C++. Jittor'op is similar to NumPy. Let's try some operations. We create Var a and b via operation jt.float32, and add them. Printing those variables shows they have the same shape and dtype. -
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AI-Agent-Host
The AI Agent Host is a module-based development environment.
...The AI Agent Host is a module-based environment designed to facilitate rapid experimentation and testing. It includes a docker-compose configuration with QuestDB, Grafana, Code-Server and Nginx. The AI Agent Host provides a seamless interface for managing and querying data, visualizing results, and coding in real-time. The AI Agent Host is built specifically for LangChain, a framework dedicated to developing applications powered by language models. LangChain recognizes that the most powerful and distinctive applications go beyond simply utilizing a language model and strive to be data-aware and agentic. ... -
Your monitoring isn't a stack. It's a pile. Fix that.Replace Datadog, New Relic, and Sentry without adding three more dashboards.
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10
MoCo v3
PyTorch implementation of MoCo v3
MoCo v3 is a PyTorch reimplementation of Momentum Contrast v3 (MoCo v3), Facebook Research’s state-of-the-art self-supervised learning framework for visual representation learning using ResNet and Vision Transformer (ViT) backbones. Originally developed in TensorFlow for TPUs, this version faithfully reproduces the paper’s results on GPUs while offering an accessible and scalable PyTorch interface. MoCo v3 introduces improvements for training self-supervised ViTs by combining contrastive learning with transformer-based architectures, achieving strong linear and end-to-end fine-tuning performance on ImageNet benchmarks. The repository supports multi-node distributed training, automatic mixed precision, and linear scaling of learning rates for large-batch regimes. ... -
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DeepImageTranslator
DeepImageTranslator: a deep-learning utility for image translation
Created by: Run Zhou Ye, En Zhou Ye, and En Hui Ye DeepImageTranslator: a free, user-friendly tool for image translation using deep-learning and its applications in CT image analysis Citation: Please cite this software as: Ye RZ, Noll C, Richard G, Lepage M, Turcotte ÉE, Carpentier AC. DeepImageTranslator: a free, user-friendly graphical interface for image translation using deep-learning and its applications in 3D CT image analysis. SLAS technology. 2022 Feb 1;27(1):76-84. https://doi.org/10.1016/j.slast.2021.10.014 -
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ChainerRL
ChainerRL is a deep reinforcement learning library
...ChainerRL has a set of accompanying visualization tools in order to aid developers' ability to understand and debug their RL agents. With this visualization tool, the behavior of ChainerRL agents can be easily inspected from a browser UI. Environments that support the subset of OpenAI Gym's interface (reset and step methods) can be used. -
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Torchreid
Deep learning person re-identification in PyTorch
Torchreid is a library for deep-learning person re-identification, written in PyTorch and developed for our ICCV’19 project, Omni-Scale Feature Learning for Person Re-Identification. In "deep-person-reid/scripts/", we provide a unified interface to train and test a model. See "scripts/main.py" and "scripts/default_config.py" for more details. The folder "configs/" contains some predefined configs which you can use as a starting point. The code will automatically (download and) load the ImageNet pretrained weights. After the training is done, the model will be saved as "log/osnet_x1_0_market1501_softmax_cosinelr/model.pth.tar-250". ... -
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CRP - Chemical Reaction Prediction
Predicting Organic Reactions using Neural Networks.
The intend is to solve the forward-reaction prediction problem, where the reactants are known and the interest is in generating the reaction products using Deep learning. This Graphical User Interface takes simplified molecular-input line-entry system (SMILES) as an input and generates the product SMILE & molecule. Beam search is used in Version 2, to generate top 5 predictions. Maximum input length for the model is 15 (excluding spaces). -
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Monk Computer Vision
A low code unified framework for computer vision and deep learning
...Monk object detection is our take on assembling state of the art object detection, image segmentation, pose estimation algorithms at one place, making them low code and easily configurable on any machine. - Monk GUI - https://github.com/Tessellate-Imaging/Monk_Gui. An interface over these low code tools for non coders.
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