Search Results for "model-builder"
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Showing 35 open source projects for "model-builder"
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MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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Enterprise-grade ITSM, for every businessFreshservice is an intuitive, AI-powered platform that helps IT, operations, and business teams deliver exceptional service without the usual complexity. Automate repetitive tasks, resolve issues faster, and provide seamless support across the organization. From managing incidents and assets to driving smarter decisions, Freshservice makes it easy to stay efficient and scale with confidence.
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Rsmpeg
A Rust crate that exposes FFmpeg's power as much as possible
...It allows developers to interact directly with FFmpeg libraries for tasks such as encoding, decoding, and stream manipulation. The library is designed to balance performance with safety by leveraging Rust’s ownership model and memory management features. It supports handling of containers, codecs, and frames, enabling fine-grained control over media workflows. rsmpeg is suitable for building high-performance multimedia applications that require precise control over processing pipelines. It also integrates well with other Rust libraries, enabling complex system development. ... -
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MeshLab
The open source mesh processing system
MeshLab is an open-source, portable, and extensible system for the processing and editing of unstructured large 3D triangular meshes. It is aimed to help the processing of the typical not-so-small unstructured models arising in 3D scanning, providing a set of tools for editing, cleaning, healing, inspecting, rendering and converting this kind of meshes. MeshLab is mostly based on the open source C++ mesh processing library VCGlib developed at the Visual Computing Lab of ISTI - CNR. VCG can... -
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ZLMediaKit
WebRTC/RTSP/RTMP/HTTP/HLS/HTTP-FLV/WebSocket
...The project is designed for protocol conversion, live streaming, low-latency playback, recording, forwarding, and large-scale client access. Its asynchronous, multithreaded network model makes it suitable for commercial streaming deployments with many concurrent connections. ZLMediaKit can be deployed directly as a complete media server or embedded through its standard C API as an SDK for other languages and systems. It also provides REST APIs and webhooks so businesses can integrate authentication, event handling, and custom streaming logic. -
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Segmentation Models
Segmentation models with pretrained backbones. PyTorch
...Preparing your data the same way as during weights pre-training may give you better results (higher metric score and faster convergence). It is not necessary in case you train the whole model, not only the decoder. Pytorch Image Models (a.k.a. timm) has a lot of pretrained models and interface which allows using these models as encoders in smp, however, not all models are supported. Input channels parameter allows you to create models, which process tensors with an arbitrary number of channels. -
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Real-ESRGAN GUI
Cross-platform GUI for image upscaler Real-ESRGAN
...According to actual measurements, arm64the single-architecture performance is better than universal2the dual- architecture Mac on the Apple chip, so Apple chip users are advised to pack arm64single-architecture applications by themselves. Real-ESRGAN can only enlarge the input image with a fixed 2-4x magnification (related to the selected model). This functionality is achieved by downsampling using a conventional scaling algorithm after multiple calls to Real-ESRGAN. Split each frame of the GIF and record the duration, zoom in one by one and then merge. Drag an image file or directory to any position in the window, and its path can be automatically set as the input. -
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Transcoder
Hardware-accelerated video transcoding using Android MediaCodec APIs
Transcoder by DeepMedia is an AI-powered video-to-video speech translation engine that enables fully automated multilingual dubbing. Unlike traditional speech translation systems that rely on multi-stage pipelines, Transcoder directly translates one speaker’s video into another language while preserving facial expressions, lip-sync, and vocal identity. Designed for real-time use and production-grade pipelines, Transcoder combines advanced deep learning models with GPU acceleration to deliver... -
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Stremio Core
Types, addon system, UI models, core logic
Stremio Core is the Rust engine that powers Stremio’s apps by centralizing all reusable logic behind discovery, catalogs, metadata, streams, add-ons, and user/library state. It exposes a clean set of modules—types, addon_transport, and state_types—so apps can talk to add-ons, model UI state, and react to events without duplicating code. The architecture is inspired by Elm: immutable state, message-driven updates, and explicit side-effects (“effects”) keep behavior predictable and testable. An environment abstraction lets hosts provide implementations for things like storage, networking, and timers, making the core portable to desktop, mobile, and web. ... -
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python-socketio
Python Socket.IO server and client
python-socketio is a robust Python library that implements the Socket.IO protocol, enabling real-time, bidirectional communication between web clients and servers. It works with multiple asynchronous frameworks such as asyncio, eventlet, and gevent, so developers can choose the concurrency model that best fits their application needs while still using a consistent API. The library provides both server and client implementations, allowing Python applications to serve and communicate with browser clients or other Socket.IO clients in real time. It supports key Socket.IO features like event handling, message broadcasting, binary data transmission, rooms, and namespaces, giving developers the building blocks to create chat applications, live dashboards, multiplayer games, and collaborative tools. -
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Live API Web Console
A react-based starter app for using the Live API over websockets
...Configuration lives in a simple .env file and the project boots with standard web tooling, letting you experiment quickly with models, system prompts, and tool declarations. It ships with demo branches that show grounded search, function calling, and visualization—one example has the model calling a function that renders Vega/Altair graphs directly in the UI. Under the hood there’s an event-emitting WebSocket client, an audio in/out processing layer, and a minimal scaffolded view so you can focus on your app logic rather than wiring. -
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A multimedia authoring and processing framework and a video playout server for television broadcasting.
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Ultimate Media Downloader
An Open source media downloader for downloading videos and audios
...Whether you're downloading a single YouTube video, extracting audio from Spotify playlists, archiving TikTok content, or batch-processing entire music libraries, UMD handles it all with elegance and efficiency. IT CONSISTS OF : 1. Unified Interface: One command, 1000+ platforms. No tool shopping, no mental model switching. 2. Production-Ready, Zero Friction Installation: Most users go from hearing about the tool to downloading content in under 5 minutes. 3. Active Maintenance: Codeberg hosting (after GitHub suspension) demonstrates commitment to long-term availability -
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auto-subtitle
Automatically generate and overlay subtitles for any video
auto-subtitle is a Python-based command-line tool that automatically generates and overlays subtitles on video files using AI-driven speech recognition. It combines FFmpeg with OpenAI’s Whisper model to transcribe spoken audio into text and synchronize it with video playback. The tool processes video input, extracts audio, and produces subtitle files that can be either exported separately or burned directly into the final video output. It supports multiple transcription models with varying accuracy and performance, allowing users to balance speed and quality depending on their needs. ... -
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Golang HLS Streamer
A server that exposes a directory for video streaming
...It provides tools for creating and managing HLS streams, including segmenting video into smaller chunks and generating M3U8 playlists. The project is intended for developers building streaming servers or media delivery systems. It focuses on performance and simplicity, leveraging Go’s concurrency model to handle streaming tasks efficiently. gohls can be integrated into backend services to enable adaptive streaming workflows. It supports real-time processing and can work alongside tools like FFmpeg for encoding. Overall, it provides a lightweight solution for implementing HLS streaming pipelines. -
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AI Models
A repository of trained models
All models (at least currently) are supported by chaiNNer, an upscaling GUI that allows for both very simple and very complex tasks to be completed in a nice manner where you "chain" nodes together. Highly recommended for images. If you're looking to upscale videos using the models then use enhancr simply due to the fact that it supports TensorRT, which will allow you to upscale videos at incredible speeds! The GUI is one of the best looking applications out there and is personally my go to... -
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Video Pre-Training
Learning to Act by Watching Unlabeled Online Videos
The Video PreTraining (VPT) repository provides code and model artifacts for a project where agents learn to act by watching human gameplay videos—specifically, gameplay of Minecraft—using behavioral cloning. The idea is to learn general priors of control from large-scale, unlabeled video data, and then optionally fine-tune those priors for more goal-directed behavior via environment interaction. -
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G2SConverter
Convert models from GoldSource engine to Source engine with AI
...An example of a processed texture is shown in the following image (parameters used: scaling-factor = 4 and deblur iterations = 4) besides upscaling and debluring the utility also generates normal maps for each texture. This is implemented using the DeepBump by HugoTiny model. Examples of normal maps are shown in the following images. -
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VSGAN
VapourSynth Single Image Super-Resolution Generative Adversarial
Single Image Super-Resolution Generative Adversarial Network (GAN) which uses the VapourSynth processing framework to handle input and output image data. Transform, Filter, or Enhance your input video, or the VSGAN result with VapourSynth, a Script-based NLE. You can chain models or re-run the model twice-over (or more). Have low VRAM? Don’t worry! The Network will be applied in quadrants of the image to reduce up-front VRAM usage. You can use any RGB video input, including float32 (e.g., RGBS) inputs. Using VapourSynth you can pass a Video directly to VSGAN, without any frame extraction needed. Any edit you make in the VapourSynth script with or without VSGAN can be re-used for any other video. ... -
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Easy Upscale
A simple image upscaler application using EDSR, ESPCN, FSRCNN, etc.
...The main theme is queues, we implement circular queues for pooling/storing a list of images to be upscaled. Gui creation is made manually using the tkinter library. For the upscale process itself, it uses the OpenCV library with a model obtained from open source. Checked using vermin. Minimum required versions: 3.6 Incompatible versions: 2. -
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Robust Video Matting (RVM)
Robust Video Matting in PyTorch, TensorFlow, TensorFlow.js, ONNX
We introduce a robust, real-time, high-resolution human video matting method that achieves new state-of-the-art performance. Our method is much lighter than previous approaches and can process 4K at 76 FPS and HD at 104 FPS on an Nvidia GTX 1080Ti GPU. Unlike most existing methods that perform video matting frame-by-frame as independent images, our method uses a recurrent architecture to exploit temporal information in videos and achieves significant improvements in temporal coherence and... -
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TimeSformer
The official pytorch implementation of our paper
TimeSformer is a vision transformer architecture for video that extends the standard attention mechanism into spatiotemporal attention. The model alternates attention along spatial and temporal dimensions (or designs variants like divided attention) so that it can capture both appearance and motion cues in video. Because the attention is global across frames, TimeSformer can reason about dependencies across long time spans, not just local neighborhoods. The official implementation in PyTorch provides configurations, pretrained models, and training scripts that make it straightforward to evaluate or fine-tune on video datasets. ... -
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Render32
Command-line video compositing and audio mixing tools
Render is a program for creating composite BMP image sequences. These images are composited as specified in a text configuration file. Mixer is a program for mixing film soundtracks. It accepts input files in WAV format and outputs a mixed soundtrack in WAV format. Each input channel can contain one or more audio files that are edited and mixed using a cue sheet. The maximum number of channels is a compile-time parameter. -
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Super-résolution via CNN
Super resolution using a CNN, based on the work of the DGtal team
...First of all, an Nvidia graphics card (neither AMD nor Intel integrated) is highly recommended to parallelize the CNN. You will then need to install CUDA. No CUDA = dozens of times slower. This program will generate "model_epoch_ .pth" files corresponding to the model at epoch n, in a folder saved_model_u t_bs bs_tbs tbs_lr lr, where corresponds to the scale factor, bsthe size of the training batch, tbsthe size of the test batch and lrto the learning rate. Low res images should be located in a "dataset/input" folder, and high res targets in a "dataset/target" folder, where each different quality image has the same name in both folders. -
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YouTube-8M
Starter code for working with the YouTube-8M dataset
youtube-8m is Google’s open source starter code and reference implementation for training and evaluating machine learning models on the YouTube-8M dataset, one of the largest video understanding datasets publicly released. The repository provides a complete pipeline for video-level and frame-level modeling using TensorFlow, including data reading, model training, evaluation, and inference. It was developed to support the YouTube-8M Video Understanding Challenge (hosted on Kaggle and featured at ICCV 2019), enabling researchers and practitioners to benchmark video classification models on large-scale datasets with over millions of labeled videos. The code demonstrates how to process frame-level features, train logistic and deep learning models, evaluate them using metrics like global Average Precision (gAP) and mean Average Precision (mAP), and export trained models for MediaPipe inference. -
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PyTorch Natural Language Processing
Basic Utilities for PyTorch Natural Language Processing (NLP)
...PyTorch-NLP comes with pre-trained embeddings, samplers, dataset loaders, metrics, neural network modules and text encoders. It’s open-source software, released under the BSD3 license. With your batch in hand, you can use PyTorch to develop and train your model using gradient descent. For example, check out this example code for training on the Stanford Natural Language Inference (SNLI) Corpus. Now you've setup your pipeline, you may want to ensure that some functions run deterministically. Wrap any code that's random, with fork_rng and you'll be good to go. Now that you've computed your vocabulary, you may want to make use of pre-trained word vectors to set your embeddings. -
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This is a MATLAB model of an end-to-end chain compliant to the DVB-T2 standard (ETSI EN 302 755 available from www.etsi.org). It was originally developed within the DVB consortium (www.dvb.org) by AICIA, BBC, Pace, Panasonic and SIDSA.
