Search Results for "idl image processing" - Page 3
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Showing 170 open source projects for "idl image processing"
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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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Spring AI Alibaba Examples
Spring AI Alibaba examples for building and testing AI apps
...It is designed to help developers understand core concepts, explore practical implementations, and follow best practices when building AI-powered systems using the Spring ecosystem. Each module focuses on a specific use case such as chat, image processing, audio handling, graph workflows, and retrieval-augmented generation. The examples highlight how to integrate AI models, manage prompts, handle memory, and build multi-model or multi-agent workflows. Developers can explore individual project folders for detailed instructions and implementation guidance. Spring AI Alibaba Examples also supports experimentation through playground modules and encourages contributions to expand real-world AI use cases and improve development practices. -
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GLM-4.5V
GLM-4.6V/4.5V/4.1V-Thinking, towards versatile multimodal reasoning
GLM-4.5V is the preceding iteration in the GLM-V series that laid much of the groundwork for general multimodal reasoning and vision-language understanding. It embodies the design philosophy of mixing visual and textual modalities into a unified model capable of general-purpose reasoning, content understanding, and generation, while already supporting a wide variety of tasks: from image captioning and visual question answering to content recognition, GUI-based agents, video understanding,... -
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BoofCV
BoofCV is an open source Java library for real-time computer vision.
...Written from scratch for ease of use and high performance, it provides both basic and advanced features needed for creating a computer vision system. Functionality include optimized low level image processing routines (e.g. convolution, interpolation, gradient) to high level functionality such as image stabilization. Released under an Apache 2.0 license for both academic and commercial use. -
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DataChain
AI-data warehouse to enrich, transform and analyze unstructured data
...Datachain can persist features of Python objects returned by AI models, and enables vectorized analytical operations over them. The typical use cases are data curation, LLM analytics and validation, image segmentation, pose detection, and GenAI alignment. Datachain is especially helpful if batch operations can be optimized – for instance, when synchronous API calls can be parallelized or where an LLM API offers batch processing. -
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ComfyUI-3D-Pack
An extensive node suite that enables ComfyUI to process 3D inputs
ComfyUI-3D-Pack is an extension package for the ComfyUI visual AI workflow environment that enables users to generate and manipulate 3D assets using advanced machine learning techniques. ComfyUI itself is a node-based interface for designing and executing generative AI pipelines, and this extension expands its capabilities by introducing nodes specifically designed for working with three-dimensional data. The package allows the platform to process inputs such as meshes and UV textures and... -
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AlphaTree
DNN && GAN && NLP && BIG DATA
...The repository organizes these architectures into a structured learning path that helps learners understand how deep learning models improved over time through changes in depth, architectural complexity, and training techniques. In addition to neural networks used for image classification, the project also references broader AI fields such as generative adversarial networks, natural language processing, and graph neural networks. -
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Jina
Build cross-modal and multimodal applications on the cloud
Jina is a framework that empowers anyone to build cross-modal and multi-modal applications on the cloud. It uplifts a PoC into a production-ready service. Jina handles the infrastructure complexity, making advanced solution engineering and cloud-native technologies accessible to every developer. Build applications that deliver fresh insights from multiple data types such as text, image, audio, video, 3D mesh, PDF with Jina AI’s DocArray. Polyglot gateway that supports gRPC, Websockets, HTTP,... -
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SMILI
Scientific Visualisation Made Easy
...The main sMILX application features for viewing n-D images, vector images, DICOMs, anonymizing, shape analysis and models/surfaces with easy drag and drop functions. It also features a number of standard processing algorithms for smoothing, thresholding, masking etc. images and models, both with graphical user interfaces and/or via the command-line. See our YouTube channel for tutorial videos via the homepage. The applications are all built out of a uniform user-interface framework that provides a very high level (Qt) interface to powerful image processing and scientific visualisation algorithms from the Insight Toolkit (ITK) and Visualisation Toolkit (VTK). ... -
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Dolphin
Document Image Parsing via Heterogeneous Anchor Prompting”
Dolphin — maintained by ByteDance — is a project aimed at providing a high-performance, robust, and extensible media or multimedia framework / player infrastructure (or possibly a streaming media solution), intended to meet modern demands for efficiency, flexibility, and integration in media-heavy applications. It seeks to combine performant media playback or handling (audio/video decoding, streaming, buffering) with a modular, developer-friendly API that allows easy embedding into larger... -
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RuoYi AI
Enterprise AI platform for building, deploying, and managing apps
RuoYi AI is a full-stack enterprise-oriented AI development platform designed to help developers rapidly build, deploy, and manage intelligent applications using modern large language models and AI ecosystems. It provides a unified framework for integrating multiple AI models from different providers, allowing teams to switch or combine models through a consistent interface without vendor lock-in. RuoYi AI includes built-in support for retrieval-augmented generation, enabling organizations... -
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OAGI Python SDK
Python SDK for the Computer Use model Lux, developed by OpenAGI
OAGI Python SDK is a Python client library for the Lux computer-use model that turns Lux into a programmable automation layer for operating human-facing software via vision and actions. It exposes the OAGI API in an ergonomic way, letting you trigger Lux in three main modes: Tasker for precise scripted sequences, Actor for fast one-shot tasks, and Thinker for open-ended, multi-step objectives. The SDK is designed around “computer use” as a paradigm, where the AI actually navigates... -
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MiniMax-01
Large-language-model & vision-language-model based on Linear Attention
MiniMax-01 is the official repository for two flagship models: MiniMax-Text-01, a long-context language model, and MiniMax-VL-01, a vision-language model built on top of it. MiniMax-Text-01 uses a hybrid attention architecture that blends Lightning Attention, standard softmax attention, and Mixture-of-Experts (MoE) routing to achieve both high throughput and long-context reasoning. It has 456 billion total parameters with 45.9 billion activated per token and is trained with advanced parallel... -
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libsombrero
Astronomical object/structure detection from 1D and 2D data sets.
Sombrero is a fast wavelet image processing and object detection C library for astronomical images. Sombrero is named after the "Mexican Hat" shape of the wavelet masks used in image convolution and is released under the GNU LGPL library. -
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PromptSniffer
View Extract & Remove AI generation metadata with right click
A powerful tool for reading, extracting, and removing AI generation metadata from image files. Specifically designed to handle metadata from AI image generation tools like ComfyUI, Stable Diffusion, SwarmUI, InvokeAI, and more. Core Functionality Read EXIF/Metadata: Extract and display comprehensive metadata from images AI Metadata Detection: Automatically identify and highlight AI generation metadata Metadata Removal: Strip AI generation metadata while preserving image quality Batch Processing: Handle multiple files with wildcard patterns Cross-Platform: Works on Windows, macOS, and Linux AI Tool Support ComfyUI: Detects and extracts workflow JSON data Stable Diffusion: Identifies prompts, parameters, and generation settings SwarmUI/StableSwarmUI: Handles JSON-formatted metadata Midjourney, DALL-E, NovelAI: Recognizes generation signatures Automatic1111, InvokeAI: Extracts generation parameters -
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cleanvideo-cli
CLI tool for removing watermarks from AI-generated videos using frame-
cleanvideo-cli is a command-line tool designed to remove visible watermarks from AI-generated videos. It works by analyzing video frames and reconstructing the underlying pixels in watermark regions, without cropping or blurring the original content. This project is intended for developers, researchers, and creators who need a lightweight utility for cleaning preview or draft videos before further processing. Note: This tool does not bypass platform restrictions and should be used... -
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AI File Sorter
Local AI file organization with categorization and rename suggestions
AI File Sorter is a cross-platform desktop application that uses AI (local LLMs run on your computer) to organize files and suggest meaningful file names based on real content, not just filenames or extensions. The app can analyze images locally and propose descriptive rename suggestions (for example, IMG_2048.jpg → clouds_over_lake.jpg). It can also analyze document text to improve categorization and renaming. Supported formats include PDF, DOCX, XLSX, PPTX, ODT, ODS, ODP, and common... -
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StableSwarmUI
Multi-user UI for managing and running Stable Diffusion workflows tool
StableSwarmUI is a web-based interface designed to manage and coordinate Stable Diffusion image generation workflows in a multi-user environment. It focuses on enabling multiple users to interact with shared resources, making it suitable for collaborative or server-based deployments. It provides a centralized system where users can submit, monitor, and manage generation tasks through a browser interface. It abstracts much of the complexity involved in running diffusion models by offering a structured environment for handling prompts, outputs, and processing queues. ... -
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HunyuanVideo-I2V
A Customizable Image-to-Video Model based on HunyuanVideo
HunyuanVideo-I2V is a customizable image-to-video generation framework developed by Tencent, extending the capabilities of HunyuanVideo. It allows for high-quality video creation from still images, using PyTorch and providing pre-trained model weights, inference code, and customizable training options. The system includes a LoRA training code for adding special effects and enhancing video realism, aiming to offer versatile and scalable solutions for generating videos from static image inputs. -
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fastdup
An unsupervised and free tool for image and video dataset analysis
fastdup is a powerful free tool designed to rapidly extract valuable insights from your image & video datasets. Assisting you to increase your dataset images & labels quality and reduce your data operations costs at an unparalleled scale. -
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DocWire SDK
Award-winning modern data processing SDK in C++20
DocWire SDK, a standout C++20AI driven data processing tool, has received award from SourceForge and strong backing from Microsoft. It handles nearly 100 file types, empowering efficient text extraction, web data extraction, and document analysis. For businesses, the shift to DocWire SDK signifies a leap forward. It promises comprehensive document format support and the ability to extract valuable insights from email boxes, databases, and websites using cutting-edge AI. DocWire SDK aims to... -
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MediaPipe Face Detection
Detect faces in an image
The MediaPipe Face Detection model is a high-performance, real-time face detection solution that uses machine learning to identify faces in images and video streams. It is optimized for mobile and embedded platforms, offering fast and accurate face detection while maintaining a small memory footprint. This model supports multiple face detections and is highly efficient, making it suitable for a variety of applications such as augmented reality, user authentication, and facial expression analysis. -
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Computer Vision in Action
A computer vision closed-loop learning platform
Computer Vision in Action is a practical, example-rich repository that demonstrates real-world applications of computer vision techniques and algorithms in Python, often using OpenCV, deep learning models, and related tooling. It serves as a hands-on companion for learners and engineers who want to understand not just the theory, but how computer vision is actually implemented for tasks like object detection, image classification, feature tracking, optical flow, and image segmentation. The... -
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MegEngine
Easy-to-use deep learning framework with 3 key features
MegEngine is a fast, scalable and easy-to-use deep learning framework with 3 key features. You can represent quantization/dynamic shape/image pre-processing and even derivation in one model. After training, just put everything into your model and inference it on any platform at ease. Speed and precision problems won't bother you anymore due to the same core inside. In training, GPU memory usage could go down to one-third at the cost of only one additional line, which enables the DTR algorithm. ... -
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MATLAB Deep Learning Model Hub
Discover pretrained models for deep learning in MATLAB
Discover pre-trained models for deep learning in MATLAB. Pretrained image classification networks have already learned to extract powerful and informative features from natural images. Use them as a starting point to learn a new task using transfer learning. Inputs are RGB images, the output is the predicted label and score. -
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PyDenseCRF
Python wrapper to Philipp Krähenbühl's dense (fully connected) CRFs
PyDenseCRF is a Python library that provides a wrapper around the implementation of fully connected Conditional Random Fields (CRFs) developed by Philipp Krähenbühl and Vladlen Koltun. The project allows developers and researchers to integrate Dense CRF inference into Python-based machine learning pipelines, particularly for computer vision tasks such as image segmentation and labeling. Conditional Random Fields are probabilistic graphical models used to model contextual relationships...
