# Computer vision Computer vision computes properties of the three-dimensional world from digital images. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Arduino announces a live build of a privacy-focused smart doorbell on the Arduino UNO Q DevFeed: [Arduino announces a live build of a privacy-focused smart doorbell on the Arduino UNO Q]() Original publisher: [Read original article]() Author: Arduino Team Published: 2026-09-16T14:06:15Z Content type: article Language: en Sources: [Arduino Blog]() Topics: [Arduino](), [Computer vision](), [Security]() Tags: [arduino](), [computer-vision](), [model](), [notify](), [privacy](), [security](), [smart-doorbell](), [uno-q]() ### AI overview Arduino announces a live build showing how to create a smart doorbell using a computer vision model that runs locally on an Arduino UNO Q board. The event is scheduled for September 22 at 3 PM CET / 9 AM ET and will include questions for the Arduino team. ### Source excerpt Go on your favorite online shopping platform, and you'll find any number of smart doorbell options. Click to purchase, have it delivered, install it, download some app. But where's the fun in that? And also, don't you wonder how that thing works? That thing that watches you and your loved ones go in and out, [...] The post Build your own smart doorbell and protect your privacy - in one hour, with Massimo Banzi appeared first on Arduino Blog. ## MYWAI ports its VILMA visual imitation learning toolkit to Arduino UNO Q and VENTUNO Q DevFeed: [MYWAI ports its VILMA visual imitation learning toolkit to Arduino UNO Q and VENTUNO Q]() Original publisher: [Read original article]() Author: Arduino Team Published: 2026-09-15T14:26:17Z Content type: article Language: en Sources: [Arduino Blog]() Topics: [Robotics](), [Artificial Intelligence](), [Computer vision](), [Arduino](), [Qualcomm](), [UNO Q](), [VENTUNO Q]() Tags: [ai](), [arduino](), [computer-vision](), [edge-ai](), [industrial](), [machine-learning](), [qualcomm](), [robotics](), [robots](), [uno-q](), [ventuno-q](), [warehouse]() ### AI overview The article describes VILMA, an AI-powered toolkit from MYWAI that enables robots and humanoids to learn manipulation tasks from one-shot human demonstrations. It reports that the toolkit is being ported to Arduino UNO Q and VENTUNO Q boards powered by Qualcomm Dragonwing processors. ### Source excerpt Every day, hundreds of thousands of kits are prepared in warehouses before components ever reach an automotive production line. While robots have become commonplace in modern manufacturing, many upstream logistics activities still rely heavily on human operators performing repetitive pick-and-place and kitting tasks. What if robots could learn these operations the same way humans do: [...] The post MYWAI™ VILMA™ is designed to bring human-like learning to robots via one-shot demonstration appeared first on Arduino Blog. ## Axelera Europa Ships: 629 TOPS at 45W Per AIPU, in Validated Dell XE5 and Supermicro Servers DevFeed: [Axelera Europa Ships: 629 TOPS at 45W Per AIPU, in Validated Dell XE5 and Supermicro Servers]() Original publisher: [Read original article]() Author: Harold Fritts Published: 2026-09-15T13:00:00Z Content type: news Language: en Sources: [StorageReview.com]() Topics: [Artificial Intelligence](), [Inference](), [Computer vision](), [servers](), [dell](), [RISC-V]() Tags: [ai](), [computer-vision](), [dell](), [enterprise](), [inference](), [risc-v](), [servers]() ### AI overview Axelera AI is shipping Europa, a second-generation AI Processing Unit, in bare-chip and PCIe card configurations. The company says the 45W device delivers 629 TOPS and supports on-premises inference workloads including generative AI, vision-language models, and computer vision. The Edge 232p card is shipping in validated Dell XE5 and Supermicro 111AD systems. ### Source excerpt Axelera AI is shipping Europa, the second-generation AI Processing Unit (AIPU) it has been previewing since last year, and it's launching with validated servers from Dell and Supermicro attached. The Eindhoven company's pitch is inference on infrastructure the customer controls: agentic systems, vision-language models, generative AI, and computer vision running in a standard rackmount server The post Axelera Europa Ships: 629 TOPS at 45W Per AIPU, in Validated Dell XE5 and Supermicro Servers appeared first on StorageReview.com. ## Building smarter AMRs with the Arduino® VENTUNO™ Q board DevFeed: [Building smarter AMRs with the Arduino® VENTUNO™ Q board]() Original publisher: [Read original article]() Author: Arduino Team Published: 2026-09-11T11:10:16Z Content type: article Language: en Sources: [Arduino Blog]() Topics: [Arduino](), [Physical AI](), [Microcontroller](), [Linux](), [Computer vision](), [real-time](), [systems]() Tags: [amr](), [arduino](), [autonomous-mobile-robots](), [computer-vision](), [featured](), [linux](), [mcu](), [physical-ai](), [real-time](), [robotics](), [robots](), [ros](), [sensor](), [sensors](), [ventuno-q]() ### AI overview The article explains how Arduino's VENTUNO Q board could support autonomous mobile robots by combining a Linux-capable MPU with a real-time MCU. The MPU can run Linux, ROS 2, navigation, computer vision, and AI workloads, while the MCU handles motor control, encoder feedback, inertial measurements, local sensing, and motor-driver communication. ### Source excerpt Physical AI is based on the idea that intelligence shouldn't stop at perception: instead, it should bring to life systems able to sense their environment, reason about it, and act on it - all in one continuous loop. It's what makes the difference between a device that observes and one that acts. Autonomous mobile robots [...] The post Building smarter AMRs with the Arduino® VENTUNO™ Q board appeared first on Arduino Blog. ## Automatic image cropping in Appwrite with AutoGravity DevFeed: [Automatic image cropping in Appwrite with AutoGravity]() Original publisher: [Read original article]() Author: Torsten Dittmann Published: 2026-09-10T00:00:00Z Content type: article Language: en Sources: [Appwrite Blog]() Topics: [Appwrite](), [Computer vision](), [Code](), [Go]() Tags: [announcements](), [automatic](), [computer-vision](), [go](), [image](), [object-detection](), [open-source](), [storage](), [web]() ### AI overview Appwrite introduces AutoGravity, an open-source Go service that adds automatic image cropping to Appwrite Storage. It uses a model pipeline to identify a focal point and returns normalized coordinates that Appwrite's existing image transformation pipeline uses for cropping. The article explains that transformed images are cached and identifies U²-Net as the main model behind the feature. ### Source excerpt AutoGravity brings automatic image cropping to Appwrite Storage. Learn how saliency detection and face detection pick the focal point behind gravity=auto. ## The Reflexes Machine turns a reaction game into an interactive experience DevFeed: [The Reflexes Machine turns a reaction game into an interactive experience]() Original publisher: [Read original article]() Author: Arduino Team Published: 2026-09-09T12:37:27Z Content type: article Language: en Sources: [Arduino Blog]() Topics: [Arduino](), [UNO Q](), [Computer vision](), [webcam](), [real-time](), [Hardware]() Tags: [architecture](), [arduino](), [building](), [camera](), [computer-vision](), [hardware](), [i2c](), [led](), [modulino-nodes](), [project](), [reaction-game](), [real-time](), [reflex-game](), [reflexes-machine](), [sensors](), [uno-q]() ### AI overview An Arduino community member built Reflexes Machine, an arcade-style reaction game using an Arduino UNO Q board, Modulino nodes, a USB camera, illuminated buttons, LED Matrix displays, and sound effects. The camera detects a player's face and automatically starts the countdown. ### Source excerpt What happens when you combine a little imagination with a powerful dual-brain board and a handful of building-block sensors? In the case of Arduino community member LucaDilo, you get a reaction game that doesn't simply wait for someone to press a button... it actually notices when you walk up and invites you to play. Reflexes [...] The post The Reflexes Machine turns a reaction game into an interactive experience appeared first on Arduino Blog. ## Blue Proton Initiative: four 17-year-olds are building AI-powered livestock monitoring with Arduino DevFeed: [Blue Proton Initiative: four 17-year-olds are building AI-powered livestock monitoring with Arduino]() Original publisher: [Read original article]() Author: Arduino Team Published: 2026-08-27T13:18:53Z Content type: article Language: en Sources: [Arduino Blog]() Topics: [Arduino](), [Artificial Intelligence](), [Computer vision](), [Neural Network](), [object-detection](), [C](), [datasets]() Tags: [ai](), [ai-powered-livestock-monitoring](), [arduino](), [computer-vision](), [data](), [livestock-monitoring](), [model-training](), [monitoring](), [neural-networks](), [uno-q](), [yolo]() ### AI overview An Arduino Blog article profiles four 17-year-olds in Italy developing an AI-powered livestock monitoring system with the UNO Q. The project uses computer vision, custom-trained neural networks, and diverse image data to identify animals, count them, and detect possible health problems in real time. ### Source excerpt Pietro Maria Piazza, Alessandro Nesci, Davide Santucci, and Matteo Angiolillo are not waiting to finish school before starting to build something real. Based in Forlì, Italy, the four friends behind Blue Proton Initiative strive to develop an AI-powered livestock monitoring system designed to help farmers identify individual animals and detect early signs of health problems [...] The post Blue Proton Initiative: four 17-year-olds are building AI-powered livestock monitoring with Arduino appeared first on Arduino Blog. ## \[July 2026\] AI Community -- Activity Highlights and Achievements DevFeed: [\[July 2026\] AI Community -- Activity Highlights and Achievements]() Original publisher: [Read original article]() Author: Nari Yoon Published: 2026-08-24T02:23:20Z Content type: article Language: en Sources: [Google Developer Experts - Medium]() Topics: [google-antigravity](), [AI Agent](), [Agentic development](), [Artificial Intelligence](), [SDK](), [Orchestration](), [Computer vision](), [Microsoft Agent Framework]() Tags: [2026](), [agentic-development](), [ai](), [ai-agents](), [computer-vision](), [google](), [google-ai](), [google-antigravity](), [orchestration](), [sdk]() ### AI overview A July 2026 roundup highlights Google AI community projects built with the Antigravity SDK and related tools. The featured work covers asynchronous triggers, autonomous and self-correcting agents, approval-gated workflows, computer vision operations, and parallel multi-agent orchestration. ### Source excerpt We love sharing the accomplishments of the Google AI communities over the month. We appreciate all the hard work and dedication of our community members. Without further ado, here are the key highlights by products! Agentic DevelopmentAntigravity Antigravity has no task queue. Meet @trigger, its real async primitive by AI GDE Omotayo Aina (UK) explores the design philosophy behind Antigravity SDK, detailing how it leverages asyncio and triggers instead of a traditional task queue. It demonstrates how to construct asynchronous patterns like bounded task queues and cron-like scheduling using this minimalist primitive. https://medium.com/media/0311867ab42ff4749c6db6e2653e2716/href Inside the /goal Loop: How to Build Autonomous AI Agents (repository) by GDE Alexander Amin (Germany) explores the architecture of a custom autonomous agent built with Antigravity SDK that coordinates a multi-agent squad to retrieve data and edit documents. It demonstrates how to implement human gate policies and maintain secure, production-ready agentic loops. Anatomy of a Self-Correcting Agent -- How /goal Closes the Loop in Antigravity by AI GDE Krupa Galiya (India) is a framework with a live dashboard to analyze an AI agent's self-correction process. It examines how agents respond to intentional failures through a loop of verification, diagnosis, replanning, and retrying. image source VisionOps Crew: A Multi-Agent Architecture for Computer Vision Operations Using Google ADK and the Antigravity SDK (repository) by AI GDE Henry Ruiz (US) introduces a multi-agent assistant designed to address fragmentation in computer vision engineering using ADK and Antigravity SDK. Henry leverages specialized agents and external tool integrations to coordinate model discovery, data inspection, and workflow execution. EscrowGuard: Building Approval-Gated AI Agents with the Google Antigravity SDK (repository) by AI GDE Aye Hninn Khine (Thailand) leverages Antigravity SDK to build a multi-agent architecture wi ## Building Menu Vision: Real-Time Dish Recognition DevFeed: [Building Menu Vision: Real-Time Dish Recognition]() Original publisher: [Read original article]() Author: Arpitha Dudi, Growth Tech Lead Published: 2026-08-20T00:00:00Z Content type: article Language: en Sources: [Yelp]() Topics: [Computer vision](), [Artificial Intelligence](), [Android](), [cameraX](), [ML Kit](), [Hackathon](), [Development](), [iOS]() Tags: [ai](), [android](), [camerax](), [data-pipeline](), [development](), [hackathon](), [ios](), [ml-kit](), [pipeline](), [prototype](), [real-time](), [recognition](), [vision]() ### AI overview Yelp describes Menu Vision, a feature that uses a phone camera, AI, augmented reality, and text recognition to identify dishes on restaurant menus and surface related user photos and reviews. The article covers its hackathon-origin Android prototype and the production system's on-device machine learning, real-time computer vision, fuzzy matching, and distributed data pipeline. ### Source excerpt Menus aren't just lists, they're a window into a restaurant's unique offerings, specialties, and personality, shaping where and what we choose to eat. But here's the challenge: reading "Kung Pao Chicken - Stir-fried chicken with peanuts in spicy sauce" doesn't tell you what the portions look like, whether other diners loved it, or if it matches your expectations. At Yelp, we knew we had the solution sitting in our user-generated content: hundreds of millions of photos, reviews, and prices for dishes. The problem? Users had to manually search for each dish, an experience that doesn't work well when you're at... ## RAISE Summit 2026: What I Learned About AI, Robotics, Agents, and Infrastructure DevFeed: [RAISE Summit 2026: What I Learned About AI, Robotics, Agents, and Infrastructure]() Original publisher: [Read original article]() Author: Alex Razvant Published: 2026-07-25T07:00:53Z Content type: opinion Language: en Sources: [Neural Bits]() Topics: [Artificial Intelligence](), [Physical AI](), [Robotics](), [World models](), [Computer vision]() Tags: [ai](), [conferences](), [models](), [physical-ai](), [robotics](), [vision]() ### AI overview A personal account of RAISE and MACHINA Summit 2026 in Paris, covering conference themes and observations about AI, robotics, embodied AI, world models, perception, and the differences between language-oriented models and models used for robotic perception and action. ### Source excerpt Key takeaways from talks, demos, and conversations at RAISE and MACHINA conferences in Paris this year. ## MIT researchers develop a real-time spatiotemporal memory framework for robots DevFeed: [MIT researchers develop a real-time spatiotemporal memory framework for robots]() Original publisher: [Read original article]() Author: Adam Zewe | MIT News Published: 2026-06-17T04:00:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [Robotics](), [Artificial Intelligence](), [Computer vision]() Tags: [3d-scene-graphs](), [aeronautical-and-astronautical-engineering](), [ai](), [algorithms](), [artificial-intelligence](), [autonomous-vehicles](), [computer-science-and-technology](), [computer-vision](), [daaam](), [describe-anything-anywhere-at-any-moment](), [electrical-engineering-and-computer-science-eecs](), [laboratory-for-information-and-decision-systems-lids](), [luca-carlone](), [machine-learning](), [map](), [memory](), [mit-schwarzman-college-of-computing](), [nicolas-gorlo](), [paper](), [real-time](), [research](), [robot-memory](), [robotic-perception](), [robotics](), [robots](), [school-of-engineering](), [spatial](), [spatiotemporal-mapping]() ### AI overview MIT researchers developed a long-term spatiotemporal memory framework that helps robots form and recall detailed models of large environments. The system combines map representations with language-based descriptions, answers environmental questions in plain language, and runs fast enough for real-time mobile-robot use. ### Source excerpt A new spatial memory system for robots efficiently captures details about the objects they see while exploring their environment. ## ESP-WHO: Get started DevFeed: [ESP-WHO: Get started]() Original publisher: [Read original article]() Author: John Lee Published: 2026-05-28T00:00:00Z Content type: tutorial Language: en Sources: [Blog on Developer Portal]() Topics: [Tutorial](), [Computer vision](), [Image processing](), [ESP32-S3](), [ESP-IDF](), [Inference](), [Hardware](), [Framework](), [Development]() Tags: [blog](), [camera](), [computer-vision](), [dl](), [esp-idf](), [esp-who](), [esp32](), [esp32-s3](), [face-recognition](), [hardware](), [image-processing](), [inference](), [led](), [tutorial]() ### AI overview This tutorial explains how to set up ESP-WHO on the ESP32-S3-EYE board, run a face recognition example, and extend it with custom detection callbacks. It covers the platform architecture, component pipeline, hardware abstraction, prerequisites, and an LED response when a face is detected. ### Source excerpt In this article, we will set up ESP-WHO on the ESP32-S3-EYE board, running a face recognition example, and extending it with custom detection callbacks. ## Seasons time-lapse - alignment DevFeed: [Seasons time-lapse - alignment]() Original publisher: [Read original article]() Author: Nicolas Fränkel Published: 2026-05-24T00:00:00Z Content type: tutorial Language: en Sources: [Nicolas Fränkel]() Topics: [Computer vision](), [OpenCV](), [image distortion](), [Algorithms]() Tags: [algorithm](), [algorithms](), [art](), [camera](), [computer-vision](), [development](), [iphone](), [open-source](), [python](), [time-lapse](), [transformation]() ### AI overview This article explains how the Seasons time-lapse project aligns photographs taken from slightly different viewpoints and with different phone cameras. It describes using OpenCV feature matching with ORB, followed by RANSAC and a homography to estimate the geometric transformation. ### Source excerpt In the previous post, I described the Seasons project: a time-lapse of hundreds of pictures taken from nearly the same viewpoint over the years. The hardest challenge wasn't taking the pictures or assembling them, but aligning them. You might have noticed the nearly part about viewpoint in the above paragraph. Indeed, it's an approximation. I'm a human being, not a tripod. The position changes ever so slightly, and so does the exact angle. ## From Products to Inspiration: Inside the Engine of Occasion-based outfit visualiser DevFeed: [From Products to Inspiration: Inside the Engine of Occasion-based outfit visualiser]() Original publisher: [Read original article]() Author: Ankit Kumar Published: 2026-04-23T18:23:51Z Content type: article Language: en Sources: [Myntra]() Topics: [Data Science](), [Computer vision](), [Generative AI](), [JSON]() Tags: [ai](), [computer-vision](), [data-science](), [drapes](), [generative-ai](), [json](), [occasion-based-shopping](), [outfit-ideas](), [product](), [shopping]() ### AI overview This article describes how Myntra built its Looks occasion-based outfit visualiser. The feature combines fashion intelligence, data science, computer vision, generative AI, and JSON-based outfit rules to turn individual product images into coordinated outfit recommendations and visualisations. The supplied text details its style taxonomy and curation of over a million styles, but the article is truncated before the visualisation implementation is fully explained. ### Source excerpt Ankit Kumar | Oct 2025 - 6 min read The "Why": Moving Beyond the Grid Picture this: A white background. A shirt. Fabric details. Fit specs. A price tag. For decades, this has been the status quo of online shopping. It is clinical, clear, and -- let's be honest -- completely detached from real life. In this model, the customer does all the heavy lifting. "Where would I wear this?" they wonder. "Does this go with those beige chinos I bought last year?" They close their eyes. They imagine. They guess. Sometimes they buy; often, they bounce. Traditional Product Detail Page (PDP) recommendations tried to help by suggesting jeans to pair with shirts. But the truth is, they remained a list of ingredients, not a prepared meal. At Myntra, we decided to change that. We set out to build Looks, a feature designed to transport a static product into a lived experience -- a Friday night in Bangalore, a high-intensity gym in Gurgaon, or a quiet art gallery in Mumbai. This is the story of how we orchestrated Data Science, Computer Vision, and Generative AI to build a personal stylist that scales to millions. Phase 1: The Brain -- Orchestrating the Look Before we could visualize an outfit, we had to understand fashion. Not just as data points, but as a language. This required Fashion Intelligence: a system that knows what works, what doesn't, and why. Our Data Science team undertook a massive curation effort, analyzing over a million styles. They didn't just tag clothes; they mapped them to the "cascading tree of style." For every Primary Style (e.g., a Polo shirt), the engine identifies four critical layers: The Occasion: (Weekend Outing, Office Smart-Casual) Secondary Style: (The bottom wear) Tertiary Style: (Footwear) Tertiary (others) : (Accessories like watches or sunglasses) The Recipe in the Code The logic is powered by a JSON structure that acts as the "AI Stylist's" brain: JSON "29936239": [ { "Weekend outing": [ [ 29936239, // Primary: The Polo T-Shirt 33551732, // Secondary: B ## New OpenVX Extensions Streamline Compute Workloads on Heterogeneous SoCs DevFeed: [New OpenVX Extensions Streamline Compute Workloads on Heterogeneous SoCs]() Original publisher: [Read original article]() Author: jphilips (jeff@khronosgroup.org) Published: 2026-04-13T13:00:00Z Content type: release Language: en Sources: [Blogs Khronos Blog]() Topics: [Computer vision](), [Artificial Intelligence](), [API](), [Hardware](), [systems]() Tags: [ai](), [api](), [blog](), [compute](), [computer-vision](), [distributed](), [hardware](), [openvx](), [release]() ### AI overview The OpenVX Working Group has released the Target Kernel and Node Command extensions for computer vision and AI applications on heterogeneous SoCs. The extensions support remote compute execution and asynchronous runtime control, and their functionality is planned for inclusion as core features in OpenVX 2.0. ### Source excerpt New extensions address long-standing challenges in distributed computing and runtime control, delivering the final core features for OpenVX 2.0. ## Why Walk-Out Checkout Technology Works Better in Stadiums and Small Stores DevFeed: [Why Walk-Out Checkout Technology Works Better in Stadiums and Small Stores]() Original publisher: [Read original article]() Author: Shawn DeVries Published: 2026-02-16T19:30:00Z Content type: article Language: en Sources: [8th Light Insights]() Topics: [Computer vision](), [Artificial Intelligence](), [Cloud]() Tags: [ai-and-emerging-tech](), [computer-vision](), [edge](), [edge-ai](), [on-device](), [real-time](), [technology](), [video]() ### AI overview The article argues that walk-out checkout technology struggled in grocery stores because of complex inventories, unpredictable customer behavior, and extensive human review. It says the approach is performing better in stadiums and small-format stores, where controlled conditions, limited products, edge AI, and 3D computer vision support rapid transactions. ### Source excerpt This technology platform, called 'Just Walk Out', required over 1,000 workers in India manually reviewing video footage to verify transactions. By 2022, 700 out of every 1,000 sales needed human review. Amazon pulled the technology from their Fresh grocery stores in 2024. They paved the way. But it wasn't sustainable. The problem wasn't the concept. It was the complexity. Grocery stores carry thousands of SKUs, items without barcodes, and unpredictable customer behavior at scale. In fact, this same technology is succeeding in stadiums and small-format stores. Mashgin kiosks are deployed across 150+ sports venues and a number of airports. Guests drop items on a tray, the system scans and prices them using computer vision, and checkout finishes in under 15 seconds. No barcode scanning. No cashier. No human review loop. Over 1.4 million items sold at NFL stadiums in 2024 alone. Median transaction time sits below 15 seconds, even during halftime surges when traditional concessions collapse under volume. Walk out technology works when the context is controlled. Why Stadiums Succeed Where Grocery Failed Stadiums solve the problems that broke Amazon's model: Limited SKUs. Concessions carry hundreds of items, not tens of thousands. High margins. Premium pricing justifies the tech investment. Controlled environment. Fixed locations, predictable lighting, standardized packaging. Edge AI processing. No cloud dependency. No human review. Decisions happen on-device in real time. Mashgin removes the constraint. Throughput scales without adding headcount. The kiosk processes items as fast as fans can place them on the tray. Speed only holds if the system stays up. Any downtime, and the kiosk becomes a bottleneck worse than the line it replaced. How Mashgin Handles Volume Mashgin uses 3D computer vision and edge AI to identify items in real time: Multiple cameras capture items from different angles as they land on the tray On-device neural networks classify products without cloud d ## Last article of 2025 - A Directional Update DevFeed: [Last article of 2025 - A Directional Update]() Original publisher: [Read original article]() Author: Alex Razvant Published: 2025-12-27T14:35:36Z Content type: article Language: en Sources: [Neural Bits]() Topics: [Building AI Systems](), [ai and ml](), [Computer vision](), [Code](), [C](), [C++](), [Python](), [Objective-C](), [Swift](), [iOS]() Tags: [2025](), [ai](), [applications](), [article](), [asus](), [building-ai-systems](), [c-plus-plus](), [code](), [computer-vision](), [ios](), [mac](), [objective-c](), [python](), [swift](), [update]() ### AI overview An end-of-year reflection on how the author's newsletter will change in 2026. The author connects early work and study choices with a gradual move into programming, AI, machine learning, and building AI systems, emphasizing that direction mattered more than speed. ### Source excerpt Reflecting on 2025, and how this newsletter is changing going into 2026 ## Supercharge your OCR Pipelines with Open Models DevFeed: [Supercharge your OCR Pipelines with Open Models]() Original publisher: [Read original article]() Author: merve; Aritra Roy Gosthipaty; Daniel van Strien; Hynek Kydlicek; Andres Marafioti; Vaibhav Srivastav; Pedro Cuenca Published: 2025-10-21T00:00:00Z Content type: tutorial Language: en Sources: [Hugging Face - Blog]() Topics: [document ai](), [Computer vision](), [Open Source](), [multimodal](), [Fine-tuning](), [Language models]() Tags: [artificial-intelligence](), [computer-vision](), [document-ai](), [fine-tuning](), [html](), [llm](), [markdown](), [models](), [multimodal](), [ocr](), [open](), [open-source](), [pipelines](), [qa](), [retrieval](), [vision](), [vlm]() ### AI overview This guide surveys open-weight OCR and vision-language models for document AI. It explains their capabilities, output formats, multimodal document retrieval, document question answering, and the tradeoffs between fine-tuning and using models out of the box. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## How AI Medical Imaging Is Powering Precision Healthcare DevFeed: [How AI Medical Imaging Is Powering Precision Healthcare]() Original publisher: [Read original article]() Author: MARTIN ELIAS COSTA, AI ENGINEER @ TOPTAL Published: 2025-07-18T05:00:00Z Content type: article Language: en Sources: [Toptal Blog]() Topics: [Artificial Intelligence](), [Medical imaging](), [Computer vision](), [Deep neural networks](), [data](), [Development]() Tags: [ai](), [ai-engineer](), [artificial-intelligence](), [computer-vision](), [data](), [development](), [diagnostics](), [healthcare](), [images](), [machine-learning](), [medical-imaging](), [neural-networks]() ### AI overview This article explains how artificial intelligence is transforming medical imaging from image acquisition through diagnosis. It focuses on computer vision, neural networks, medical imaging data, and GPUs, and describes an AI system that analyzes brain MRI scans, identifies demyelinating lesions, measures brain-region volumes, classifies atrophy patterns, and integrates results into electronic health records. ### Source excerpt Artificial intelligence is revolutionizing how medical images are acquired, analyzed, and interpreted. The transformation ushers in a new era of data-driven diagnostics and faster, more personalized patient care. ## Building GARUD, a Video-Powered Animal Tracking System for the NxEVOS Hackathon DevFeed: [Building GARUD, a Video-Powered Animal Tracking System for the NxEVOS Hackathon]() Original publisher: [Read original article]() Author: Amanjeet Singh Gurtatta Published: 2025-06-07T16:01:00Z Content type: opinion Language: en Sources: [Amanjeet Singh]() Topics: [Development](), [Hackathon](), [Robotics](), [Computer vision](), [dashboards]() Tags: [computer-vision](), [dashboards](), [development](), [hackathon](), [robotics]() ### AI overview The article recounts how the authors returned to hackathons as working professionals and developed GARUD, a video-powered animal tracking system, for the NxEVOS Hackathon. The project was inspired by difficulties locating wildlife and was built using the NxEVOS video cloud platform. ### Source excerpt The Hackathon Bond Jasmeet and I have always loved hackathons. Back in our college days, we collectively participated in over 20 hackathons. For me, they helped shape my early interest in Android development; for Jasmeet, it was robotics. In the beginning, we were just figuring out what we enjoyed -- ## Build awesome datasets for video generation DevFeed: [Build awesome datasets for video generation]() Original publisher: [Read original article]() Author: Sayak Paul Published: 2025-02-12T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [datasets](), [Artificial Intelligence](), [Open Source](), [Machine learning](), [Computer vision](), [Tooling](), [Fine-tuning]() Tags: [artificial-intelligence](), [datasets](), [fine-tuning](), [generation](), [guide](), [ocr](), [open](), [open-source](), [pipelines](), [recognition](), [tool](), [training](), [video]() ### AI overview This article presents open tooling for building datasets used to fine-tune video generation models. It describes a three-stage pipeline for downloading videos, splitting them into clips, filtering for watermarks, aesthetics, NSFW content, and motion, and applying captioning, object recognition, and OCR to extracted frames. The tooling is intended to support both small-scale community projects and larger video dataset efforts. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Fine-tuning Florence-2 - Microsoft's Cutting-edge Vision Language Models DevFeed: [Fine-tuning Florence-2 - Microsoft's Cutting-edge Vision Language Models]() Original publisher: [Read original article]() Author: Andres Marafioti; merve; Piotr Skalski Published: 2024-06-24T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [Fine-tuning](), [vlm](), [Computer vision](), [datasets](), [Embeddings]() Tags: [collaboration](), [community](), [computer-vision](), [dataset](), [datasets](), [fine-tuning](), [object-detection](), [ocr](), [open-source](), [research](), [transformer-architecture](), [vision](), [vqa]() ### AI overview The article explains how to fine-tune Microsoft's Florence-2 vision-language model for DocVQA. It describes the model's sequence-to-sequence architecture, its large FLD-5B pre-training dataset, prompting experiments, and evaluation using Levenshtein similarity. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Total noob's intro to Hugging Face Transformers DevFeed: [Total noob's intro to Hugging Face Transformers]() Original publisher: [Read original article]() Author: Andrew Jardine Published: 2024-03-22T00:00:00Z Content type: tutorial Language: en Sources: [Hugging Face - Blog]() Topics: [Transformers](), [hugging face](), [Machine learning](), [Open Source](), [Natural language processing](), [Library](), [Python](), [spaces](), [datasets](), [Computer vision](), [PyTorch](), [Tensorflow]() Tags: [artificial-intelligence](), [community](), [computer-vision](), [datasets](), [frameworks](), [github](), [guide](), [hugging-face](), [library](), [machine-learning](), [open-source](), [python](), [pytorch](), [science](), [source](), [spaces](), [tensorflow](), [transformers](), [vision]() ### AI overview This beginner-friendly guide introduces Hugging Face Transformers and open-source machine learning to readers without prior technical or Python knowledge. It explains the Transformers library, pretrained models, supported tasks and frameworks, the Hugging Face Hub for sharing models and datasets, and Spaces for building and hosting web-based machine learning demos and applications. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## MELON: Reconstructing 3D objects from images with unknown poses DevFeed: [MELON: Reconstructing 3D objects from images with unknown poses]() Original publisher: [Read original article]() Author: Google AI (noreply@blogger.com) Published: 2024-03-18T18:41:00Z Content type: article Language: en Sources: [Google Research]() Topics: [3D](), [Computer vision](), [Inference](), [Google]() Tags: [3d](), [computer-vision](), [gans](), [gaussian-splatting](), [generative](), [google](), [images](), [inference](), [machine-learning](), [models](), [neural](), [research](), [rgb](), [rotation](), [techniques](), [vision]() ### AI overview This Google Research article explains the challenge of reconstructing 3D objects from a small number of images when the camera poses are unknown. It covers pose inference, pseudo-symmetries, local-minimum failures, and prior approaches including NeRF, 3D Gaussian Splatting, GAN-based methods, BARF, SAMURAI, GNeRF, VMRF, SparsePose, and RUST. ### Source excerpt Posted by Mark Matthews, Senior Software Engineer, and Dmitry Lagun, Research Scientist, Google Research A person's prior experience and understanding of the world generally enables them to easily infer what an object looks like in whole, even if only looking at a few 2D pictures of it. Yet the capacity for a computer to reconstruct the shape of an object in 3D given only a few images has remained a difficult algorithmic problem for years. This fundamental computer vision task has applications ranging from the creation of e-commerce 3D models to autonomous vehicle navigation. A key part of the problem is how to determine the exact positions from which images were taken, known as pose inference. If camera poses are known, a range of successful techniques -- such as neural radiance fields (NeRF) or 3D Gaussian Splatting -- can reconstruct an object in 3D. But if these poses are not available, then we face a difficult "chicken and egg" problem where we could determine the poses if we knew the 3D object, but we can't reconstruct the 3D object until we know the camera poses. The problem is made harder by pseudo-symmetries -- i.e., many objects look similar when viewed from different angles. For example, square objects like a chair tend to look similar every 90° rotation. Pseudo-symmetries of an object can be revealed by rendering it on a turntable from various angles and plotting its photometric self-similarity map. Self-Similarity map of a toy truck model. Left: The model is rendered on a turntable from various azimuthal angles, θ. Right: The average L2 RGB similarity of a rendering from θ with that of θ*. The pseudo-similarities are indicated by the dashed red lines. The diagram above only visualizes one dimension of rotation. It becomes even more complex (and difficult to visualize) when introducing more degrees of freedom. Pseudo-symmetries make the problem ill-posed, with naïve approaches often converging to local minima. In practice, such an approach might mistake the [Next page]()