# object-detection A computer vision task that identifies objects in images or video and determines their locations. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Quiz: Traditional Face Detection With Python DevFeed: [Quiz: Traditional Face Detection With Python]() Original publisher: [Read original article]() Author: Real Python Published: 2026-09-13T12:00:00Z Content type: article Language: en Sources: [Real Python]() Topics: [Python](), [object-detection](), [Algorithm](), [real-time]() Tags: [article](), [object-detection](), [python](), [real-time](), [tutorial]() ### AI overview An interactive 10-question quiz that tests understanding of traditional face detection with Python, including image representation, Haar-like features, integral images, AdaBoost, and cascading classifiers. ### Source excerpt Test your understanding of face detection with Python. Review Haar-like features, integral images, AdaBoost, and cascading classifiers. ## Amlogic A123X and C305X2 Arm Cortex-A320 SoCs target industrial and low-power AIoT applications DevFeed: [Amlogic A123X and C305X2 Arm Cortex-A320 SoCs target industrial and low-power AIoT applications]() Original publisher: [Read original article]() Author: Jean-Luc Aufranc (CNXSoft) Published: 2026-09-11T03:13:41Z Content type: news Language: en Sources: [CNX Software - Embedded Systems News]() Topics: [Arm](), [cpu](), [Embedded Systems](), [Artificial Intelligence](), [Internet of things](), [Hardware](), [object-detection](), [Neural Network](), [Transformer]() Tags: [aiot](), [amlogic](), [arm](), [armv9](), [camera](), [computer-vision](), [cortex-a320](), [edge-ai](), [embedded](), [ethernet](), [h-264](), [hardware](), [iot](), [linux](), [llm](), [low-power](), [neural](), [npu](), [object-detection](), [processor](), [security](), [soc]() ### AI overview Amlogic has announced the A123X quad-core and C305X2 dual-core Arm Cortex-A320 SoCs for industrial and battery-powered edge AI and IoT devices. The preliminary specifications include video encoding and decoding, NPUs, image signal processing, camera interfaces, networking, USB, and low-power features. The article notes that full specifications, block diagrams, and software details are not yet available. ### Source excerpt Amlogic has unveiled the A123X quad-core and C305X2 dual-core Arm Cortex-A320 SoCs for industrial and battery-powered Edge AI and IoT applications such as robots, dashcams, IP cameras, video conferencing equipment, and so on. The Arm Cortex-A320 low-power Armv9 CPU core was introduced in February 2025, and Amlogic is the first silicon vendor to announce Cortex-A320 SoCs. Details are sparse, with no full specifications or block diagrams and limited software information, but let's see what we know so far. Amlogic A123X Amlogic A123X specifications: CPU - Quad-core Arm Cortex-A320 processor (Armv9.2-A, SVE2) GPU - None or not disclosed VPU H.264/H.265 encoding at 4K @ 60fps H.264/H.265 decoding at 4K @ 30fps AI 4 TOPS ADLA2 NPU for object detection and tracking CNN models 8 TOPS ADLA3 NPU supporting hardware-accelerated Transformer operations for ViT, LLM, etc. Neural network-based hardware SED engine for low-power audio event identification ISP - Low-light HDR ISP Supports [...] The post Amlogic A123X and C305X2 Arm Cortex-A320 SoCs target industrial and low-power AIoT applications appeared first on CNX Software - Embedded Systems News. ## Strengthening Camera Support in Zephyr for Advanced Vision Applications DevFeed: [Strengthening Camera Support in Zephyr for Advanced Vision Applications]() Original publisher: [Read original article]() Author: Zephyr Project Published: 2026-09-04T21:12:17Z Content type: article Language: en Sources: [Zephyr Project]() Topics: [Embedded Systems](), [Embedded Software Dev](), [Open Source](), [Framework](), [Artificial Intelligence](), [object-detection](), [real-time](), [Streaming](), [Robotics]() Tags: [ai](), [blog](), [cameras](), [development](), [embedded](), [embedded-systems](), [events](), [india](), [industry-conference](), [object-detection](), [open-source](), [open-source-summit](), [real-time](), [robotics](), [streaming](), [vision](), [zephyr](), [zero-copy]() ### AI overview This event recap examines proposed changes to Zephyr's camera and driver architecture for AI-driven vision workloads. The proposals include attaching metadata and inference results to individual video buffers and adding per-buffer callbacks to improve buffer ownership, reduce CPU wakeups, and support more manageable camera pipelines. The changes remain under exploration through prototypes and community discussions. ### Source excerpt The Zephyr community came together at Open Source Summit India 2026 in Mumbai to share knowledge and explore developments, tooling, and real-world applications across embedded systems. In this second post event blog, we recap two lightning talks from the Zephyr track focused on camera support. ## What Is the Raspberry Pi AI Kit? And What Replaced It? DevFeed: [What Is the Raspberry Pi AI Kit? And What Replaced It?]() Original publisher: [Read original article]() Author: Dhairya Parikh Published: 2026-09-03T01:38:42Z Content type: tutorial Language: en Sources: [RaspberryTips]() Topics: [Hardware](), [Machine learning](), [Person Detection](), [object-detection](), [Linux](), [Generative AI]() Tags: [artificial-intelligence](), [chatgpt](), [guide](), [hardware](), [how-to-tutorials](), [linux](), [models](), [object-detection](), [raspberry-pi](), [raspberry-pi-5](), [tutorial](), [update](), [vision]() ### AI overview A tutorial explaining what the Raspberry Pi AI Kit was, how it enabled AI and computer vision projects on the Raspberry Pi 5, why it was discontinued, and what replaced it. The article also highlights its affordability, Hailo collaboration, software examples, and current availability. ### Source excerpt When I first wrote about the Raspberry Pi AI Kit, it was one of the easiest ways to add AI acceleration to a Raspberry Pi 5. Well, things moved fast: Raspberry Pi has since discontinued it and introduced newer options instead. The official Raspberry Pi AI Kit made it much easier to run AI applications... ## Build Your Own Face Recognition Tool With Python DevFeed: [Build Your Own Face Recognition Tool With Python]() Original publisher: [Read original article]() Author: Kyle Stratis Published: 2026-09-01T14:00:00Z Content type: tutorial Language: en Sources: [Real Python]() Topics: [Machine learning](), [object-detection](), [Training AI Models](), [Command-line interface]() Tags: [command-line](), [computer-vision](), [machine-learning](), [python](), [testing](), [train](), [tutorial]() ### AI overview A Python tutorial for building a command-line face-recognition tool that detects faces in images, trains and validates a model, and labels detected faces with bounding boxes. ### Source excerpt In this tutorial, you'll build your own face recognition command-line tool with Python. You'll learn how to use face detection to identify faces in an image and label them using face recognition. With this knowledge, you can create your own face recognition tool from start to finish! ## 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. ## What Is an NPU Actually For? CPU vs GPU vs NPU in Frigate DevFeed: [What Is an NPU Actually For? CPU vs GPU vs NPU in Frigate]() Original publisher: [Read original article]() Author: Techno Tim Published: 2026-08-22T13:00:00Z Content type: article Language: en Sources: [Techno Tim]() Topics: [cpu](), [object-detection](), [GPU](), [real-time](), [benchmarking](), [Benchmark](), [Hardware](), [Inference](), [intel](), [Latency](), [cloud-infrastructure]() Tags: [ai](), [benchmark](), [comparison](), [cpu](), [edge](), [gpu](), [hardware](), [inference](), [intel](), [latency](), [object-detection](), [real-time](), [video]() ### AI overview The article compares CPU, integrated GPU, and NPU performance for continuous real-time object detection in Frigate using OpenVINO, four repeatable camera streams, and the same YOLOv9 model. It finds that the NPU did not replace the GPU but matched the integrated GPU while using less total system power and leaving more GPU capacity available for other work. ### Source excerpt NPUs are showing up in more computers, but their job is still harder to explain than the CPU or GPU. I know what I use a CPU for. I know what I use a GPU for. The NPU was kind of a mystery to me. To find out where it actually fits, I ran the same real-time object detection workload on the CPU, GPU, and NPU inside the MINISFORUM MS-03. The test used Frigate, OpenVINO, four repeatable camera st... ## What a Raspberry Pi Can (and Can't) Do With AI DevFeed: [What a Raspberry Pi Can (and Can't) Do With AI]() Original publisher: [Read original article]() Author: Patrick Fromaget Published: 2026-06-24T11:54:14Z Content type: article Language: en Sources: [RaspberryTips]() Topics: [Artificial Intelligence](), [Local AI](), [Large language models (LLMs)](), [object-detection](), [OpenClaw]() Tags: [ai](), [applications](), [computer-vision](), [cpu](), [gpu](), [hardware](), [large-language-models-llms](), [local](), [local-ai](), [object-detection](), [openclaw](), [quick-tips](), [raspberry-pi]() ### AI overview This article explains what Raspberry Pi devices can and cannot do with AI. They can run applications such as object detection, computer vision projects, lightweight AI agents, and some small language models, but limited computing power makes most modern LLMs slow or impractical locally. AI HAT and AI Camera products can improve computer vision workloads but do little for LLM execution. ### Source excerpt AI (artificial intelligence) is a buzzword that has been thrown around a lot these days, and the Raspberry Pi ecosystem is no exception. New use cases have been tested on it, and new products have even been released to accompany this phenomenon. So, what can your Raspberry Pi actually do with AI? A Raspberry Pi... ## Google Earth AI: Unlocking geospatial insights with foundation models and cross-modal reasoning DevFeed: [Google Earth AI: Unlocking geospatial insights with foundation models and cross-modal reasoning]() Original publisher: [Read original article]() Published: 2025-10-23T08:08:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Earth AI](), [Google](), [foundation-models](), [Artificial Intelligence](), [object-detection](), [Cloud]() Tags: [ai](), [climate-sustainability](), [cloud](), [developers](), [earth-ai](), [foundation-models](), [generative-ai](), [google](), [machine-perception](), [models](), [object-detection](), [vision]() ### AI overview Google Research presents Google Earth AI, a family of geospatial AI models and reasoning agents that combines foundation models, Gemini-based orchestration, real-world data, datastores, and geospatial tools to answer complex planetary-scale questions. The article also introduces Remote Sensing Foundations models for satellite imagery analysis using vision-language models, open-vocabulary object detection, and adaptable vision backbones. ### Source excerpt Climate & Sustainability ## How SewerAI is using ClickHouse to modernize sewer management at scale DevFeed: [How SewerAI is using ClickHouse to modernize sewer management at scale]() Original publisher: [Read original article]() Author: ClickHouse Published: 2025-09-19T00:00:00Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [data](), [dataset](), [object-detection]() Tags: [analytics](), [clickhouse](), [computer-vision](), [data](), [real-time]() ### AI overview SewerAI uses ClickHouse for real-time analytics on large volumes of sewer-inspection data and describes denormalizing its dataset into a single Megatable to keep analytics current and costs controlled. ### Source excerpt "Our compute usage remained stable. We didn't have any more failing updates. Because of that, we didn't have backlogs. Our analytics were up to date, and our customers were happy." ## SigLIP 2: A better multilingual vision language encoder DevFeed: [SigLIP 2: A better multilingual vision language encoder]() Original publisher: [Read original article]() Author: Aritra Roy Gosthipaty; merve; Pavel Iakubovskii Published: 2025-02-21T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [Artificial Intelligence](), [computer-use](), [object-detection](), [Google AI](), [Open Source]() Tags: [artificial-intelligence](), [multimodal](), [object-detection](), [open-source](), [performance](), [retrieval](), [train](), [training](), [vision](), [vlms](), [zero-shot]() ### AI overview Google's SigLIP 2 is a multilingual vision-language encoder family that extends SigLIP's sigmoid-loss training with additional objectives for semantic understanding, localization, and dense visual features. The models improve on SigLIP across scales and core capabilities including zero-shot classification, image-text retrieval, and visual representation transfer, with a dynamic-resolution variant for resolution- and aspect-ratio-sensitive tasks. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## PaliGemma 2 Mix - New Instruction Vision Language Models by Google DevFeed: [PaliGemma 2 Mix - New Instruction Vision Language Models by Google]() Original publisher: [Read original article]() Author: merve; Aritra Roy Gosthipaty; Andreas P. Steiner Published: 2025-02-19T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [vlm](), [Fine-tuning](), [Google](), [gemma](), [object-detection](), [datasets]() Tags: [artificial-intelligence](), [datasets](), [demo](), [fine-tuning](), [gemma](), [google](), [llm](), [multimodal](), [object-detection](), [ocr](), [open-source](), [vision](), [vlms]() ### AI overview Google's PaliGemma 2 mix models are fine-tuned on a mixture of vision-language tasks, including OCR, image captioning, visual question answering, document understanding, object detection, and image segmentation. The article explains how the mix models indicate the performance of pretrained PaliGemma 2 checkpoints after fine-tuning and describes their prompting approach. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Building Intelligent Apps with Apple AI Models DevFeed: [Building Intelligent Apps with Apple AI Models]() Original publisher: [Read original article]() Published: 2024-09-18T00:00:00Z Content type: tutorial Language: en Sources: [Kodeco | High quality programming tutorials: iOS, Android, Swift, Kotlin, Unity, and more]() Topics: [AI Development](), [Machine learning](), [object-detection](), [real-time](), [App]() Tags: [ai](), [app](), [apple](), [computer-vision](), [course](), [face-recognition](), [framework](), [machine-learning](), [object-detection](), [on-device](), [real-time](), [use-cases]() ### AI overview A learning path about building intelligent apps with Apple AI Models. It covers on-device machine learning, computer vision with the Vision framework, real-time translation with the Translation framework, and customizing pre-built models with Create ML. ### Source excerpt This course explores on-device machine learning using Apple's powerful tools. See how simple the Vision framework makes complex computer vision tasks, enabling your app to understand the real world, through tasks like object detection and face recognition. Learn to leverage the Translation framework for on-device, real-time language translation, breaking down language barriers for your users. Before finally looking at how to develop your own machine learning models, by customizing Apple's pre-built models for specific use cases within your apps. ## Pollen-Vision: Unified interface for Zero-Shot vision models in robotics DevFeed: [Pollen-Vision: Unified interface for Zero-Shot vision models in robotics]() Original publisher: [Read original article]() Author: Antoine Pirrone; Simon Le Goff; Rouanet; Simon Revelly Published: 2024-03-25T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [Library](), [object-detection](), [Robotics](), [Open Source](), [reachy](), [real-time](), [3D](), [Google](), [Meta]() Tags: [3d](), [artificial-intelligence](), [image-tagging](), [object-detection](), [open-source](), [reachy](), [real-time](), [robotic-manipulation](), [robotics](), [robots](), [tools](), [training](), [vision](), [zero-shot]() ### AI overview Pollen-Vision is an open-source library from the Pollen Robotics team that provides a modular interface for zero-shot vision models in robotics. Its initial release combines models for 3D object detection and segmentation, producing object coordinates to support autonomous grasping and other basic manipulation tasks without additional training. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## The TensorFlow Lite Plugin for Flutter is Officially Available DevFeed: [The TensorFlow Lite Plugin for Flutter is Officially Available]() Original publisher: [Read original article]() Author: TensorFlow Blog (noreply@blogger.com) Published: 2023-08-18T16:00:00Z Content type: news Language: en Sources: [The TensorFlow Blog]() Topics: [Flutter](), [TensorFlow Lite](), [Tensorflow](), [cross-platform](), [Mobile](), [object-detection](), [GitHub](), [Google](), [Kaggle]() Tags: [announcement](), [cross-platform](), [explore](), [flutter](), [github](), [google](), [inference](), [kaggle](), [mobile](), [object-detection](), [plugin](), [tensorflow](), [tensorflow-lite](), [tensorflowlite](), [tutorial]() ### AI overview The TensorFlow Lite plugin for Flutter has been officially migrated to the TensorFlow GitHub account and released. The updated plugin adds features and example apps, including live-camera object detection, and enables local TensorFlow model inference in Flutter apps across supported platforms. ### Source excerpt Posted by Paul Ruiz, Developer Relations Engineer We're excited to announce that the TensorFlow Lite plugin for Flutter has been officially migrated to the TensorFlow GitHub account and released! Three years ago, Amish Garg, one of our talented Google Summer of Code contributors, wrote a widely used TensorFlow Lite plugin for Flutter. The plugin was so popular that we decided to migrate it to our official repo, making it easier to maintain directly by the Google team. We are grateful to Amish for his contributions to the TensorFlow Lite Flutter plugin. Through the efforts of developers in the community, the plugin has been updated to the latest version of TensorFlow Lite, and a collection of new features and example apps have been added, such as object detection through a live camera feed. So what is TensorFlow Lite? TensorFlow Lite is a way to run TensorFlow models on devices locally, supporting mobile, embedded, web, and edge devices. TensorFlow Lite's cross-platform support and on-device performance optimizations make it a great addition to the Flutter development toolbox. Our goal with this plugin is to make it easy to integrate TensorFlow Lite models into Flutter apps across mobile platforms, with desktop support currently in development through the efforts of our developer community. Find pre-trained TensorFlow Lite models on model repos like Kaggle Models or create your own custom TensorFlow Lite models. Let's take a look at how you could use the Flutter TensorFlow Lite plugin for image classification: TensorFlow Lite Image Classification with Flutter First you will need to install the plugin from pub.dev. Once the plugin is installed, you can load a TensorFlow Lite model into your Flutter app and define the input and output tensor shapes. If you're using the MobileNet model, then the input tensor will be a 224 by 224 RGB image, and the output will be a list of confidence scores for the trained labels. // Load model Future _loadModel() async { final opt ## Stanford AI Lab Papers at ICCV 2021 DevFeed: [Stanford AI Lab Papers at ICCV 2021]() Original publisher: [Read original article]() Author: Compiled by Drew A. Hudson Published: 2021-10-08T07:00:00Z Content type: article Language: en Sources: [The Stanford AI Lab Blog]() Topics: [Artificial Intelligence](), [Self-supervised learning](), [multimodal](), [Representation learning](), [object-detection](), [Transformer]() Tags: [3d](), [ai](), [computer-vision](), [diffusion](), [fairness-accountability-transparency](), [learning](), [multimodal](), [object-detection](), [reasoning](), [video](), [vision]() ### AI overview Stanford AI Lab presents its accepted work for ICCV 2021, including research on multimodal medical image recognition, self-supervised learning, diffusion-based shape generation, object pose tracking, human-object relationships in videos, 3D human motion, computational photography, and related vision topics. ### Source excerpt The International Conference on Computer Vision (ICCV 2021) will be hosted virtually next week. We're excited to share all the work from SAIL that will be presented, and you'll find links to papers, videos and blogs below. Feel free to reach out to the contact authors directly to learn more about the work that's happening at Stanford! List of Accepted Papers GLoRIA: A Multimodal Global-Local Representation Learning Framework for Label-efficient Medical Image Recognition Authors: Mars Huang Contact: mschuang@stanford.edu Keywords: medical image, self-supervised learning, multimodal fusion 3D Shape Generation and Completion Through Point-Voxel Diffusion Authors: Linqi Zhou, Yilun Du, Jiajun Wu Contact: linqizhou@stanford.edu Links: Paper | Video | Website Keywords: diffusion, shape generation CAPTRA: CAtegory-level Pose Tracking for Rigid and Articulated Objects from Point Clouds Authors: Yijia Weng*, He Wang*, Qiang Zhou, Yuzhe Qin, Yueqi Duan, Qingnan Fan, Baoquan Chen, Hao Su, Leonidas J. Guibas Contact: yijiaw@stanford.edu Award nominations: Oral Presentation Links: Paper | Video | Website Keywords: category-level object pose tracking, articulated objects Detecting Human-Object Relationships in Videos Authors: Jingwei Ji, Rishi Desai, Juan Carlos Niebles Contact: jingweij@cs.stanford.edu Links: Paper Keywords: human-object relationships, video, detection, transformer, spatio-temporal reasoning Geography-Aware Self-Supervised Learning Authors: Kumar Ayush, Burak Uzkent, Chenlin Meng, Kumar Tanmay, Marshall Burke, David Lobell, Stefano Ermon Contact: kayush@cs.stanford.edu, chenlin@stanford.edu Links: Paper | Website Keywords: self-supervised learning, contrastive learning, remote sensing, spatio-temporal, classification, object detection, segmentation HuMoR: 3D Human Motion Model for Robust Pose Estimation Authors: Davis Rempe, Tolga Birdal, Aaron Hertzmann, Jimei Yang, Srinath Sridhar, Leonidas Guibas Contact: drempe@stanford.edu Award nominations: Oral Presentati ## KotlinDL 0.3: поддержка ONNX, Object Detection API, 20+ новых моделей в ModelHub, и много новых слоев DevFeed: [KotlinDL 0.3: поддержка ONNX, Object Detection API, 20+ новых моделей в ModelHub, и много новых слоев]() Original publisher: [Read original article]() Author: zaleslaw (JetBrains) Published: 2021-10-04T13:56:53Z Content type: release Language: ru Sources: [JetBrains RU]() Topics: [onnx](), [object-detection](), [Maven](), [Maven Central](), [Artificial Intelligence](), [Keras](), [PyTorch]() Tags: [big-data](), [central](), [deep-learning](), [fine-tuning](), [image-recognition](), [keras](), [kotlin](), [machine-learning](), [maven-central](), [object-detection](), [onnx](), [onnx-runtime](), [pytorch](), [tensorflow]() ### AI overview This release article presents KotlinDL 0.3, adding ONNX model support through ONNX Runtime Java API, new ModelHub models, an experimental high-level image-recognition API, additional layers, and Maven Central availability. It also describes object-detection support and fine-tuning limitations for ONNX models. ### Source excerpt Представляем версию 0.3 библиотеки глубокого обучения KotlinDL! Вас ждет множество новых фич: новые модели в ModelHub (включая модели для обнаружения объектов и распознавания лиц), возможность дообучать модели распознавания изображений, экспортированные из Keras и PyTorch в ONNX, экспериментальный высокоуровневый API для распознавания изображений и множество новых слоев, добавленных контрибьюторами. Также KotlinDL теперь доступен в Maven Central. В этой статье мы коснемся самых главных изменений релиза 0.3. Полный список изменений доступен по ссылке. Узнать больше о релизе ## Running Darknet Object Detection on an NVIDIA RTX 3090 DevFeed: [Running Darknet Object Detection on an NVIDIA RTX 3090]() Original publisher: [Read original article]() Author: Techno Tim Published: 2021-01-30T14:00:00Z Content type: tutorial Language: en Sources: [Techno Tim]() Topics: [NVIDIA RTX](), [Machine learning](), [object-detection](), [Ubuntu](), [Windows]() Tags: [homelab](), [machine-learning](), [nvidia-rtx](), [object-detection](), [ubuntu](), [windows]() ### AI overview A tutorial on compiling and running the open-source Darknet neural network on an NVIDIA RTX 3090 in Windows and Ubuntu Linux for object detection on images and real-time video. ### Source excerpt The NVIDIA RTX 3090 is a beast.We all know it can beat the benchmarks in gaming, but how about machine learning and neural networks? Today we walk through the RTX 3090 and then compile and run Darknet, an open source neural network, on Windows and then Ubuntu Linux and run object detection on pictures, images, and real-time video.You will be amazed at how much more you can get out of your vide... ## Android Developer Challenge DevFeed: [Android Developer Challenge]() Original publisher: [Read original article]() Author: Michael Evans Published: 2020-06-22T03:41:17Z Content type: article Language: en Sources: [Gadget Habit]() Topics: [Android](), [Machine Learning & Artificial Intelligence](), [App](), [Google](), [object-detection]() Tags: [android](), [apps](), [camera](), [developer](), [google](), [machine-learning](), [map](), [object](), [on-device]() ### AI overview The article reviews several winning apps from the Android Developer Challenge, highlighting how on-device machine learning enables camera-based object recognition, American Sign Language learning, and obstacle heatmaps for visually impaired people. ### Source excerpt Late last year, Google announced the Android Developer Challenge, a contest for Android developers to show off new experiences made possible by on-device Machine Learning. Since then, tons of developers have submitted their ideas and been hard at work developing their apps. Today, the winners of the challenge have been announced! I was lucky to get access to a cool trial box that Google sent out, complete with little goodies to try out some of the apps from the winners! Here are some obligatory unboxing photos: After checking out the cool loot, I downloaded the winning apps to check them out, and wanted to show off some of my favorites. Trashly The first app I tried was Trashly. The goal of this app is to make recycling easier by providing up-to-date information about where and how to recycle your items. You can type in any item that you're interested in recycling, but what's cooler (and relevant to the challenge) is that you can use the camera to detect an object and find out 1) if the item is recyclable, and 2) where you can go to recycle it. I tried this with a can of soda, which was instantly recognized: And was given a map of nearby places that I could take my can to recycle. Very cool and useful! Leepi The next app that I tried was Leepi. It's a fun, educational app to help users learn American Sign Language. I personally had never learned Sign Language, so this was a really cool way to start! It uses the camera and on-device machine learning to interpret the user's hand positions to verify that they are doing the hand positions and gestures correctly. Path Finder The last app I wanted to talk about was called Path Finder. The gist of this app is to use the camera and machine learning to build a heatmap of obstacles that might be problematic for visually impaired people in public environments. I tried the app out on the streets of New York City and have some screenshots of the results below. I am not sure how useful this would be in practice, but it certainly ## What's new from Firebase at Google I/O 2019 DevFeed: [What's new from Firebase at Google I/O 2019]() Original publisher: [Read original article]() Author: Francis Ma Published: 2019-05-07T00:00:00Z Content type: release Language: en Sources: [Firebase Blog]() Topics: [Firebase](), [ML Kit](), [API](), [Google](), [object-detection](), [Mobile]() Tags: [c-plus-plus](), [crashlytics](), [emulator-suite](), [fabric](), [firebase](), [firestore](), [google](), [google-analytics](), [google-i-o](), [launch](), [machine-learning](), [ml-kit](), [mobile](), [object-detection](), [on-device](), [performance-monitoring](), [test-lab](), [updates]() ### AI overview Firebase announces its biggest Google I/O 2019 updates, including new ML Kit beta capabilities for on-device translation, real-time object detection and tracking, and custom image classification with AutoML Vision Edge. ### Source excerpt News, tutorials, and updates from the Firebase team. ## See how ML Kit and ARKit play together DevFeed: [See how ML Kit and ARKit play together]() Original publisher: [Read original article]() Author: Ibrahim Ulukaya Published: 2018-12-17T00:00:00Z Content type: tutorial Language: en Sources: [Firebase Blog]() Topics: [ML Kit](), [Firebase](), [iOS](), [Mobile](), [Cloud APIs](), [object-detection]() Tags: [android](), [api](), [apple](), [arkit](), [cloud-apis](), [firebase](), [image](), [ios](), [machine-learning](), [ml-kit]() ### AI overview This tutorial explains how to combine ML Kit and ARKit in an iOS project. It describes processing camera frames with ML Kit image labeling, using on-device results for responsiveness and cloud-based labeling for higher accuracy, then displaying the detected label in a 3D scene. ### Source excerpt News, tutorials, and updates from the Firebase team.