# object-detection Published articles for object-detection. 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. ## NeoEyes NE302 - A tiny USB-C-powered WiFi 6 Edge AI Vision camera based on STM32N6 MCU DevFeed: [NeoEyes NE302 - A tiny USB-C-powered WiFi 6 Edge AI Vision camera based on STM32N6 MCU]() Original publisher: [Read original article]() Author: Jean-Luc Aufranc (CNXSoft) Published: 2026-09-12T02:19:36Z Content type: news Language: en Sources: [CNX Software - Embedded Systems News]() Topics: [Embedded Systems](), [Microcontroller](), [wifi 6](), [Hardware](), [object-detection]() Tags: [c-c-plus-plus](), [camera](), [computer-vision](), [edge-ai](), [embedded-systems](), [hardware](), [mcu](), [object-detection](), [open-source](), [stm32](), [stmicro-stm32](), [vision](), [wifi-6]() ### AI overview The article reports on CamThink's NeoEyes NE302, a compact USB-C-powered Wi-Fi 6 edge AI vision camera built around the STM32N6 MCU. It describes the device's reduced memory and feature set compared with the NE301, along with its camera, wireless, debugging, video-encoding, object-detection, networking, OTA, and security capabilities. ### Source excerpt CamThink NeoEyes NE302 is a tiny WiFi 6 Edge AI camera based on an STM32N6 Arm Cortex-M55 MCU with Neural-ART NPU which the company says is "especially suitable for developers and makers". I initially thought about it as a smaller version of the CamThink NeoEyes NE301 based on the same STM32N6 MCU and 4MP OS04C10 camera sensor. But it's quite a different device. First, it doesn't have a battery, and the USB-C is only used for power. Memory and storage capacities have been reduced to 32 MB PSRAM and 64 MP SPI flash, and a range of built-in and optional features have been removed, including 4G LTE module (global or US), audio wafers, PIR motion connector, and a 16-pin GPIO header, as well as support for PoE power. CamThink NeoEyes NE302 specifications: Vision MCU STMicro STM32N6 MCU Core - Arm 32-bit Cortex-M55 CPU @ up to 800MHz with Arm Helium [...] The post NeoEyes NE302 - A tiny USB-C-powered WiFi 6 Edge AI Vision camera based on STM32N6 MCU appeared first on CNX Software - Embedded Systems News. ## 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. ## 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. ## 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. ## 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 Software Dev](), [Embedded Systems](), [Open Source](), [Artificial Intelligence](), [systems]() Tags: [ai](), [blog](), [cameras](), [embedded](), [embedded-systems](), [events](), [industry-conference](), [object-detection](), [open-source](), [vision](), [zephyr]() ### AI overview This event recap examines proposed changes to Zephyr's camera and driver architecture for AI vision workloads. It discusses metadata attached to video buffers, per-buffer callbacks, zero-copy sharing, bounded latency, backpressure, and streaming coordination. The proposals 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. ## Arduino Core on Zephyr 1.0.0 is here! DevFeed: [Arduino Core on Zephyr 1.0.0 is here!]() Original publisher: [Read original article]() Author: Arduino Team Published: 2026-09-03T15:20:44Z Content type: release Language: en Sources: [Arduino Blog]() Topics: [Arduino](), [Release notes](), [Hardware](), [real-time](), [Robotics](), [Automation](), [Qualcomm]() Tags: [arduino](), [arduino-core-on-zephyr](), [arduino-ide](), [bug-fixes](), [camera](), [image-classification](), [object-detection](), [physical-ai](), [qualcomm](), [real-time](), [release](), [robotics](), [ventuno-q](), [zephyr]() ### AI overview Arduino announced ArduinoCore-Zephyr 1.0.0, adding support for the VENTUNO Q board, updating the base to Zephyr 4.4.1, enabling camera support for the Nicla Vision module, and including bug fixes and cleanup. ### Source excerpt We're excited to announce version 1.0.0 of the ArduinoCore-Zephyr, our biggest release yet. Building on the stability milestone we hit with 0.90.0, this release brings new hardware support, an updated Zephyr base, and a long list of fixes and improvements across the core. Thank you to everyone who tested 0.90.0, filed issues, and helped us [...] The post Arduino Core on Zephyr 1.0.0 is here! appeared first on Arduino Blog. ## 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... ## 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... ## One-Shot Any Web App with Gradio's gr.HTML DevFeed: [One-Shot Any Web App with Gradio's gr.HTML]() Original publisher: [Read original article]() Author: yuvraj sharma; hysts; Freddy Boulton Published: 2026-02-18T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [web applications](), [modern web development](), [HTML5 and CSS3 tricks](), [React UI animations]() Tags: [3d](), [app](), [artificial-intelligence](), [claude](), [community](), [css](), [gradio](), [html](), [html5](), [javascript](), [object-detection](), [python](), [react](), [spaces](), [speech](), [three-js](), [transcription](), [vibe-coding]() ### AI overview The article shows how Gradio's gr.HTML can create interactive, single-file Python web apps without a separate frontend build step. Examples include timers, a kanban board, visual ML result viewers, 3D controls, and live speech transcription. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## 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 ## nanoVLM: The simplest repository to train your VLM in pure PyTorch DevFeed: [nanoVLM: The simplest repository to train your VLM in pure PyTorch]() Original publisher: [Read original article]() Author: Aritra Roy Gosthipaty; Luis; Andres Marafioti; Sergio Paniego; merve; Pedro Cuenca; Vaibhav Srivastav Published: 2025-05-21T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [vlm](), [PyTorch](), [Language models](), [llama](), [Transformer](), [Artificial Intelligence]() Tags: [community](), [llama](), [llm](), [nanovlm](), [object-detection](), [open](), [pytorch](), [research](), [train](), [training](), [vision](), [vlm]() ### AI overview nanoVLM is a minimal, readable pure-PyTorch toolkit for building and training vision-language models. It processes images and text to generate text, focuses on visual question answering, and is designed to help beginners explore VLMs through a compact codebase and guided training workflow. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## 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. ## Welcome, Gradio 5 DevFeed: [Welcome, Gradio 5]() Original publisher: [Read original article]() Author: Abubakar Abid Published: 2024-10-09T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [gradio](), [Server-side rendering](), [Streaming](), [WebRTC](), [Open Source](), [Artificial Intelligence](), [object-detection](), [webcam]() Tags: [ai](), [gradio](), [object-detection](), [open-source](), [playground](), [python](), [security](), [server-side-rendering](), [spaces](), [time](), [webcam](), [webrtc]() ### AI overview Welcome, Gradio 5 introduces production-oriented improvements for building machine learning web applications in Python, including faster loading through server-side rendering, refreshed components and themes, low-latency streaming, WebRTC support, expanded streaming examples, an experimental AI Playground, and improved web security. ### 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. ## 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. ## 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... ## 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.