# Image Classification Published articles for Image Classification. 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 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. ## AI Image Classification for PEZ Collectors | Vertex AI & MediaPipe on Android DevFeed: [AI Image Classification for PEZ Collectors | Vertex AI & MediaPipe on Android]() Original publisher: [Read original article]() Author: Mike Wolfson Published: 2024-03-26T18:00:00Z Content type: tutorial Language: en Sources: [My Big Appetite - Mike Wolfson]() Topics: [Artificial Intelligence](), [data](), [App](), [Mobile](), [Website](), [MediaPipe](), [Google]() Tags: [ai](), [android](), [app](), [classification](), [data](), [dataset](), [devices](), [google](), [image-classification](), [mediapipe](), [mobile](), [pez](), [tech](), [vertex](), [vertex-ai]() ### AI overview This tutorial describes building an image-classification model to distinguish visually similar PEZ dispensers. It covers creating a labeled photo dataset, training the model with Vertex AI, and using MediaPipe to support integration on smartphones or websites. ### Source excerpt I've always had a soft spot for PEZ dispensers and have been collecting them for over 30 years. These quirky little collectibles come in an incredible variety of shapes and characters. But there's more to PEZ than just fun; some dispensers can be quite valuable depending on their age and variation. To address the challenge of identification, I harnessed the power of AI to create an image classification model that could help identify these subtle differences. Use Case: Identifying PEZ dispensers Identifying the precise type of PEZ dispenser isn't always easy. Take Mickey Mouse dispensers, for example. While even a novice collector can broadly identify it as Mickey, subtle differences in the face or eyes could separate a common version from a prized rarity. A $1 Mickey and a $150 Mickey can look awfully similar. To illustrate the problem, these Mickey Mouse dispensers look similar, but are worth drastically different amounts (from left-to-right). Mickey Die-Cut (1961) - $125 Mickey B (1989) - $15 Mickey C (1997) - $1 It is fairly easy for even an untrained human to tell the difference between these dispensers. There are obvious differences between the shape of the face, and the eyes. Could a Computer Tell the Difference? I decided to see if I could train a custom image classification model to distinguish between PEZ dispensers. That model, in theory, could be embedded into a mobile app or a website, giving PEZ enthusiasts a tool to aid in identification. For this project, I embraced technologies from Google: Vertex AI to manage my image dataset and train the model, and MediaPipe for easy integration onto edge devices like smartphones. Step 1: Picture This - Building the Dataset Like any good AI project, I needed data. My original plan was to scrape images from the web, but there simply weren't enough consistent, high-quality pictures for the level of detail I wanted. The best solution? Become a PEZ photographer! I gathered a dozen dispensers from my collection and met ## Health-specific embedding tools for dermatology and pathology DevFeed: [Health-specific embedding tools for dermatology and pathology]() Original publisher: [Read original article]() Author: Google AI (noreply@blogger.com) Published: 2024-03-08T19:33:00Z Content type: release Language: en Sources: [Google Research]() Topics: [Medical imaging](), [Embeddings](), [Deep learning](), [Machine learning](), [Google]() Tags: [computer-vision](), [deep-learning](), [embedding](), [google](), [health](), [image-classification](), [medical-imaging](), [product](), [release]() ### AI overview Google Research announces Derm Foundation and Path Foundation, two domain-specific embedding tools for research in dermatology and digital pathology. The tools convert medical images into specialized numerical vectors that researchers can use to develop models for downstream applications. ### Source excerpt Posted by Dave Steiner, Clinical Research Scientist, Google Health, and Rory Pilgrim, Product Manager, Google Research There's a worldwide shortage of access to medical imaging expert interpretation across specialties including radiology, dermatology and pathology. Machine learning (ML) technology can help ease this burden by powering tools that enable doctors to interpret these images more accurately and efficiently. However, the development and implementation of such ML tools are often limited by the availability of high-quality data, ML expertise, and computational resources. One way to catalyze the use of ML for medical imaging is via domain-specific models that utilize deep learning (DL) to capture the information in medical images as compressed numerical vectors (called embeddings). These embeddings represent a type of pre-learned understanding of the important features in an image. Identifying patterns in the embeddings reduces the amount of data, expertise, and compute needed to train performant models as compared to working with high-dimensional data, such as images, directly. Indeed, these embeddings can be used to perform a variety of downstream tasks within the specialized domain (see animated graphic below). This framework of leveraging pre-learned understanding to solve related tasks is similar to that of a seasoned guitar player quickly learning a new song by ear. Because the guitar player has already built up a foundation of skill and understanding, they can quickly pick up the patterns and groove of a new song. Path Foundation is used to convert a small dataset of (image, label) pairs into (embedding, label) pairs. These pairs can then be used to train a task-specific classifier using a linear probe, (i.e., a lightweight linear classifier) as represented in this graphic, or other types of models using the embeddings as input. Once the linear probe is trained, it can be used to make predictions on embeddings from new images. These predictions can be ## Presenting Project Ergo: How to Build an Airplane Detector for Satellite Imagery With Deep Learning DevFeed: [Presenting Project Ergo: How to Build an Airplane Detector for Satellite Imagery With Deep Learning]() Original publisher: [Read original article]() Author: Simone Margaritelli Published: 2018-11-22T17:15:50Z Content type: tutorial Language: en Sources: [evilsocket]() Topics: [Keras](), [Tensorflow](), [Framework](), [Deep learning](), [Deep neural networks](), [datasets](), [Open Source](), [Code](), [Development](), [Back end](), [Python](), [Shell]() Tags: [cnn](), [code](), [computer-vision](), [convolutional-neural-networks](), [cpu](), [cuda](), [cudnn](), [cybersecurity](), [deep-learning](), [deep-neural-networks](), [dnn](), [ergo](), [how-to](), [image-classification](), [keras](), [machine-learning](), [neural-networks](), [nvidia](), [open-source](), [planes](), [planes-detector](), [planesnet](), [project-release](), [python](), [shell](), [tensorflow](), [tf](), [train](), [training](), [tutorial]() ### AI overview The article introduces Project Ergo, an open-source framework and manager for Keras-based machine-learning projects. It demonstrates prototyping, training, and testing a convolutional neural network with the PlanesNet dataset to build an airplane detector for satellite imagery, including CPU and GPU setup considerations. ### Source excerpt It's been a while that i've been quite intensively playing with Deep Learning both for work related research and personal projects. More specifically, I've been using the Keras framework on top of a TensorFlow backend for all sorts of stuff. From big and complex projects for malware detection, to smaller and simpler experiments about ideas i just wanted to quickly implement and test - it didn't really matter the scope of the project, I always found myself struggling with the same issues: code reuse over tens of crap python and shell scripts, datasets and models that are spread all over my dev and prod servers, no real standard for versioning them, no order, no structure. So a few days ago I started writing what it was initially meant to be just a simple wrapper for the main commands of my training pipelines but quickly became a full-fledged framework and manager for all my Keras based projects. Today I'm pleased to open source and present project Ergo by showcasing an example use-case: we'll prototype, train and test a Convolutional Neural Network on top of the PlanesNet raw dataset in order to build an airplane detector for satellite imagery.