# image recognition Published articles for image recognition. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## AI agent reliability requires recovery infrastructure for partial failures DevFeed: [AI agent reliability requires recovery infrastructure for partial failures]() Original publisher: [Read original article]() Author: Melanie Warrick Published: 2026-04-01T00:00:00Z Content type: opinion Language: en Sources: [Temporal Blog]() Topics: [Artificial Intelligence](), [reliability](), [Resilience](), [agent observability](), [Machine learning](), [coding assistant](), [Neural Network](), [image recognition](), [Google](), [recommendation systems]() Tags: [agent](), [agents](), [ai](), [ai-agents](), [coding-assistant](), [google](), [image-recognition](), [neural-network](), [recommendation-systems](), [reliability](), [resilience](), [temporal-voices]() ### AI overview AI agents can reason through complex tasks and run long workflows, but failures during execution can compound when systems cannot checkpoint progress, recover from partial failures, or resume work. The article argues that dependable AI requires durable infrastructure and resilience in addition to better models, placing current agent reliability challenges in the context of earlier AI failures. ### Source excerpt Smart AI agents still fail mid-workflow. Learn why solving the AI reliability gap requires durable infrastructure, not just better models. ## Four ways to run ONNX models on a GPU with CUDA in Clojure DevFeed: [Four ways to run ONNX models on a GPU with CUDA in Clojure]() Original publisher: [Read original article]() Published: 2025-11-09T17:49:00Z Content type: tutorial Language: en Sources: [Dragan Djuric]() Topics: [CUDA](), [GPU](), [onnx](), [Clojure](), [Artificial Intelligence](), [Deep neural networks]() Tags: [ai](), [clojure](), [code](), [cuda](), [deep](), [diamond](), [examples](), [gpu](), [image-recognition](), [model](), [neural-networks](), [onnx](), [tensors]() ### AI overview A tutorial presents four ways to run ONNX models on a GPU with CUDA using Clojure libraries including Diamond ONNX RT, Deep Diamond, and ClojureCUDA. It covers GPU tensor backends and a configuration that keeps input and output tensors in main memory while executing the model on the GPU. ### Source excerpt Two weeks ago, I announced a new Clojure ML library, Diamond ONNX RT, which integrates ONNX Runtime into Deep Diamond. In that post, we explored the classic Hello World example of Neural Networks, MNIST handwritten image recognition, step-by-step. We run that example on the CPU, from main memory. The next logical step is to execute this stuff on the GPU. You'll see that with a little help of ClojureCUDA and Deep Diamond built-in CUDA machinery, this is both easy and simple, requiring almost no effort from a curious Clojure programmer. But don't just trust me; let's fire up your REPL, and we can continue together. Here's how you can evaluate this directly in your REPL (you can use the Hello World that is provided in the ./examples sub-folder of Diamond ONNX RT as a springboard). Require Diamond's namespaces First things first, we refer functions that we're going to use. (require '[uncomplicate.commons.core :refer [with-release]] '[uncomplicate.neanderthal.core :refer [transfer! iamax native]] '[uncomplicate.diamond [tensor :refer [tensor with-diamond]] [dnn :refer [network]] [onnxrt :refer [onnx]]] '[uncomplicate.diamond.internal.dnnl.factory :refer [dnnl-factory]] '[uncomplicate.diamond.internal.cudnn.factory :refer [cudnn-factory]] '[hello-world.native :refer [input-desc input-tz mnist-onnx]]) None of the following ways to run CUDA models has preference, you use the one that best suits your needs. Way one One of the ways to run ONNX models on your GPU is to simply use Deep Diamond's cuDNN factory as the backend for your tensors. Then, the machinery recognizes what you need and proceeds doing everything on the GPU, using the right stream for tensors, Deep Diamond operations, and ONNX Runtime operations. This looks exactly the same as any other Deep Diamond example from this blog or the DLFP book. (with-diamond cudnn-factory [] (with-release [cuda-input-tz (tensor input-desc) mnist (network cuda-input-tz [mnist-onnx]) classify! (mnist cuda-input-tz)] (transfer! input ## Announcing Winners of the Square + Google Cloud AI Hackathon DevFeed: [Announcing Winners of the Square + Google Cloud AI Hackathon]() Original publisher: [Read original article]() Author: Eva Sasson Published: 2023-11-21T07:00:00Z Content type: news Language: en Sources: [Square Corner Blog RSS Feed]() Topics: [Hackathon](), [Artificial Intelligence](), [Generative AI](), [Google Cloud Platform (GCP)](), [Google]() Tags: [ai](), [ai-apis](), [apis](), [cloud](), [demo](), [generative-ai](), [google-cloud](), [hackathon](), [image-recognition]() ### AI overview Square and Google Cloud announce the winners of their AI hackathon, highlighting projects built with generative AI, image recognition, and Square and Google Cloud APIs. ### Source excerpt Check out the winning submissions using generative AI, image recognition, and more ## 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 ## Using a Neural Network for sending memes to my girlfriend DevFeed: [Using a Neural Network for sending memes to my girlfriend]() Original publisher: [Read original article]() Published: 2021-05-20T00:00:00Z Content type: tutorial Language: en Sources: [Mutt0-ds Notes]() Topics: [Neural Network](), [image recognition](), [ImageNet](), [Reddit](), [API](), [Library](), [email]() Tags: [api](), [cats](), [email](), [image-recognition](), [images](), [library](), [model](), [neural](), [neural-network](), [password](), [reddit]() ### AI overview This tutorial describes a project that uses Reddit's API and the praw library to download wholesome memes, applies a ResNet50 image-recognition model to identify animal content, and emails selected memes to a recipient. ### Source excerpt Well, my GF's tastes in memes are simple: she loves wholesome memes and, most importantly, she loves animals. That's why I created a fun... ## How we wrote chicken egg counter on a Raspberry PI DevFeed: [How we wrote chicken egg counter on a Raspberry PI]() Original publisher: [Read original article]() Author: Ivan Ursul Published: 2018-10-03T09:23:24Z Content type: tutorial Language: en Sources: [Ivan Ursul]() Topics: [Raspberry Pi](), [OpenCV](), [webcam](), [Linux](), [Databases]() Tags: [camera](), [image-recognition](), [java](), [linux](), [migrations](), [mongodb](), [network](), [raspberry-pi](), [stream](), [webcam]() ### AI overview A project account describing the design of a Raspberry Pi application that uses a webcam and OpenCV-based image recognition to count chicken eggs on a factory conveyor and send the count to a database. It discusses tracking eggs across camera frames and preprocessing images to identify contours and ellipses. ### Source excerpt How it started Besides my main work on Upwork I quite often pick different projets. So I found a project, where I had to write a program for recognizing chicken eggs on a factory stream line. Customer wanted to install the application on computer with web camera, put this camera at a top of stream line and the application had to calculate eggs and send them to the DB. He also wanted to run this program on a cheap computer. The quality of the network in the factory isn't stable, so the program had to be resilient to outstand the network issues. There was enough challenges for me, so I decided to participate on this project. ## Using TensorFlow and the Serverless Framework for deep learning and image recognition DevFeed: [Using TensorFlow and the Serverless Framework for deep learning and image recognition]() Original publisher: [Read original article]() Author: Rustem Feyzkhanov Published: 2018-07-24T00:00:00Z Content type: tutorial Language: en Sources: [Serverless Blog]() Topics: [Tensorflow](), [serverless framework](), [Deep learning](), [AWS Lambda](), [Amazon API Gateway](), [Amazon Web Services]() Tags: [api-gateway](), [aws](), [aws-lambda](), [cloud-computing](), [deep-learning](), [engineering](), [faas](), [function-as-a-service](), [how-to](), [image-recognition](), [infrastructure-as-code](), [serverless](), [serverless-architecture](), [serverless-framework](), [tensorflow]() ### AI overview A tutorial on deploying a simple deep learning model for image recognition with TensorFlow, the Serverless Framework, AWS Lambda, and API Gateway. ### Source excerpt We'll cover how to use TensorFlow, the Serverless Framework, AWS Lambda and API Gateway to deploy a simple deep learning model. ## Building an image recognition endpoint with Serverless and Google Cloud Functions. DevFeed: [Building an image recognition endpoint with Serverless and Google Cloud Functions.]() Original publisher: [Read original article]() Author: Alex DeBrie Published: 2017-10-20T00:00:00Z Content type: tutorial Language: en Sources: [Serverless Blog]() Topics: [Cloud Functions](), [Serverless](), [API](), [Cloud]() Tags: [api](), [aws](), [aws-lambda](), [building](), [cloud-computing](), [cloud-functions](), [engineering](), [faas](), [function-as-a-service](), [image-recognition](), [images](), [infrastructure-as-code](), [serverless](), [serverless-architecture](), [serverless-framework](), [vision]() ### AI overview A tutorial on using Serverless, Google Cloud Functions, and the Cloud Vision API to build an image-classification endpoint. ### Source excerpt Use Serverless, Google Cloud Functions, and the Cloud Vision API to build an endpoint to classify images.