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#Ultralytics Reel by @data.science.gate - Visual Quality Inspection with Deep Computer Vision ✅ Precise packaging inspection ✅ Counting, detecting errors in barcodes, product details ✅ Label
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@data.science.gate
Visual Quality Inspection with Deep Computer Vision ✅ Precise packaging inspection ✅ Counting, detecting errors in barcodes, product details ✅ Label placement ✅ No code, an end-to-end platform for AI visual quality inspection #ultralytics #yolov8 #computervision #deeplearning #artificialintelligence #artificialintelligenceai #cnn
#Ultralytics Reel by @coreelectronics - Yolo E works a little differently from usual vision models, learn more at the link. in bio⁠ ⁠ https://coreelec.io/1id
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@coreelectronics
Yolo E works a little differently from usual vision models, learn more at the link. in bio⁠ ⁠ https://coreelec.io/1id
#Ultralytics Reel by @followglobalnetwork - 🔲 NANO MODELS, BIG RESULTS: THE ULTIMATE YOLO VS. RF‑DETR SHOWDOWN. 👉 The author ran a three‑way showdown between today's most talked‑about object
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@followglobalnetwork
🔲 NANO MODELS, BIG RESULTS: THE ULTIMATE YOLO VS. RF‑DETR SHOWDOWN. 👉 The author ran a three‑way showdown between today’s most talked‑about object detection models - YOLO11, the brand‑new YOLO26, and the Transformer‑driven RF‑DETR - to see how they perform in real‑world traffic conditions. 👉 Using a custom PyTorch pipeline with SORT tracking, I trained the Nano (n) versions of all models on a license‑plate dataset and tested them side‑by‑side on high‑traffic video footage. The results were clear: 🥇 RF‑DETR delivered the most stable and consistent detections, especially on small, distant plates. 🥈 YOLO26 impressed with its NMS‑free architecture and tighter boxes, outperforming YOLO11 but not quite matching RF‑DETR’s precision. 🥉 YOLO11 remained reliable but lagged behind the newer architectures in this efficiency‑focused test. A fascinating look at how transformers vs. next‑gen YOLO stack up in real deployment scenarios. 🟥 follow globalnetwork video author: Raef Alshami / globalnetwork YOLOvX Ultralytics Roboflow #YOLOvX #Ultralytics #Roboflow #RFDETR
#Ultralytics Reel by @ai.trend.kr (verified account) - 사진 한 장으로 컨베이어 벨트 위 감자를 완벽하게 세는 AI 등장 🤖🥔 요즘 AI 하면 GPT-4, Claude 같은 거대 모델만 떠올리잖아요. 그런데 제조 현장에서 진짜 필요한 건 이런 거대한 모델이 아니라고 합니다. 개발자 Viet이 컨베이어 벨트 위를 지나
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@ai.trend.kr
사진 한 장으로 컨베이어 벨트 위 감자를 완벽하게 세는 AI 등장 🤖🥔 요즘 AI 하면 GPT-4, Claude 같은 거대 모델만 떠올리잖아요. 그런데 제조 현장에서 진짜 필요한 건 이런 거대한 모델이 아니라고 합니다. 개발자 Viet이 컨베이어 벨트 위를 지나가는 감자를 정확히 세는 AI 시스템을 만들었는데요. 여기서 충격적인 건 학습 데이터가 딱 한 장이라는 점이에요. 어떻게 만들었냐고요? ⒈ Meta의 SAM 2(이미지 분할 모델)로 한 프레임만 라벨링 ⒉ 이걸로 YOLO11 nano(초경량 객체 탐지 모델) 학습 ⒊ Ultralytics ObjectCounter 툴 사용 ⒋ 끝. 실시간으로 완벽 작동함 감자 데이터셋 하나 없이 그냥 한 장 찍어서 학습시켰는데 전체 영상에서 완벽하게 돌아간다는 거죠. 모두가 거대 모델 쫓을 때, 정작 제조 현장에서는 이렇게 가볍고 목적이 확실한 AI가 훨씬 빠르고 확실한 성과를 보여주는 것 같아요. 복잡한 인프라 필요 없고, 실수 줄이고, 시간 절약되고. 이게 진짜 실용적인 AI 아닐까요?💡 Editor ・ kiki Video ・ ©️IlirAliu_ / x Content curated by @ai.trend.kr - AI appears that perfectly counts potatoes on a conveyor belt with just one photo 🤖🥔 These days, when people think of AI, they only think of massive models like GPT-4 or Claude. But they say what’s really needed on the manufacturing floor aren‘t these huge models. Developer Viet created an AI system that accurately counts potatoes passing on a conveyor belt. The shocking part here is that the training data was just a single image. How did he make it? ⒈ Label just one frame with Meta’s SAM 2 (Image Segmentation Model) ⒉ Train YOLO11 nano (Ultra-lightweight Object Detection Model) with this ⒊ Use Ultralytics ObjectCounter tool ⒋ Done. Works perfectly in real-time It means it runs perfectly on the entire video even though it was trained by just taking one picture without a single potato dataset. While everyone chases huge models, it seems such lightweight and purpose-driven AI shows much faster and definite results on the actual manufacturing floor. No need for complex infrastructure, reduces mistakes, and saves time. Isn‘t this truly practical AI?💡 Editor ・ kiki Video ・ ©️IlirAliu_ / x Content curated by @ai.trend.kr
#Ultralytics Reel by @abdullah.jirjees - YOLOv8.0 vs 8.2 vs 9.0 Detection #yolo #objectdetection #python #ultralytics #يولو #بايثو
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@abdullah.jirjees
YOLOv8.0 vs 8.2 vs 9.0 Detection #yolo #objectdetection #python #ultralytics #يولو #بايثو
#Ultralytics Reel by @interestingengineering (verified account) - AI can now help farms with its image recognition technology, removing a tedious task and improving accuracy. 📽️: X/ ultralytics #SmartFarming #a
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@interestingengineering
AI can now help farms with its image recognition technology, removing a tedious task and improving accuracy. 📽️: X/ ultralytics #SmartFarming #agritech #farmmanagement #AIInFarming
#Ultralytics Reel by @officialprojecto - Quanto era richiesto il video su #YOLO da 1 a 11? #ObjectDetection #ObjectDetector #intelligenzaartificiale #artificialintelligence #ai #DeepLearning
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@officialprojecto
Quanto era richiesto il video su #YOLO da 1 a 11? #ObjectDetection #ObjectDetector #intelligenzaartificiale #artificialintelligence #ai #DeepLearning #yolov11 #poseestimation #torch #pytorch #onnx #ultralytics
#Ultralytics Reel by @imfhope - If you don't know that now everything flows to the server, including road information. Fundamentals of machine learning Module 12 Units. This is Nan
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@imfhope
If you don’t know that now everything flows to the server, including road information. Fundamentals of machine learning Module 12 Units. This is Nano Model, Big Impact: Counting Cars with YOLOv8 🚗 Have you ever wondered how traffic flow is monitored in real time? It's often done with sophisticated computer vision systems, and I've been experimenting with one of the latest and greatest: YOLOv8! It identifies and counts vehicles in real time. It's amazing to see how it picks out buses and cars. The best part? This nano model is surprisingly powerful. It's resource-efficient, fast, and easy to deploy. It's a fascinating journey exploring how we can use AI to make our roads smarter and safer! What do you think about using AI for traffic monitoring? I'd love to hear your thoughts and ideas! #Ultralytics #OpenCV #YOLOv8 #ComputerVision #TrafficMonitoring #DeepLearning #MachineLearning #AI #Innovation
#Ultralytics Reel by @datamovesher - Got a chance to play with Ultralytics YOLOv8 2D pose estimation this weekend. For those not yet privy, 2D pose estimation is a computer vision techni
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@datamovesher
Got a chance to play with Ultralytics YOLOv8 2D pose estimation this weekend. For those not yet privy, 2D pose estimation is a computer vision technique that enables machines to estimate the positions and orientations of human body joints (keypoints) from 2D images or videos. By mapping the joints and their connections, computers can comprehend human motion. 2D Pose Estimation has a myriad of applications beyond watching a pink haired lady do gymnastics. Imagine its impact on physical therapy, sports performance analysis, robotics, or even in designing lifelike characters for video games! Although my colab was just basically straight from the documentation, I'd still be happy to share it if it would help anyone. You'd basically only have to upload your video and you'd be good. Hope you enjoy my little bit of exploration from this weekend! Next stop, 3D pose estimation! #computervision #ultralytics #yolov8 #ai #deeplearning #artificialintelligence #datascience
#Ultralytics Reel by @gorselanaliz_ - 🚀 YOLOv8 ile geliştirilen modeller nesne sayım veya object counting uygulamalarında rahatlıkla kullanılabilir. 👨‍💻 Bu tip uygulamaları geliştirmek
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@gorselanaliz_
🚀 YOLOv8 ile geliştirilen modeller nesne sayım veya object counting uygulamalarında rahatlıkla kullanılabilir. 👨‍💻 Bu tip uygulamaları geliştirmek için internet sitemizdeki blog yazımıza göz atabilirsiniz. [Link biyografimizde..] #yolov8 #opencv #ultralytics #teknofest #objecttracking #computervision
#Ultralytics Reel by @code_helping - A senior AI engineer built a vision system that counts potatoes on a conveyor belt 🥔📷. No giant datasets. No massive models. Just a clear problem an
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@code_helping
A senior AI engineer built a vision system that counts potatoes on a conveyor belt 🥔📷. No giant datasets. No massive models. Just a clear problem and a smart setup. He used Ultralytics ObjectCounter with a tiny YOLO11-nano model 🤖. Since no potato dataset existed, he annotated one single frame using SAM 2 and trained from that. One frame — and it still works across the whole video 🎥. A good reminder: real industrial AI usually looks like this. Focused. Lightweight. Practical ⚙️. If you’re in manufacturing or robotics, these small systems are often the quickest wins. They save time, reduce errors, and don’t require heavy infrastructure 🏭⚡. His projects: github.com/vietnh1009 . . . . #ai #aitools #computervision #yolo #ultralytics #automation #industry40 #machinelearning #robotics #cloudengineer #backenddevelopment #scalability #webdevelopment #codingtips #techlearning #code_helping #decisiontree #neuralnetworks #artificialintelligence #cnn #rnn #transformers #gpt
#Ultralytics Reel by @pyresearch - Instance Segmentation with Object Tracking using Ultralytics YOLOv8 video all available : https://www.youtube.com/@Pyresearch #computervision #objec
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@pyresearch
Instance Segmentation with Object Tracking using Ultralytics YOLOv8 video all available : https://www.youtube.com/@Pyresearch #computervision #objectdetection #InstanceSegmentation hashtag#yolo #yolov8 #opencv hashtag#airplane #transportation #ai #Ultralytics

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#Ultralytics is one of the most engaging trends on Instagram right now. With over thousands of posts in this category, creators like @interestingengineering, @code_helping and @ai.trend.kr are leading the way with their viral content. Browse these popular videos anonymously on Pictame.

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