Getting to know YOLOX for modern object detection YOLOX is a high-performance object detection model that builds on the YOLO family while rethinking some of its core design choices. Instead of relying on predefined anchor boxes, YOLOX uses an anchor-free approach, which simplifies the detection head and makes training and deployment easier in real-world projects. […]
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Getting to know Detr for smarter object detection Detr (DEtection TRansformer) is a modern approach to object detection that replaces many of the hand-crafted tricks in classic detectors with a clean, transformer-based design. Instead of relying on anchors, custom assignment rules, and complex post-processing, Detr treats detection as a direct set prediction problem: given an
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Getting started with Detectron2 custom dataset training When you move beyond simple demos and pre-trained models, detectron2 custom dataset training is what turns object detection into a real-world tool. Instead of relying only on COCO classes, you teach Detectron2 to recognize the exact objects that matter to your project – fruits on a conveyor belt,
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Getting comfortable with a Detectron2 object detection tutorial A detectron2 object detection tutorial is all about turning raw images into meaningful, labeled scenes using one of Facebook AI’s most powerful computer vision libraries.Instead of hand-writing complex neural networks from scratch, you stand on the shoulders of pre-trained models like Faster R-CNN and ResNet, and focus
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Introduction If you’re looking for a practical way to get started with modern deep learning–based object detection, SSD MobileNet v3 object detection is one of the best places to begin.It’s lightweight, fast, and works great even on standard laptops, which makes it perfect for real-world projects, demos, and tutorials. In this post, we’ll walk through
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Introduction to Pixelib Pixelib is a lightweight, open-source Python library that makes image and video segmentation simple, fast, and practical—even if you’re just getting started.In this tutorial, you’ll learn how to use Pixelib to run state-of-the-art segmentation with just a few lines of code, apply pre-trained models, and adapt workflows for real projects like background
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