Fine-Tuning YOLOv10 for Object Detection on a Custom Dataset скачать в хорошем качестве

Fine-Tuning YOLOv10 for Object Detection on a Custom Dataset

YOLOv10 is a new generation in the YOLO series for real-time end-to-end object detection. It aims to improve both the performance and efficiency of YOLO models by eliminating the need for non-maximum suppression (NMS) and comprehensively optimizing the model architecture. In this tutorial, we will explore its architecture and how to fine-tune it to detect cancer cells for cancer diagnosis. ⭐️ Contents ⭐️ 00:00 Introduction to YOLOv10 07:03 Exploring dataset from Roboflow Universe 09:19 Install YOLOv10 and Download pre-trained weights 11:28 Downloading Dataset from roboflow 14:30 Custom Training 17:58 Validate Custom Model 19:05 Inference with Custom Model 📚 Resources 📚 Yolov10 paper: https://arxiv.org/abs/2405.14458 Cancer cell-box dataset : https://universe.roboflow.com/nationa... Colab Notebook: https://colab.research.google.com/dri... 🔔 Our Newsletter and Featured Articles: https://abonia1.github.io/newsletter/ 🔗 Linkedin:   / aboniasojasingarayar   🔗 Find me on Github : https://github.com/Abonia1 🔗 Medium Articles:   / abonia   #yolo #yolov10 #ObjectDetection #finetuning #CancerCellDetection #objectdetection jectDetection