Fine-Tuning YOLOv10 for Object Detection on a Custom Dataset #yolo #finetuning

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...

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#yolo #yolov10 #ObjectDetection #finetuning  #CancerCellDetection #objectdetection jectDetection

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