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In our latest video, we finetune and compare YOLO instance segmentation models on a medical imaging dataset.
~ The significance of image segmentation, differentiating between semantic and instance segmentation with practical examples.
~ Using the Nuclei Instance Segmentation dataset consisting of 665 image-mask pairs over 31 human and mouse organ samples.
~ Splitting the dataset into 80:20 ratio for training and validation.
~ Step-by-step process to finetune YOLOv8 and YOLOv9 models on the medical imaging dataset.
~ Converting image masks to COCO and YOLO format annotations.
~ Training the models with Ultralytics, covering all necessary steps from environment setup to model evaluation.
~ Visualizing and comparing the performance of YOLOv8 and YOLOv9 models.
~ Detailed insights into the training process, loss metrics, and inference results.
💡 What You’ll Learn:
How to prepare and process a custom medical imaging dataset for instance segmentation.
The workflow for converting dataset annotations to YOLO format.
Fine-tuning YOLOv8 and YOLOv9 models and comparing their performance.
Practical applications of instance segmentation in medical imaging.
Watch our video on Learn OpenCV to dive into the implementation and fine-tuning experiments with YOLOv8 and YOLOv9 models for medical imaging.
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16 сен 2024
