🔑Annotate the data using CVAT, a popular computer vision annotation tool.
🔑Structure the file system with specific directories for images and labels.
🔑Create the binary masks for the images using a Python script.
🔑Train the semantic segmentation model using YOLO V8.
🔑Use Google Colab for faster training with access to a GPU.
What is semantic segmentation?
—Semantic segmentation is a computer vision technique that involves labeling each pixel in an image with a corresponding class label.
Why use YOLO V8 for semantic segmentation?
—YOLO V8 is a popular object detection model that can also be used for semantic segmentation. Its architecture and features make it suitable for this task.
What is CVAT?
—CVAT is a computer vision annotation tool that simplifies the process of annotating data for computer vision tasks like semantic segmentation.
Why is it important to structure the file system with specific directories?
—Structuring the file system with specific directories makes it easier to organize and locate the data during the training process.
What are the advantages of training the model in Google Colab?
—Training the model in Google Colab provides access to a free GPU, which significantly speeds up the training process.
00:00Introduction to training a semantic segmentation algorithm using YOLO V8.
03:10Annotating the data using the CVAT annotation tool.
08:10Creating the binary masks for the images using a Python script.
13:28Structuring the file system with specific directories for images and labels.
17:40Training the semantic segmentation model using YOLO V8.
