Custom object detection in Python using YOLOv8

TL;DR

Learn how to train YOLO V8 object detection models on a custom dataset using Python.

Transcript

hello everyone and welcome to my YouTube channel in today's video we are going to take a look at how to train YOLO p8 on a custom data set using python now I have seen a lot of YouTube videos on YOLO V8 and almost all of them are using the command line interface and they are creating data using some kind of labeling tool but in today's video I'm go... Read More

Key Insights

• 🥰 YOLO V8 offers state-of-the-art object detection with real-time performance.
• 💁 Converting data to the YOLO format is necessary for training YOLO V8 models.
• 💁 The Plastic In River dataset is an example of a dataset that needs to be converted to the YOLO format for training.
• 😄 YOLO V8 training and inference can be performed using Python, providing flexibility and ease of use.
• 💁 Different bounding box formats exist, but they can be converted to the YOLO format with simple mathematical calculations.
• ☠️ The YOLO V8 model can be fine-tuned using different parameters, such as image size and learning rate.
• 👶 After training, the model can be used for object detection on new images.

Summary & Key Takeaways

• YOLO V8 is a popular object detection model claimed to be state-of-the-art, and Python can be used for training and inference.

• Converting data to the YOLO format is necessary for training YOLO V8, and there are different formats for bounding boxes, including coordinate-based formats and YOLO's normalized width and height format.

• The Plastic In River dataset is used as an example, and the video explains how to convert this dataset into the YOLO format for training.