Guide on how to utilize 'Weighted Loss' method in yolov5 custom training about yolov5 HOT 2 OPEN

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Designed to be fast, accurate, and easy to use, YOLOv8 is an ideal choice for a wide range of object detection, image segmentation and image classification tasks. With YOLOv8, you'll be able to quickly and accurately detect objects in real-time, streamline your workflows, and achieve new levels of accuracy in your projects.

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pip install ultralytics

from yolov5.

Hello! 😊 Great choice evolving your approach to tackle class imbalance using the Weighted Loss method in YOLOv5.

To implement weighted loss during your custom training with YOLOv5, you'll need to adjust your training configuration to account for the variance in class representation.

Here's a concise way to do it:

1. Modify your data.yaml file by adding a class_weights field. You want to give higher weights to the underrepresented classes. Given your class instance distribution 459:80:14, you might consider a weighting like [0.5, 2, 10] reflecting the inverse proportion of the instances.

   # data.yaml
   train: path/to/train/dataset
   val: path/to/val/dataset
   
   nc: 3
   names: ['player', 'referee', 'ball']
   class_weights: [0.5, 2, 10]

2. Adjust the loss computation in your training script. Unfortunately, out-of-the-box YOLOv5 doesn't directly support weighted loss modification via the configuration file, so you'll need to manually tweak the loss computation within your training code, specifically when calculating the classification loss.

Understanding and making these changes can be somewhat involved, especially if you're new to the codebase. If you need a deeper dive into implementing these changes, I highly recommend checking our detailed methodological guides and model training strategies in the YOLOv5 documentation here: https://docs.ultralytics.com/yolov5/

Good luck with your model training, and feel free to reach out with more questions as you progress! 🌟

from yolov5.