YOLO11 detect model for the modern beehive
This model runs with the help of the Ultralytics Python library. If you are not familiar with the train() method or something else, I highly recommend you visit the Ultralytics docs page.
Models that can be trained off of are located in the models/ directory. Only use models/yolo11-obb.yaml if you plan on training a new model from scratch. Otherwise, use models/model.pt. Specify the model in train.py like so:
model = YOLO("models/model.pt") # replace with weighted modelThe organization directory contains tools that will help you organize and prepare a modified dataset for training. Use organization/annotations.py to fetch the number of annotations for a class in the dataset, as well as the total number of annotations in the dataset. This is very important for weighting your classes during training.
Modify weights in dataset.yaml. Use the formula (TOTAL ANNOTATIONS) / (NUM CLASSES * ANNOTATIONS FOR CLASS X) to calculate the weight for each class.
The organization directory also contains sort.py. This organizes the labels and images in your dataset into train/ and val/ subdirectories. 80% of images and their respective labels will go into the train/ subdirectories, while the remaining 20% will be randomly organized into the val/ subdirectories. It is vital that you do this prior to training to ensure even representation of data. Remember to set the environment variable DATASET to the path of your dataset prior to running organization/sort.py.
On Linux:
export DATASET=/path/to/datasetOn Windows:
set DATASET=/path/to/dataset