ObjectTrainer 0.1

Simplify YOLO model training and data splitting for object detection tasks.

ObjectTrainer

ObjectTrainer is a Python package designed to simplify the process of training YOLO (You Only Look Once) models for object detection tasks. With ObjectTrainer, users can easily train YOLO models using custom datasets and split their data into training, validation, and testing sets. The package provides classes for both model training and data splitting, allowing users to efficiently manage their training pipeline. Additionally, ObjectTrainer includes functionality for saving the best-performing model weights, making it easy to deploy trained models for inference tasks.

Features

• Train YOLO models with custom datasets

• Split datasets into training, validation, and testing sets

• Save best-performing model weights for deployment

Installation

You can install ObjectTrainer using pip:

pip install ObjectTrainer

Usage

Training a YOLO Model

from ObjectTrainer import YOLO_trainer



# Initialize YOLO Trainer with absolute data.yaml folder path and absolute destination folder path for best weights

trainer = YOLO_trainer(Data_yaml_fold_path='path/to/data.yaml', Best_Weight_dest='path/to/destination', epochs=50)



# Run the full training process

trainer.run_full_training()

Splitting Data

from ObjectTrainer import data_splitter



# Initialize Data Splitter with absolute data folder path, destination folder path, and number of classes

splitter = data_splitter(data_folder='path/to/data', dest_fold='path/to/destination', no_classes=3)



# Run the full data splitting process

splitter.run_full_split()

License

Model Trainer is licensed under the MIT License. See the LICENSE file for details.

Support

For support, please open an issue on our GitHub repository.

This Markdown-formatted README includes the updated usage instructions with the new single-method calls for training and data splitting, making it easy for users to follow and implement.