Metadata-Version: 2.1
Name: ObjectTrainer
Version: 0.1
Summary: Simplify YOLO model training and data splitting for object detection tasks.
Home-page: https://github.com/Rathoreatri03/Model_Trainer
Author: Atri Rathore
Author-email: <rathoreatri@gmail.com>
Keywords: python,model training,YOLO,data splitting,deep learning
Classifier: Development Status :: 1 - Planning
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: Microsoft :: Windows
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: torch
Requires-Dist: ultralytics


# 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:



```bash

pip install ObjectTrainer

````



## Usage



## Training a YOLO Model



```python

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



```python

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](https://github.com/Rathoreatri03/Model_Trainer/blob/main/LICENSE) file for details.



## Support



For support, please open an issue on our [GitHub repository](https://github.com/Rathoreatri03/Model_Trainer/issues).

```



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.
