Training YOLOv8 on Custom Datasets
Last updated September 4, 2024
Training a YOLOv8 model on custom datasets empowers you to tailor object detection capabilities to specific domains and applications. This guide provides a detailed walkthrough of the training process, covering data preparation, configuration, and training execution.
Preparing Your Custom Dataset
- Image Collection: Gather a comprehensive dataset of images containing the objects you want to detect. Ensure the images are of good quality and diverse in terms of backgrounds, lighting conditions, and object poses.
- Annotation: Annotate the objects in your images by creating bounding boxes and assigning labels to each object. You can manually annotate images using tools like LabelImg or leverage automatic annotation options available in the Ultralytics library.
- Dataset Organization: Organize your annotated dataset in a structured format. The Ultralytics library expects a specific dataset structure:
- Images: Store your images in a directory named "images".
- Labels: Create a corresponding directory named "labels" and place the annotation files (e.g., .txt files with bounding box coordinates and class labels) within.
- Class Definitions: Define your object classes in a file named "data.yaml". This file contains information about the number of classes, class names, and the dataset paths.
Configuring Training Parameters
- Model Selection: Choose the appropriate YOLOv8 model for your task. Ultralytics offers several model variations based on accuracy and speed trade-offs.
- Dataset Paths: Specify the paths to your image and label directories in the training configuration file ("train.yaml").
- Training Hyperparameters: Fine-tune training hyperparameters such as:
- Batch size
- Learning rate
- Epochs
- Optimizer
- Weight decay
- Data Augmentation: Utilize data augmentation techniques to increase the diversity of your training dataset. This includes random cropping, flipping, scaling, and color adjustments.
Training Your YOLOv8 Model
- Run Training: Execute the training process using the Ultralytics library's command-line interface: `python train.py --data your_data.yaml --cfg your_model.yaml`.
- Monitoring Progress: Monitor the training progress by observing metrics such as loss, precision, recall, and mean Average Precision (mAP).
- Evaluation: Evaluate the performance of your trained model on a separate validation set.
- Saving and Deploying: Save the trained weights for future use and deploy your YOLOv8 model for real-time object detection in your target application.
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