How to Train YOLOv8 on Custom Dataset? A Comprehensive Guide

Leave a Comment / By Jane Torres / April 1, 2024

Introduction

YOLOv8, or You Only Look Once version 8, is a state-of-the-art object detection algorithm known for its speed and accuracy. Training YOLOv8 on a custom dataset is vital if you want to apply it to your specific task and dataset.

This guide will walk you through the process of Train YOLOv8 on Custom Dataset on your own dataset, enabling you to detect objects of interest in images or videos.

Prerequisites

Before diving into the training process, make sure you have the following prerequisites installed on your system:

Python: YOLOv8 is implemented in Python, so ensure you have Python installed on your machine.
Git: Clone the YOLOv8 repository from GitHub by running git clone https://github.com/ultralytics/yolov8 in your terminal.
Dependencies: Install the required dependencies by running pip install -U -r requirements.txt inside the YOLOv8 directory.
Dataset: Prepare your custom dataset in the required format. Each image should have an associated annotation file, typically in YOLO format, specifying object bounding boxes.

Preparing the Custom Dataset

Preparing the Custom Dataset

1: Data Annotation:

Annotate your dataset with bounding boxes around objects of interest. Tools like LabelImg or RectLabel can help in this process.

Save annotations in YOLO format, where each line in the annotation file corresponds to an object in the image, with the format class x_center y_center width height.

2: Dataset Structure:

Organize your dataset into training and validation sets. The recommended structure is as follows:

kotlin
/path/to/dataset/
├── images/
| ├── train/
| | ├── img1.jpg
| | ├── img2.jpg
| | └── …
| └── val/
| ├── img3.jpg
| ├── img4.jpg
| └── …
└── labels/
├── train/
| ├── img1.txt
| ├── img2.txt
| └── …
└── val/
├── img3.txt
├── img4.txt
└── …

3: Configuration Files

YOLOv8 uses configuration files to specify training parameters. Modify the yolov8.yaml file in the yolov8/data directory to suit your dataset’s characteristics.

4: Adjust the following parameters:

nc: Number of classes.
names: List of class names.
train, val: Paths to your training and validation datasets.

Training YOLOv8:

Run the following command to start the training process:

bash
python train.py –img-size 640 –batch-size 16 –epochs 100 –data data/yolov8.yaml –weights yolov8.ckpt
–img-size: Input image size for training.
–batch-size: Number of images per batch.
–epochs: Number of training epochs.
–data: Path to the configuration file.
–weights: Pre-trained weights (optional but recommended).

During training, YOLOv8 will save checkpoints, and you can monitor progress using Tensor Board:

bash
tensor board –logdir=runs

Inference with Trained Model

After training, you can perform inference on new images using the following command:

bash
python detect.py –weights runs/exp/weights/best.pt –img-size 640 –conf 0.4 –source /path/to/test/images

–weights: Path to the trained weights.
–img-size: Input image size for inference.
–conf: Confidence threshold for detections.
–source: Path to the directory containing test images.

How to Train YOLOv8 on Custom Dataset? write step by step guide

Training YOLOv8 on a custom dataset involves several steps. YOLOv8 is an object detection model, and training it on a custom dataset requires the following steps:

1: Install Dependencies:

Make sure you have Python and pip installed. Install the required dependencies using the following commands:

bash
pip install torch torchvision
pip install -U PyYAML

2: Clone YOLOv8 Repository:

Clone the YOLOv8 repository from GitHub:

bash
git clone https://github.com/ultralytics/yolov5.git
cd yolov5

3: Prepare Custom Dataset:

Organize your custom dataset in the YOLO format, where each annotation file corresponds to an image and contains information about the bounding boxes and class labels. The directory structure should look like this:

bash
/Custom_dataset
/Images
image1.jpg
image2.jpg
…
/Labels
image1.txt
image2.txt
…

4: Create YAML File:

Create a YAML file that defines the configuration for your custom dataset. Save this file as custom. Yaml. An example YAML file might look like this affecting epochs required to train YOLOv8:

yaml
train: ../custom_dataset/images/train/
val: ../custom_dataset/images/val/
nc: 1 # number of classes
names: [‘object’]

Adjust the paths and class names according to your dataset.

5: Train the Model:

Run the training script by specifying the custom YAML file:

bash
python train.py –img-size 640 –batch-size 16 –epochs 50 –data custom. yaml –weights yolov5s.pt
–img-size: Input image size (default is 640).
–batch-size: Batch size for training (adjust according to your GPU memory).
–epochs: Number of training epochs.
–data: Path to your custom YAML file.
–weights: Pre-trained weights (you can start with yolov5s.pt).

6: Evaluate the Model:

After training, you can evaluate the model on the validation set:

bash
python val.py –img-size 640 –batch-size 16 –data custom. Yaml –weights runs/train/exp/weights/best.pt

Replace runs/train/exp/weights/best.pt with the path to the best weights obtained during training.

7: Test the Model:

Test the trained model on new images:

bash
python detect.py –source path/to/test/images/ –weights runs/train/exp/weights/best.pt
Replace path/to/test/images/ with the path to the directory containing test images

Remember to adjust paths and parameters based on your specific dataset and requirements. Additionally, refer to the YOLOv5 documentation for more advanced configurations and options.

Conclusion

Training YOLOv8 on a custom dataset involves careful preparation, configuration, and execution. By following this guide, you should be able to adapt YOLOv8 to your specific object detection task, providing accurate and efficient results.

Keep in mind that hyperparameter tuning and additional data augmentation techniques can further improve the model’s performance.

FAQS (Frequently Asked Questions)

Q#1: What is YOLOv8, and why should I train it on a custom dataset?

YOLOv8 (You Only Look Once version 8) is a real-time object detection algorithm known for its speed and accuracy. Training it on a custom dataset allows you to create a model tailored to your specific needs, enabling accurate detection of objects relevant to your application, such as custom objects or unique scenarios not covered by pre-trained models.

Q#2: How do I prepare my custom dataset for YOLOv8 training?

Ensure your dataset is organized in the YOLO format, which typically includes images and corresponding label files. Label files should contain bounding box coordinates and class labels for each object of interest. Use tools like LabelImg or YOLO Annotation Tool to annotate your dataset. Finally, split your dataset into training and validation sets to assess model performance.

Q#3: What are the essential configuration parameters for YOLOv8 training?

Key parameters include the model architecture (e.g., YOLOv3 or YOLOv4), batch size, image size, and the number of classes in your dataset. Additionally, set the paths to your training and validation datasets, as well as the pre-trained weights if you choose to fine-tune an existing model. Adjust other hyperparameters, like learning rate and augmentation options, based on your dataset characteristics.

Q#4: How can I fine-tune YOLOv8 with my custom dataset?

Fine-tuning YOLOv8 involves starting with pre-trained weights on a large dataset, like COCO, and then training the model on your specific dataset. Set the pre-trained weights path in your configuration file and ensure that the last layers of the model are modified to match the number of classes in your dataset. Fine-tuning allows the model to adapt its knowledge to your custom objects while retaining the features learned from the pre-trained weights.

Q#5: What steps should I follow to monitor and evaluate the training progress of YOLOv8?

Utilize tools like Tensor Board to monitor training metrics such as loss, precision, and recall. Regularly check the validation set performance to prevent overfitting. Experiment with different hyperparameters to improve results. After training, assess the model’s accuracy on new images by running inference. Fine-tune the model as needed based on the evaluation results to achieve optimal object detection performance for your custom dataset.

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I’m Jane Austen, a skilled content writer with the ability to simplify any complex topic. I focus on delivering valuable tips and strategies throughout my articles.

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