Object detection is the task of detecting where in an image an object is located and classifying every object of interest in a given image. To solve this problem Iam using YOLOv8 Model.
This is a initial version of custom trianing with YOLOv8. Currently YOLOv8 is the newest state-of-the-art YOLO model that can be used for object detection and instance segmentation tasks.
I have used Yolov8m for custom training with Bikes data. I did training in Google colab by reading data from Google drive. The notebook explains the below steps:
1.Setting Up Google Colab 2.YOLOV8 Installation 3.Mounting Google Drive 4.Create bike_detetcion.yaml (dataset config file) (YOLOV8 format) 5.Training Our Custom bike Detetcion Model 6.Metrics 7.Run Inference With Custom YOLOv8 Object Detector Trained Weights.
Based on the inference results, the trained model is doing a great job. We can still imrpove it by using large yolov8 models, additional data and hyperparameter changes. Model file is also available for any type of testing.
1.https://github.com/ultralytics/ultralytics 2.https://docs.ultralytics.com/tasks/detection/