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-# Yolov8-custom-object-detection
-Before starting the training process, you need to prepare your dataset with the following folder structure. This structure ensures that YOLOv8 can easily locate the training and validation data.
-
-## Dataset Folder Structure
-
-The dataset should be organized as follows:
-```
-dataset_example/
-│
-├── images/
-│   ├── train/
-│   │   ├── img001.jpg
-│   │   ├── img002.jpg
-│   │   └── ...
-│   ├── val/
-│       ├── img001.jpg
-│       ├── img002.jpg
-│       └── ... 
-│        
-├── labels/
-│   ├── train/
-│   │   ├── img001.txt
-│   │   ├── img002.txt
-│   │   └── ...
-│   ├── val/
-│       ├── img001.txt
-│       ├── img002.txt
-│	    └── ...
-│       
-└── data.yaml
-```
-You can refer to the folder "dataset/dataset_example" as a reference for setting up your dataset and data.yaml file. Make sure that each image in the train and val folders has a corresponding .txt file in the labels folder with the same filename. These .txt files contain the bounding box information for each object in the images.
-
-## Training Output
-When you run the training script in Jupyter Notebook or any other platform, a runs folder will be created automatically. You can check the terminal/output during the training process to see where your training results will be saved. Typically, it will save the results to a directory like runs/detect/train, where you can find model weights, logs, and metrics.