Image resizing and annotations in YOLOv8

[junbro1016]

Hello, I am a student participating in an object detection-related competition using the YOLOv8 model.

We downloaded an image dataset, and the annotations were provided in COCO format JSON files. We converted these annotations into YOLOv8 annotation text files by our own python codes. Afterward, we uploaded the original images and the COCO format annotation files to the Roboflow platform to obtain another dataset. We noticed that Roboflow resized the original images to 640 x 640. (The original image sizes were different) The annotation text files remained the same as the ones we created.

We trained the model using two different datasets: the one with our modified annotations and the original images, and the other with the resized images and annotations from Roboflow. Surprisingly, the model's performance was better with the dataset obtained from Roboflow.

Here are my questions:

Is resizing to 640 x 640 a factor that can improve the performance of the YOLOv8 model?
If it is, YOLOv8 uses the (x_center, y_center, width, height) format to represent bounding boxes. If the aspect ratio of the image changes due to resizing, wouldn't this format not align with the annotations? Could this potentially lead to a decrease in performance?
I would like to ask for your insights. I am still a beginner and facing difficulties, so I would be extremely grateful for help from experts. Thank you very much!

[suhailshaji]

@HistoryDan
The default model image input size for training a YOLOv8 model is 640 . If you want to specify on the train argument, you must specify it imgsz='' arguments Roboflow will resize the images and also the associated bounding box values w.r.t. image size.

check the documentation - Object detection label format: https://docs.ultralytics.com/datasets/detect/

consistent input sizes aligns can lead to better performance.

[glenn-jocher]

@suhailshaji hello there! It's great to hear about your involvement in an object detection competition using YOLOv8. 🚀

Resizing images to a consistent size like 640x640 can indeed improve the performance of the YOLOv8 model. This is because neural networks often benefit from uniformity in input data dimensions, allowing the model to learn more efficiently.

When images are resized, the bounding box annotations must also be transformed accordingly to maintain their correct positions and dimensions relative to the new image size. If the aspect ratio changes during resizing, the annotations need to be adjusted to reflect this change, otherwise, it could indeed lead to a decrease in performance.

The fact that you observed better performance with the Roboflow dataset suggests that their resizing and annotation transformation process was correctly handled. It's important to ensure that any resizing operation is accompanied by the appropriate scaling of the bounding box coordinates.

For more detailed insights on how YOLOv8 handles annotations and image resizing, you can refer to the Ultralytics documentation on dataset preparation and training. Keep experimenting and learning, and don't hesitate to reach out if you have more questions. Happy detecting! 😊