Hi, I’m currently working on my master’s thesis, where I need to evaluate multiple models for defect detection on synthetic datasets and eventually estimate the position of the defects in the image.
Here’s my setup: I generated various 3D models of parts and wrote a Python script to render images of them. To avoid regenerating the models from scratch, I simulated different part scales by applying multiple zoom levels in the renderer.
My goal is to train a model on this custom dataset to detect defects and estimate their bounding boxes using computer vision.
My question is: will using different zoom levels cause inaccuracies in the bounding box coordinates, and if so, is this something to address during training, or can it simply be corrected at inference time using a basic matrix transformation?
thanks alot for your help 
Yes, different zoom levels are fine for Ultralytics YOLO training, and they usually help the model become more robust to scale changes.
The key point is that the label must match the final rendered image. If your zoom is just a post-render resize, then the box can be corrected with a simple scale transform. If it is a true camera zoom / FOV change inside the renderer, then you should recompute the 2D box from the rendered projection, not try to “fix” it later with one generic matrix.
During training, YOLO already handles normal image resizing/letterboxing consistently, and at inference predictions are mapped back to the original image size. If you ever need to manually rescale boxes between image shapes, use scale_boxes() in the Ultralytics utilities or see the scale_boxes() reference.
So short answer: no inherent inaccuracy from multiple zoom levels, as long as your annotations are generated from the exact final image geometry. If you want, I can also suggest a good synthetic-data setup for defect detection with Ultralytics YOLO26.
Thanks for the support.
So basically your answer covers the scenario where the datasets are already annotated with one constant zoom level and then resized afterward. But if the dataset is generated with various zoom levels and annotated accordingly, then the bounding boxes should be accurate — however, this requires more annotation effort. What would you suggest in this case? I’m also interested in the setup you mentioned.
If you generate each image at its own zoom level and export the annotation from that same render/camera state, that’s the best setup. I would not manually re-annotate those images if your renderer already knows the defect geometry. Instead, automate the labels: project the defect mesh/mask into the final rendered image, compute the visible 2D bounding rectangle, clip it to the image bounds, then save YOLO-format labels as normalized class x_center y_center width height.
That makes zoom simply another synthetic variation, like lighting, pose, background, or camera distance. The important part is that the label is generated after the final camera/FOV/zoom/resolution decision, not from a “base zoom” label that is later guessed back into place.
For defect detection, I’d suggest this workflow: generate a broad synthetic train set with randomized zoom/FOV, lighting, material, camera pose, background, and defect size/severity; keep a separate validation/test set with fixed, known distributions; and if possible include a small real-image test set to measure the synthetic-to-real gap. If the defect shape matters more than just its enclosing box, consider training yolo26n-seg.pt instead of plain detection, since masks can give better position/extent information for irregular defects. For simple rectangular localization, yolo26n.pt or yolo26s.pt is a good starting point.
Minimal training example:
from ultralytics import YOLO
model = YOLO("yolo26n.pt")
model.train(data="defects.yaml", imgsz=640, epochs=100)
Your dataset should follow the standard YOLO detection dataset format with normalized labels. You can also use the Ultralytics Platform if you want a simpler workflow for reviewing labels, training, and comparing experiments.