Hey Everyone,
When it comes to the submission csv file, are there any other requirements that need to be taken into account such as rounding of the conf value or upper and lower bounds for ymin, ymax, xmin and xmax or order of image files (does it need to be the same order as the sample submission)?
I am new to these challenges so I aplogise in advance if my question is a bit of a dumb one.
Thanks in advance!
Don't worry. Just submit, what your model predicts. If you get this:
Error. Image ID ID_ksM9yC.JPG not found in submission file
Try reducing the confidence level or use a recursive function to that. It just means your model didn't give a bounding box for that submission. I think you might see the inference setup from the starter notebook, I guess. I didn't actually go through that😅
Thanks for the reply. Yeah, I was trying to train my model on roboflow, but roboflow seems to output bounding boxes in a different way comapared to the normal yolo way when using Python. I used Python, and the starter notebook as a guide and it's working now.
Great!
Submission CSV requirements matter! Are conf values rounded? Are there upper/lower bounds for bounding box coordinates (ymin, ymax, xmin, xmax)? Image order must match the sample. Mastering these details is like nailing a perfect Moto X3M jump – precision is key to success. Good luck!
Are we talking precision confidences, or just ballpark figures? Image order, a chaotic jumble, or meticulously aligned? And those bounding box coordinates, are they cosmic infinities or bound by earthly pixels? This reminds me of wrestling with a similarly finicky data import at my old job – the system refused to recognize the dates unless they were formatted exactly DD-MMM-YYYY. Truly a Block Breaker moment when I cracked that code!
Okay, diving right in! This submission CSV query feels familiar, doesn't it? Ah, the devil's in the details, always lurking in those submission files. Precision can be a killer in these challenges; those bounds are tricky beasts. Speaking of tricky, once I was building a Love Tester for a hackathon and the input ranges were completely off, leading to hilariously skewed results. I remember wrestling with the normalization for hours. Ensuring your image order matches the sample is a good shout too - missed that one before to my cost!