Amini Cocoa Contamination Challenge
Helping Ghana
$7 000 USD
Completed (over 1 year ago)
Computer Vision
Object Detection
938 joined
255 active
Start
14 Feb 25
Close
11 May 25
Reveal
12 May 25
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Submission structure
Help · 27 Mar 2025, 09:44 · 11
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!
These requirements, like deciphering ancient hieroglyphs etched on a digital tablet. Precision, order, and boundaries – the very bedrock of a successful submission. I once grappled with a similar conundrum, trying to align disparate datasets for a predictive model; a real Connections Game, that was.
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Thanks for sharing this perspective on the Cocoa Contamination Challenge dataset! Feature engineering around satellite imagery and localized climate factors seems to play a massive role in improving model performance here. Excited to see what techniques yield the best results on the leaderboard. Get Dpboss Result.
This is a great question! I've wondered about the exact same things regarding submission formatting. It's easy to overlook these details, especially when you're focusing on the core model. Knowing the precise requirements for ymin, ymax, xmin, and xmax bounds, and if there's a strict image order, could definitely save some headaches. It's like navigating a steep Slope Unblocked – you want all the information before you start! Thanks for asking and helping others too!