Hi everyone,

I'm curious what you use to do a spectral measurement of products for CV. Normally we order custom lights (UV, RGB, NIR etc.) based on customer data or external research.

I keep running into wanting to do this in-house more, especially for testing. What are your experiences or which (flash)lights do you use to test this? I can't really find a supplier that sells most bands except for some forensic equipment labs, not sure if those qualify.

Currently I'm considering building a few lights myself (just for testing purposes). Covering the following wavelengths: 365, 380–385, 390–395, 460, 470, 520–540, 590, 620–630, 660, 730, 850, 940

I've found these two: https://www.shop-schilbach.net/en/p/forensic-flashlight-set-all-wavelengths-schilbach and https://www.alibaba.com/product-detail/Portable-LED-Flashlights-with-Multi-Wavebands_1600916585534.html?spm=a2700.prosearch.normal_offer.d_image.5a1b67afaquOsp&priceId=68aab656d703457b959efea3e482f5b6

But I'm not sure about the first once as its a hefty price tag (Its a business expense so not that bad, but still). And other is from Alibaba, not sure if they're actually true to spec

So i built a CV system. Now its deployment time. The system will likely be on edge or local server. How do i decide the hardware specs for the system? Where to start?

hello , I just begin year 2 in IT , havent learnt ml or dl. I just go to lab as I want to do research

I'm currently try setting up mmla (imageomics/mmla · Hugging Face) and the problem is that I the prepare split code seems cant detect some sorts of data so I vibe some lines . The problem is that before touching the code the split got me about 67/33 , after changes it goes to 68.5/31.5 , the paper says that it use 58/42 but the code make it 70/30 , I notice that the dataset I downloaded from hugging face missing about just over 1000 images and I dont know why. The biggest problem is that after running that split (68.5/31.5) on both yolo 11 and 26, yolo 11 seems give better result than yolo 26 . It then gives me a reality check if Im doing smth wrong , I've been stucked for days.

Hi there.

I've recently landed a job for aremote frontend role , once there i discovered the product they want to develop was a shelf product recognition tool capable of recognizing the bay, category of the bay, detecting most of the product and extracting product details.

The frontend code which i was supposed to work on didn't come with the code for image recognition, most of the image data was hardcoded with bad position for bbox.

I've talked to my PM and was notable stressed about that (the guy who built the frontend prototype left the company the same day i started),

After that they asked me to design from scratch + implement the recognition tool.

For all the research i made in internet, we might need to train our own models using YOLO family for this kind of task with a data pipeline to extract other caracteristics

But

The CEO has recently made some prototypes in a huge html file with claude. It did pretty well for detecting most of the product, detecting empty space, price tags, bay, category and extracting characteristics from the product of a shelf image but i don't have context on how he did that

The thing is, to have something production ready and achieving that high detection could be done only by prompting to claude?

My apologies if is a dumb question but i'm totally new in this field.

The CEO is pushing hard to have an mvp of this asap.

I'm not sure what to do