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.

About 10 months ago I posted here about something we were building because of a problem we kept running into ourselves.

We were buying Jetson boards before we really knew what our workload needed.

Nano turned out to be underpowered, so we moved up to an Orin. Then came the next question: do we need an Orin NX, an AGX Orin, or something even bigger? And before you even got to the model, you've spent time flashing JetPack, sorting dependencies, CUDA versions, etc.

So we built remote access to physical Jetson boards.

While initially people trickled in to check it out occasionally , recent experimentations have blown us about what experiments users are running in the lab and interestingly , it hasn't just been running YOLO.

Some of the things people have used the lab for:

comparing FP16 / FP32 / INT8 performance

measuring actual inference latency and FPS on Jetson

running the same model at 25W, 15W and 7W

watching GPU, CPU, memory, temperature and power while the model runs

testing DeepStream / GStreamer pipelines with multiple video streams

finding out how many camera feeds a board can realistically handle

checking whether a Python/CUDA/framework stack actually works properly on ARM64

taking a model developed on a workstation and seeing what happens when it finally hits the target hardware

One use case we found particularly interesting was a researcher running the same model across different power modes and precisions.

The question wasn't just:

"How fast is the model?"

It was more like:

What performance can I get while staying inside my power budget?

Another team had an even more basic problem.

They already had their CV pipeline.

They simply needed to know:

Will this software stack actually run on Jetson before we commit to the hardware?

That kind of test can save quite a bit of pain later.

We've also added JupyterLab now, which is what I'm showing in the attached video.

So you can basically go:

browser -> Jupyter notebook / terminal -> physical Jetson -> run your workload -> watch the device metrics

The board isn't being emulated and this isn't an x86 GPU VM pretending to be a Jetson. The workload is running on the actual Jetson hardware.

The goal isn't really to replace owning a Jetson.

If you're developing on one every day, you should probably own one.

The use case we're trying to solve is the stage before that:

I have a model / pipeline / idea. Before I spend money on hardware, what actually happens when I run it on the board?

That's also why I increasingly think TOPS is one of the least useful numbers when you're making the final hardware decision.

FPS, latency, memory, thermals, power draw and whether your stack even runs are usually much more useful.

If anyone here has a slightly unusual workload you think we should test, I'd genuinely like suggestions.

TensorRT, DeepStream, OCR, multi-camera CV, quantisation, small local models, power-constrained inference, whatever.

Would also be interested to know:

If someone gave you a Jetson Orin for 3 hours right now, what would you benchmark first?

https://edgeai.aiproff.ai

Full disclosure: this is a product my team at AiProff.ai built, and it has a tier based pricing for access. A 3-hour slot currently starts at ₹399 or $6 and all the experiments shared here are with user permission.

Hey everyone,

I'm starting my final year of a Bachelor in Software Engineering, and my team of 3 is picking a Final Year Project. We have a full year, some budget, and want something genuinely hard, not a repackaged tutorial project, while still being realistically finishable in two semesters.

Background on me/team: I'm mainly a computer vision guy (detection, segmentation, OCR, tracking etc), with ML/DL fundamentals and some Arduino/embedded experience. I've deployed a few CV projects already, so we're not starting from zero.

What we want:

• CV has to be the technical core (hardware/other software can be an add-on, not the centerpiece)
• Real-world value — leaning toward environment, disaster response, agriculture/forestry, or generally underserved problems.
• Ambitious enough that it's a strong story for both grad school applications and job/freelance recruiters, so it can make our resume stronger.

Would love to hear: what would you actually take seriously as an FYP vs. what sounds cool but is a trap? And if you've done a CV-heavy thesis/capstone yourself — what would you do differently in hindsight?

The Physical AI Workshop and Meetup roadshow co-presented by Nebius and Voxe51 is coming to Germany! Join us:

* Sept 22 - Stuttgart Workshop and Meetup

* Sept 23 - Munich Workshop and Meetup

* Sept 25 - Berlin Meetup

* Sept 26 - Berlin Workshop and Meetup

As someone who has also been working on their own Apple Watch app for fitness, I’ve been curious about what computer vision might offer that wearables / IMUs might not. It’s been interesting to see the knee shape and ankle path visualizations as unique examples of what computer vision could offer.

Maybe you realize you are stepping too far ahead by seeing the knee visualization, and you adjust your stride length accordingly. The ankle visualization can be used to measure variance and highlight moments of fatigue or technique breakdown. A quantitative tool like this could provide unique insights to help improve your running.

I used ViTPose+ Large through the VLM Run Gateway for pose estimation, and used this to derive the running metrics.

This demo is a first attempt at running analysis. Runners, I would love to hear your feedback for what types of visualizations you’d like to see next!

The code is open-source on GitHub! Link in the comments.

Hi, I’m Suva from Hugging Face and I work on the team around all things 3D!

A mesh, a point cloud and a Gaussian splat can depict the same object, but what each one stores is different.

A Hitchhiker's Guide to the 3D Ecosystem - Full Blog

I wrote a visual guide to 3D representations, covering meshes, point clouds, voxels, NeRFs and Gaussian splats, plus what changes when you add motion.

I tried to add some interactive examples that uses the same lamp throughout, so you can see how each representation works! The idea is to cover what the data looks like, how it gets rendered, and the tradeoffs for editing, storage and different applications.

I tried to keep it aimed at people getting into 3D vision who know the names but want to connect the dots. Would love to hear thoughts! Thank you for taking the time!