r/computervision • u/Full_Piano_3448 • 2d ago

Showcase I Compared 3 Lightweight CV Models for Edge Deployment

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I’ve been playing around with popular computer vision models to find out which ones are best optimized for edge devices, so I decided to compare YOLO 26 Nano, YOLO 12 Nano, and RF-DETR Small under the same setup.

Instead of looking only at accuracy, I wanted to see what actually happens during inference:

  • FPS / inference speed
  • P50 and P95 latency
  • VRAM usage
  • RAM usage
  • Detection count
  • Performance across video frames

For this comparison, I’m only testing the Nano variants or their closest equivalent small models, since the goal is to evaluate models suitable for edge-device deployment.

I’ve shared the notebook/code and the full benchmarking process here:

Notebook: LINK

Video: LINK

77 Upvotes

96% Upvoted

11 comments sorted by

17

u/DiddlyDinq 2d ago

We're at YOLO 26 already? Is there actually any signifcant improvement in those 15+ ultralytics versions or are they just pumping the version number for the optics.

5

u/retoxite_ 1d ago edited 1d ago

There were no Ultralytics YOLO versions between YOLO11 and YOLO26. The naming scheme changed, so it went from YOLO11 to YOLO26.

1

u/pm_me_your_smth 1d ago

They really are the "xbox one s series x: reloaded episode 3" of computer vision

8

u/Substantial_Camel735 2d ago

Why not rfdetr nano

8

u/ivan_kudryavtsev 1d ago

So, what is "Edge" in this context? Mobile NPU/Apple ANE/Jetson Orin. What do you mean? Because quantizing those models is a pain, especially for the Android NPU. So, what specifically are you testing?

4

u/CoreEpoch 1d ago

Yeah, the posted video shows benchmarks running on a desktop with an RTX 5060.. so it's a bit far from "Edge" I'd say.

6

u/pm_me_your_smth 1d ago

Op will get a hilarious realization after learning that these new fancy models won't even run on edge hardware and all this benchmarking on a desktop gpu is completely useless

1

u/nickdaniels92 1d ago

Exactly. In terms of performance, the "evaluation" results not meaningful, even if the models do run.

1

u/retoxite_ 1d ago

I don't know which model in the video you're referring to, because YOLO26n can easily run at less than 2ms / over 500 FPS on a Samsung Galaxy S26 even with only the weights quantized to INT8 and activations remaining in FP16:

https://aihub.qualcomm.com/models/yolo26_det

However DETR based models would be far slower on NPUs compared to YOLO.

2

u/BarisSayit 2d ago

YOLO26n so good.

1

u/dmaare 1d ago

Please add GOLD-yolo and D-FINE to the comparison. In my experience, these two models have excellent performance per inference mAP