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▲ 15 r/Ultralytics

Power robotic pick-and-place workflows with Ultralytics YOLO26! 🤖

Using Ultralytics YOLO26, objects and container states can be detected in real time, providing the visual information needed for automated material handling. In this example, computer vision is used to:

✅ Detect foam panels for robotic handling
✅ Identify whether containers are ready or not ready
✅ Monitor container occupancy and object counts

Combining computer vision with industrial robotics can help automate repetitive material-handling tasks while improving visibility across production workflows.

u/muhammadrizwanmunr — 3 days ago
▲ 29 r/Ultralytics+2 crossposts

Tried YOLO26n-depth in RK3576

Since Ultralytics officially supports exporting YOLO26n-Depth to RKNN, I tried running it on RK3576.

With a simple Python video inference test, I’m getting around 3–4 FPS for now. The code and model haven’t been optimized yet, so I think there is still quite a lot of room for improvement.

One thing I noticed is that the RKNN model exported directly by Ultralytics has only one output.

For comparison, the optimized YOLO11 / YOLOv10 models from Rockchip’s rknn_model_zoo use 9 outputs, which seems to be better optimized for the Rockchip NPU and moves some of the decoding/post-processing outside the model.

So 3–4 FPS is just the initial result. I’m going to see if YOLO26n-Depth can be optimized in a similar way.

Has anyone already tried optimizing YOLO26 or YOLO26-Depth for RK3576/RK3588?

u/n1k0n1k0n1 — 5 days ago
▲ 39 r/Ultralytics

Improve operational visibility with forklift activity monitoring! 🏗️

Tracking forklift activity across warehouses and manufacturing facilities can provide insights into vehicle movement, high-traffic areas, and operational bottlenecks.

These insights can support safer and more efficient warehouse operations by helping to:
✅ Monitor forklift movement and activity at scale
✅ Measure fleet utilization across facilities
✅ Identify congestion and workflow bottlenecks
✅ Support safety and compliance initiatives

u/muhammadrizwanmunr — 5 days ago
▲ 5 r/Ultralytics

Advice on how to proceed with fine tuning Yolo26n.pt locally with Python?

I haven't trained or fine tuned a model yet. But I have been using the model to detect wildlife and have been saving the images. I use the model to help me run moose off from the garden, but the dataset doesn't have moose in it, so I have it detect horse and cow and assume it's a moose and it works well. But now I'd like to make something a bit more accurate using my linux machine with an NVIDIA Pro 6000 Blackwell with 96GB of VRAM and Python. I have a few questions.

a) I have 415 images my cameras have taken of moose, and the images include the bounding box (no label) around the moose, will this work?

b) I have 54 images of bears taken from my cameras, would it help to add them to the dataset even though the COCO dataset already includes bears, given it would factor in my landscape or would it make things worse?

c) I have about 3500 images of the wife and myself, could I include us as a category so the model can distinguish between us and others or would that just confuse things?

d) My images of moose only puts one bounding box around one moose even when there are more in an image, should I ensure bounding boxes around all of them before using them for fine tuning?

e) I assume I would make a .yaml which would point to a folder structure of my new images and their labels before running the python command to fine tune, should I include the entire COCO dataset to prevent memory loss of the originals?

f) I can only use 18 of the objects in the COCO dataset, would it make sense to download the COCO training dataset and delete "fire hydrant" and "bus" and all the things that we will never see out here in the forest?

Many thanks for any pointers or advice, I know it's a noob question and shows that I have barely done any research.

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u/Noob_to_the_Noob — 7 days ago
▲ 32 r/Ultralytics

Interactive object tracking with Ultralytics YOLO26 🚀

What if you could click on any tracked object and instantly see its cropped view?

✅ Objects are tracked in real time
✅ Click any tracked object
✅ Automatically crop the selected object
✅ Display it in the top-right corner

This can be useful for surveillance, retail analytics, robotics, traffic monitoring, and video analysis. A simple interaction that makes computer vision systems much more intuitive.

u/muhammadrizwanmunr — 9 days ago
▲ 13 r/Ultralytics

Experience Ultralytics YOLO Vision 2026 🌏

The global hybrid vision AI event returns September 13.

25+ industry experts. 20+ live demos. 3,000+ registrations. 70+ countries.

Join the conversations shaping the future of computer vision through technical sessions, product launches, practical case studies, live demonstrations, and real-world insights.

Be part of building an open vision for the real world.

u/muhammadrizwanmunr — 11 days ago
▲ 28 r/Ultralytics

Newsletter bundles counting using Ultralytics YOLO26! 📚

Imagine this: a media team operates a production line for newsletters, sending out batches daily, organized by category, client, or region. Everything looks efficient on the surface. Then someone asks a simple question: "How many bundles did we actually process this week?" Nobody has a clear answer.

Manual tracking: Bundles are counted differently at each stage. The numbers don't align. That's the moment many teams realize: running a newsletter production line isn't just about output: it's about counting it right, at every step.

That's where computer vision comes in: YOLO26 detects and counts bundles directly from the line in real-time, providing full visibility from daily output to client reporting, with no manual reconciliation required.

#newspaper #MachineLearning #Research

u/muhammadrizwanmunr — 13 days ago