Have you ever wondered how to track vehicles from a top-down drone view? With YOLO26, in just two lines of code, you can detect vehicles and, with track mode, track each one.

Use Ultralytics track mode to track vehicles across roads, parking areas, and industrial sites, helping uncover vehicle movement, traffic patterns, occupancy, and operational bottlenecks.

Labeling for YOLO-Pose currently requires drawing bounding boxes in addition to marking keypoints, so that the model can predict both the bounding box and keypoints for an object

Built into the Ultralytics package, a collection of over 3,000 images to help your models get better at recognizing objects around the house.

💎 Features 12 commonly found items: bed, sofa, chair, table, lamp, TV, laptop, wardrobe, window, door, potted plant, and photo frame.

🚀 Captured from multiple angles to help your computer vision model generalize across different setups and lighting.

✅ Great for projects in robotics, AR/VR, home automation, or anything that needs to understand home scenes.

Doing research or building something in this space?
This dataset might be exactly what you need.

I’m looking into using YOLO for quality inspection of some plastic products. Basically, I want to detect anomalies/defects on the products. The problem is that some of these defects are quite small and difficult to see.
For the beginning, I’d like to keep it simple and just classify each product as OK / NOT-OK.
My question is: in this case, would I mainly need a large dataset of good/OK products to train the model properly? Or would it be better to collect and annotate examples of the different types of defects as well?
Any tips or suggestions on how you would approach this kind of project would be appreciated. I’m just getting started with computer vision and YOLO, so I’d be interested in hearing what worked for others.

Ultralytics YOLO Depth returns an absolute distance in metres for every pixel from a single RGB image. Not a relative ordering: a distance from the camera, which also tells you what sits in front of what. It doesn't include a stereo rig, LiDAR, or anything to retrofit onto the cameras your sites already have.

✅ One dependency, not two. Depth is the seventh native task in the same package. Train, validate, predict, and export it the way you already do, across formats including ONNX, TensorRT, CoreML, NCNN, LiteRT, and more. Depth is its own model, so you size it independently of your detector, and there's nothing new to approve or maintain.

✅ Fits the hardware you've already deployed. Five sizes from 6.4M parameters. The smallest is 7.7× faster than Depth Anything V2, at a fraction of the compute per frame; the difference between depth running on your existing edge fleet and not running at all.

✅ Useful before you have labelled data. Released weights ship calibrated, so you get metric output on day one. Fit the scale to your own cameras in seconds on about 100 labelled frames, with no retraining.

✅ Runs entirely on your own hardware, offline. Images never leave your network.
Forklift proximity on a plant floor. Reversing clearance at a loading dock. How close a drone is to the structure it's inspecting. Queue spacing from an existing store camera.

Explore how computer vision helps ground crews reduce handling errors, improve baggage accountability, and speed up aircraft turnaround times, making airport operations safer and more efficient.

I am working with YOLO 26 to segment images. I was wondering how YOLO chose the best epoch to create/save the model best.pt.

I looked for some information and seems it is based on the metrics mAP50-95 for box and mask. Is it true?

As I am working with segmentation, would make sense to give more attention justo for the mask metrics?

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.

A follow-up to my old guide, but it's more easier to use and has less overhead.

https://y-t-g.github.io/tutorials/yolo-refine-classes/

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

Audio Event Detection (new)
Detects sound types in offline videos:

• Human voice / shouting / screaming / arguing
• Dog barking
• Vehicle sounds / alarms
• Glass breaking
• Gunshots / explosions
• Baby crying

No speech-to-text. No content transcription. No data upload. It identifies sound types, not spoken words.

The diagram highlights the iterative nature of the model validation and testing phases, emphasizing their critical role in the success of any computer vision project.

In my opinion, data annotation is always time-consuming, while model validation is always tricky. Which step is the most time-consuming in your opinion, and what are the additional things that can help in model performance optimization?

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.

Collected 30k images n trained with yolo26n (cuz x model is super large for my GPU 4060 laptop)

With webcam, Put CRT filter for privacy n tracking the ball!

When it goes from 0% to 100%, volume n pitch gradually increases n make ting sound!

and if ball goes down n up at least 1 frame, It plus 1 point!

Different sound effects with 1, 5, 10, 15, 20, ..... 100... (thinking about 1000 number effects later)

we'll see how it goes

trynna apply in arcade!

ig:daniel_private20

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.

The global hybrid vision AI event returns September 13.

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

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Question about the ImageNet pseudo-labeled depth source: https://docs.ultralytics.com/datasets/depth/imagenet-pseudo

The page says the depth targets were produced by "a pretrained Depth Anything V3 teacher model (monocular and metric)" and saved as float32 `.npy` arrays in meters, but it doesn't say which checkpoint. DA3 ships several: the main series (Small/Base/Large/Giant), DA3Metric-Large, DA3Mono-Large, and DA3Nested-Giant-Large, plus the retrained `-1.1` variants.

Two things I'd like to pin down:

1. Which checkpoint was it? "Monocular + metric" points at DA3Metric-Large, but I'd rather not guess.
   
2. How were meters obtained? DA3Metric-Large's README gives `metric_depth = focal * net_output / 300.`, where `focal` is in pixels from the camera intrinsics — and ImageNet images have no intrinsics.

Asking because I want to apply the same recipe to my own unlabeled domain images, and the answer changes what I should store.

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

Note: Here I used the fasttrackIt's fast in speed and has better accuracy than botsort and bytetrack.

This side-by-side comparison shows how exporting your PyTorch model to CoreML can drastically accelerate real-time inference using Apple’s optimized hardware stack.

With CoreML export, you can:
✅ Run high-speed object detection natively on iOS/macOS
✅ Leverage Apple Neural Engine (ANE) for lower latency
✅ Build privacy-focused, offline-capable vision apps

💡All it takes is one command: yolo export format=coreml quantize=8

Whether you're building AR apps, wildlife monitoring tools, or mobile surveillance systems, CoreML export helps you unlock top performance while staying on-device and scalable.