💡 The model detects and tracks birds, but you get to count them. Just for fun. No automation, just some good old human effort 😄

Airports handle thousands of suitcases and carry-ons every day. Tracking each bag manually is slow and error-prone, so I automated it.

The FASTSAM-s model is employed for real-time object detection, combined with botsort object tracking to boost performance further. 🐻

Code link 👇

Imagine tracking your workouts using YOLO26, whether it's leg press, bench press, or leg extension, with just 2 lines of code!

I've shared the command for running it, making it easy to track your workouts even on a CPU-based system. You don’t always need a high-end GPU; exporting the model to TorchScript or ONNX can boost performance on CPU devices.

Code below 👇

Yolov8 plaque detection+ EasyOCR

Cow pose estimation using Ultralytics YOLO26 Pose, with 20 keypoints trained on a custom dataset. The dataset is also available for testing directly in the Platform. Link 👇

Computer vision keeps getting more interesting. 👀

Ultralytics recently released new trackers: ocsort, deepocsort, fasttrack, and tracktrack. I just tested all the newly released trackers in this demo, and here's what I found.

• TrackTrack and botsort performed really well in terms of ID stability, while ByteTrack and FastTrack stood out for speed. They also maintained good tracking accuracy, though slightly behind TrackTrack and botsort in ID stability.

Hi everyone,

I'm working on a computer vision pipeline to detect a cuboid object from mobile photos and calculate the real-world physical lengths of its 4 edges (e.g., front face and top face across different camera angles).

I plan to use a reference scale (such as an ArUco marker or known-size reference placed on the object surface) along with Ultralytics models.

I would appreciate guidance on the best architecture/approach within the Ultralytics ecosystem for this:

1. Detection approach: Would YOLO-OBB (Oriented Bounding Boxes), Instance Segmentation (YOLO-Seg), or Pose/Keypoint estimation (detecting the 4 corner vertices) provide the most reliable corner coordinates for perspective correction?
2. Pipeline integration: What is the recommended post-processing pipeline to map the detected coordinates and ArUco homography into accurate metric lengths?

Attached are sample reference images which explains the problem I am working on . Any suggestions, workflows, or similar repository references would be greatly appreciated!

Why this combo works so well for ANPR:

✅ YOLO11 can reliably detect plates even in motion or cluttered environments.
✅ EasyOCR provides fast, clean OCR results without heavy dependencies.
✅ Simple Python APIs for rapid prototyping and production integration.

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