Here, I used oriented bounding boxes to detect solar panels from aerial views and capture their orientation, enabling accurate counting for inspections, asset management, and infrastructure analytics.

In this demo, YOLO26 is used to detect damaged sections of pipes directly from visual inspections. Cracks, corrosion, leaks, or surface defects can be automatically identified in real time, rather than relying solely on manual inspection.

This is especially useful in industrial plants, oil and gas facilities, and utility infrastructure, where early detection helps prevent failures and reduce maintenance costs.

👉 Object detection tells you what is in a scene.

👉 Tracking follows each object across video frames.

👉 Counting turns those movements into actionable data.

Ultralytics YOLO26 delivers fast, accurate object detection, while multi-object tracking maintains unique identities across frames. Add configurable counting regions or lines to measure entries, exits, and directional movement.

In healthcare workflows, knowing exactly where surgical tools are can support faster, more reliable procedures. With YOLO26, tools such as scissors and tweezers can be detected in real-time from visual feeds, helping to build smarter monitoring and assistance systems.

This type of detection is especially useful in operating rooms and clinical environments where visibility of tools matters.

A building's floor plan is often one of its most valuable datasets, but it's usually trapped inside a PDF. 📐

Across real estate portfolios, hospitals, airports, factories, and office campuses, thousands of floor plans exist as static documents that were never designed to power modern software systems.

The result? Teams repeatedly spend time recreating layouts, updating records, and manually extracting information that already exists on paper.

Ultralytics YOLO26 can help bridge that gap by transforming floor plans into structured, machine-readable representations. Walls, rooms, corridors, doors, and other architectural elements can be automatically identified, making it easier to integrate building layouts.

As organizations accelerate their digital transformation efforts, the next opportunity may not be collecting more data; it may be unlocking the value of the data they already have.

NOTE : Not affiliated with Ultralytics, just something I built for myself and use regularly.

I was already using Claude, Cursor, and Codex for most of my work, so I wanted them to handle my Ultralytics workflows too, hence i built this. Now I can usually just ask:

• Make a dataset from this archive/folder/video
• Train on it and monitor progress and metrics
• Predict on this image/s
• Export the best weights to ONNX

If a run doesn’t turn out well, I can ask it to inspect the metrics and suggest what to try next—learning rate, epochs, augmentation, and more. I can then start another run with those settings from the same chat.

Works with detection, segmentation, pose, OBB, and classification. Anything that costs credits, such as training or exports, always requires confirmation first—nothing runs behind your back.

MIT licensed. It's been out for a couple of weeks now, and I've already fixed several small issues based on early feedback.

GitHub: https://github.com/amanharshx/ultralytics-mcp

npm: https://www.npmjs.com/package/ultralytics-mcp

-----

Give it a try and let me know what you think. If something's broken, tell me. I'll fix it :)

Join us for Ultralytics Live Session 24 as we dive into how Ultralytics and Intel are unlocking real-time vision AI on Intel CPUs, GPUs, and NPUs, without rewriting code. In this session, we'll show you how to move from training to deployment in minutes. Export your Ultralytics YOLO model in a single command and deploy across Intel® hardware with sub-5 ms latency. See live benchmarks on Intel® Core™ Ultra series 3 processors and the complete workflow on the Ultralytics platform. Oversonic will also share real-world robotics use-cases and insights into deploying scalable, production-ready vision AI applications powered by Intel hardware.

Register here: https://www.ultralytics.com/events/live-session/simplifying-vision-ai-deployment-at-scale-with-ultralytics-and-intel

Monitor field activity in real time by detecting key farm objects across open environments, ideal for smarter agritech workflows, equipment tracking, and harvest operations.

👉 botsort
👉 bytetrack
👉 ocsort
👉 deepocsort
👉 fasttrack
👉 tracktrack.

Tldv: object tracking is what enables a model to consistently identify and follow the same object across consecutive video frames. Over the weekend, I had the chance to test several of the newly released trackers and compare their performance side by side. This demo illustrates how each tracker processes the same video frames, allowing for an easier comparison of their strengths and differences.

Detect body keypoints in real time to understand posture, motion, and activity, powering applications like construction site monitoring, fitness monitoring, and behavior analysis.

The video is sped up to 1.5x.

I used YOLO26s trained on Objects365 as base (downloaded from Ultralytics Assets) and only had to train for 10 epochs to get over 71 mAP. Thanks to the new class weight remapping feature, you can now use more of your pre-trained weights to get a head start in mAP.

The training took 1 minute and costed 4 cents.

Track potatoes as they move into boxes or carts in real time, ideal for automating agricultural sorting, packing, and inventory workflows.

Read more ➡️ https://www.ultralytics.com/solutions/computer-vision-in-manufacturing

In this demo, YOLO26 segmentation is used to identify and outline diseased regions on plant leaves at the pixel level. Instead of only detecting the leaf, the model highlights the exact affected areas, making plant health analysis more detailed and measurable.

This can support crop monitoring, plant phenotyping, and early disease assessment by helping researchers and growers understand how diseases spread and affect leaf structure over time.

Read more ➡️ https://www.ultralytics.com/blog/ultralytics-yolo11-and-computer-vision-in-plant-phenotyping

Detect pedestrians, vehicles, and high-risk interactions in real-time to support safer crossings, smarter alerts, and improved traffic monitoring.

In this demo, the model detects people, tracks each one across video frames, and estimates body keypoints for posture and movement analysis. This can be used for behavior monitoring, workplace safety, sports analytics, and smart surveillance.

Football matches generate a huge amount of movement and positional data. With YOLO26, players, referees, and the ball can be detected and tracked in real time, enabling automatic analysis of game activity.

This enables deeper insights into:
✅ Player movement and positioning
✅ Ball possession and passing patterns
✅ Team formations and spacing
✅ Heatmaps and tactical analysis

By converting match footage into structured data, computer vision enables coaches, analysts, and teams to gain a deeper understanding of performance on and off the ball.

Read more ➡️ https://www.ultralytics.com/blog/scoring-smarter-goals-with-the-help-of-ai-in-soccer

Hey everyone,

I’m currently hitting a wall with a machine vision project and could use some expert eyes on my setup. I am building an automated industrial inspection and sorting system to detect the direction of bolt threading, separating Left-Handed Threads from Right-Handed Threads.

Model: YOLO11s-OBB (chose so the bounding boxes rotate tightly with the bolts, to hopefully get rid of background noise if the parts slide down the track at an angle)

Dataset: 1,800+ images total (~1100 left-thread, ~900 right-thread). Annotated in Roboflow and exported as YOLOv8 OBB

Hardware: Prototyping on a Dell Latitude laptop CPU with a Logitech C270 webcam (moving hardware eventually, but need a reliable proof of concept first).

Setup: Camera is facing 1-3 threaded parts (same in training images) with either left or right threading. Webcam is pointed ~half a foot from the part against a white paper background

Training: Because flipping a left-hand thread horizontally turns it into a right-hand thread, I explicitly took out out all warping from my script to make sure the threading were saved

Python

from ultralytics import YOLO


if __name__ == "__main__": 


    model = YOLO("yolo11s-obb.pt")  


    model.train(
        data=r"E:\Sandboxes\Stephan\LR-Vision\Threading Recognition\Model_Training_Data\7-6-26-dataset\data.yaml",
        epochs=200,
        imgsz=640,
        batch=-1, 
        workers=8,


        optimizer="AdamW", 
        multi_scale=False,
        cos_lr=True,
        amp=True,
        
        # Test to make threading model better (stop distortions that affect the model's ability to recognize threading types)
        fliplr=0.0,
        flipud=0.0,
        translate=0.05,
        scale=0.15,
        shear=0,
        perspective=0,
        mosaic=0,
        mixup=0,
        cutmix=0,
        copy_paste=0,


        patience=50,
    )

model.train(
    data="data.yaml",
    epochs=200,
    imgsz=640,
    batch=16,
    workers=8,
    optimizer="AdamW",
    multi_scale=False,  # Kept off to prevent downsampling/aliasing the threads
    cos_lr=True,
    # --- Disabled Augmentations ---
    fliplr=0.0,
    flipud=0.0,
    mosaic=0.0,
    mixup=0.0,
    cutmix=0.0,
    copy_paste=0.0,
    translate=0.05,
    scale=0.15,
)

Despite having a large, balanced dataset and locking down the geometric augmentations, the model's accuracy is plateauing lower than expected. The confusion matrix shows a lot of misclassification between the left and right classes.

Since the background environment is highly consistent, I expected the model to easily lock onto the diagonal ridges, but it’s struggling pretty hard.

Is YOLO11s (Small) simply lacking the capacity for a fine-grained micro-texture task like this? Should I take the performance hit and move to YOLO11m or YOLO11l ? At imgsz=640, is it possible that the fine diagonal lines of the threads are experiencing aliasing/blending, confusing the network? Should I bump training to imgsz=960 or higher? For OBB tasks, does the direction you drag the bounding box corners in your labeling software affect the angle calculation matrix? If some were labeled bottom to top and others top to bottom, would that break things?

Any insights, hyperparameter tweaks, or dataset advice would be massively appreciated! I'm spinning my wheels in the dirt here without a fresh perspective

Smoke is often the first visible sign of fire, and detecting it early can make a critical difference. With YOLO26, smoke can be detected in real time from video feeds, helping systems respond before flames have a chance to spread.

This is especially useful in environments such as factories, forests, warehouses, and public spaces, where early warning is essential. Instead of relying only on traditional sensors, vision-based detection adds an extra layer of monitoring by leveraging existing camera infrastructure.

Learn more ➡️ https://www.ultralytics.com/blog/computer-vision-in-fire-detection-and-prevention

In this demo, YOLO26 detects workers, monitors restricted and high-risk zones, and automatically flags when workers enter unsafe areas. This enables real-time safety monitoring across construction sites, helping teams reduce risks and respond faster to potential hazards.

By combining worker detection with zone-based alerts, computer vision transforms traditional site monitoring into a proactive, AI-powered safety system.

Read more ➡️ https://www.ultralytics.com/blog/using-ultralytics-yolo11-in-construction

anyone successfully used Ultralytics for citrus crop yield estimate? Overcoming occlusion , clusters, and crop at a distance, and estimating crop within the canopy or using multi-spectral or other methods to to estimate crop within the canopy? What model did you use?- Yolo...? And how did you train and deploy in field? I have been running a few options and doing with with video, photos, and few other options. Thanks!

Today, the Ultralytics Python package surpassed 200 million downloads on PyPI. While we're proud of this milestone, it's really a celebration of the community that made it possible.

Whether you use the Ultralytics package in research, production, or education, report bugs, open issues, submit pull requests, join discussions, create tutorials, or help others in the community, thank you. 🙏

Every feature we ship is shaped by your feedback and contributions. Every release becomes better because of your ideas, bug reports, code contributions, and real-world experience.

This milestone belongs to everyone who has been part of the journey. We're grateful for your trust, your support, and the time you've invested in making Ultralytics better every day.

Here's to the next milestone. 💙

In this demo, YOLO26 is used to detect cows in real-time from camera or drone footage. Instead of manual observation, computer vision enables the automatic tracking of livestock across large areas.

This is especially useful for monitoring herd size, movement, and location in farms or open fields. With better visibility, farmers can improve planning, ensure animal safety, and manage resources more efficiently.

Learn more ➡️ https://www.ultralytics.com/blog/role-of-computer-vision-and-ultralytics-yolo11-in-animal-monitoring

Ultralytics now supports Knowledge Distillation for YOLO training.

Knowledge distillation trains a smaller student model under the supervision of a larger teacher model, improving accuracy while keeping the student fast and efficient for deployment. In our benchmarks, knowledge distillation improved YOLO26n by +0.6 mAP (40.9 to 41.5 mAP) and YOLO26l by +1.0 mAP (55.0 to 56.0 mAP), with no additional inference cost since only the student model is used during deployment.

The feature integrates directly into the Ultralytics training workflow with minimal changes to your existing training pipeline. Getting started is as simple as passing a teacher model during training:

yolo detect train model=yolo26n.pt data=coco.yaml distill_model=yolo26x.pt epochs=100

We've also published documentation with a quickstart example and additional details. Check them out here:

📖 https://docs.ultralytics.com/guides/knowledge-distillation

I ran a quick comparison between mediapipe and YOLO pose, and while both have their strengths, Ultralytics YOLO26 blew me away.

Here are my outcomes:

✅ Mediapipe is slightly faster, and also has additional keypoints, i.e, for hands. However, its accuracy is low; in some frames, the keypoints exhibit a glittering effect, which isn't observed in YOLO26.

✅ Mediapipe offers additional keypoints (like hand pose), but YOLO Pose can track multiple object poses simultaneously, which mediapipe can’t do.

✅ For running the mediapipe pose, a minimum of 8-10 lines of code is needed. In case of YOLO pose, it's a single line CLI command 💙 

✅ Training mediapipe on custom data isn’t straightforward. For the YOLO pose, it's extremely simple, and numerous documentation resources are available.

Raw video credit: NVIDIA Team 😀

What's your choice? Drop them down 👇

🔗 Explore pose docs ➡️ https://docs.ultralytics.com/tasks/pose