YOLO26 Guide 2026: Ultralytics' Latest Object Detection Model

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Ultralytics released its latest object detection model, YOLO26, in 2026. The GitHub repo (ultralytics/yolo26) provides a quickstart covering detection, instance and semantic segmentation, depth estimation, classification, pose, OBB, and tracking — all in one framework.

The short version: YOLO26 is the latest object detection model from Ultralytics, delivered through the official package. It supports 7 tasks — object detection, instance/semantic segmentation, depth estimation, image classification, pose estimation, oriented object detection (OBB), and tracking — and works in both Python and CLI after pip install ultralytics. Five sizes from nano to xlarge cover edge AI to high-accuracy inference.

This article covers what YOLO26 is, supported tasks, model sizes, installation, usage, and licensing.

What is YOLO26

YOLO26 is an object detection model series by Ultralytics. This GitHub repository (ultralytics/yolo26) is a lightweight discovery page — the canonical implementation, packages, model downloads, issues, and PRs live in ultralytics/ultralytics.

7 tasks in one framework

The biggest feature: 7 tasks in a single package.

• Detection: bounding boxes for objects in images
• Instance Segmentation: pixel-level separation per object
• Semantic Segmentation: pixel classification by category
• Depth Estimation: per-pixel depth
• Classification: image-level category
• Pose Estimation: joint keypoints (e.g., human)
• OBB (Oriented Detection): rotated boxes for tilted objects (satellite/aerial)
• Tracking: follow objects across video frames

Model sizes: 5 tiers

YOLO26 offers nano (n), small (s), medium (m), large (l), xlarge (x) per task.

For detection+segmentation use yolo26n-seg.pt, for detection+depth use yolo26n-depth.pt — a dedicated model per task. Weights auto-download on first use.

Installation

pip install ultralytics

Requirements: Python 3.8+, PyTorch.

Usage

CLI

yolo predict model=yolo26n.pt source="https://ultralytics.com/images/bus.jpg"

Python

from ultralytics import YOLO

# Load a pretrained YOLO26n model
model = YOLO("yolo26n.pt")

# Run inference on the sample image
results = model("https://ultralytics.com/images/bus.jpg")

# Display the annotated results
results[0].show()

That's all it takes to run object detection.

Export & deployment

YOLO26 exports to:

• TensorRT: fast inference on NVIDIA GPUs
• ONNX: general interoperability
• CoreML: Apple devices (iOS, macOS)
• TFLite: mobile & edge devices

License

Ultralytics offers two options:

• AGPL-3.0: OSI-approved open source, great for students, researchers, and enthusiasts
• Ultralytics Enterprise License: for commercial products & services, bypassing AGPL-3.0 requirements

Consider the Enterprise License for commercial use.

Summary

YOLO26 is Ultralytics' all-in-one object detection framework.

• ✅ 7 tasks (detect, segment, depth, classify, pose, OBB, tracking)
• ✅ 5 model sizes (nano to xlarge)
• ✅ Ready with pip install ultralytics
• ✅ Python API + CLI
• ✅ Export to TensorRT, ONNX, CoreML, TFLite
• ✅ Free under AGPL-3.0

For camera-based detection, autonomous driving perception, agricultural measurement, warehouse inventory, or sports analytics, YOLO26 is a notable 2026 model. Try yolo predict model=yolo26n.pt first.

Links

• GitHub: https://github.com/ultralytics/yolo26
• Ultralytics docs: https://docs.ultralytics.com
• Canonical repo: https://github.com/ultralytics/ultralytics
• Ultralytics Platform: https://platform.ultralytics.com/ultralytics/yolo26