Choosing an Ultralytics model

Default recommendation: YOLO26, pretrained. Latest generation, NMS-free end-to-end (fastest CPU inference, simplest deployment). Use YOLO11/YOLOv8 only to match an existing codebase or a deployment target that doesn't support YOLO26 yet. Most official weights auto-download on first use; sam3.pt requires manual access and download.

Choose in Platform

For the quickest no-code start, open Platform Explore, select Projects, clone the official @ultralytics project for the model family, then train one of its pretrained models on your dataset. The New Model dialog filters base models to the selected dataset task and offers official models plus your own completed checkpoints for further fine-tuning.

Use a Platform model page to inspect metrics, test it in Predict, export it, deploy it, clone it into another project, or download its .pt weights for the Python/CLI workflows below. See Platform Models and Explore.

Model = family + size + task suffix

yolo26 + n/s/m/l/x + task suffix → yolo26s-seg.pt

Size	COCO mAP50-95	Params	T4 TensorRT	Pick for
n	40.9	2.4M	~1.7 ms	edge/mobile, CPU realtime, first prototype
s	48.6	9.5M	~2.5 ms	balanced default for most projects
m	53.1	20.4M	~4.7 ms	GPU server, accuracy matters
l	55.0	24.8M	~6.2 ms	accuracy-critical, ample GPU
x	57.5	55.7M	~11.8 ms	max accuracy, offline/batch

Strategy: prototype on n to validate the pipeline cheaply, then scale up until accuracy stops paying for the latency. A bigger model never fixes bad labels.

Suffix	Task	Output
(none)	detect	boxes
-seg	instance segmentation	polygons + boxes
-sem	semantic segmentation (YOLO26+)	per-pixel class mask
-depth	monocular depth (YOLO26+)	depth map
-cls	classification	class probabilities
-pose	pose/keypoints	keypoints + boxes
-obb	oriented boxes	rotated boxes

Notes on the newer tasks:

• semantic (-sem): dataset uses PNG masks via masks_dir (default masks/) or polygon labels; metric is mIoU.
• depth (-depth): targets are scaled uint16 PNG maps (preferred) or floating-point .npy maps in meters; metric is delta1. Exposes a unique model.calibrate(data=...) step that fits a metric-scale correction, then model.save(...) to persist it.

Family cheat sheet

Family	Class	When
YOLO26 / YOLO11 / YOLO12 / YOLOv8–v10	YOLO("yolo26n.pt")	standard closed-set tasks; default choice
YOLO-World	YOLOWorld("yolov8s-world.pt")	zero-shot detection of arbitrary text classes; model.set_classes(["person", "helmet"])
YOLOE	YOLOE("yoloe-26s-seg.pt")	open-vocabulary detect+segment via text or visual prompts; set_classes(names, embeddings), visual prompts via predict(..., visual_prompts={"bboxes": ..., "cls": ...}); -pf variants are prompt-free
SAM / SAM2 / SAM3 / MobileSAM	SAM("sam_b.pt")	promptable segmentation: predict(source, bboxes=... / points=... / labels=...); SAM2/3 add video and semantic variants
FastSAM	FastSAM("FastSAM-s.pt")	CNN-based segment-anything, much faster than SAM
RT-DETR	RTDETR("rtdetr-l.pt")	transformer detector, strong accuracy on GPU
YOLO-NAS	NAS("yolo_nas_s.pt")	inference/val only, no training

All classes share the same Model API (train/val/predict/track/export/...) — everything in the other yolo-* skills applies to them, with the exceptions noted above.

Open-vocabulary decision: need arbitrary classes at inference with no training → YOLO-World (detect) or YOLOE (detect+segment, also visual prompts). Need pixel-precise masks from clicks/boxes → SAM family. Need a trained model for a fixed class list → plain YOLO26 fine-tune (faster and more accurate on that closed set).

Architecture YAMLs (custom models)

ultralytics/cfg/models/ ships editable architecture definitions (yolo26.yaml, yolo11.yaml, yolov8.yaml, scale variants -p2 for small objects, -p6 for large imgsz, -ghost, etc.). Loading YOLO("yolo26n.yaml") builds from scratch — scale is picked from the letter in the stem. To customize the architecture but keep pretrained weights where layers match:

model = YOLO("yolo26n.yaml").load("yolo26n.pt")  # transfer matching weights

Only go here for research/unusual constraints; for normal work fine-tune the stock .pt.

Related pages

• weights-catalog.md (this folder) — read for package-known weight patterns and specialized official assets. Do not guess weight names.

Verify against the installed version

Model availability moves fast. This prints the installed package's known fast-path set; read weights-catalog.md before treating an unlisted official asset as invalid:

python -c "from ultralytics.utils.downloads import GITHUB_ASSETS_NAMES; print(*sorted(GITHUB_ASSETS_NAMES), sep='\\n')"

If a weight 404s or a class import fails, check yolo checks and trust the installed-version error.