ultralytics 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 viamasks_dir(defaultmasks/) or polygon labels; metric is mIoU. - depth (
-depth): targets are scaled uint16 PNG maps (preferred) or floating-point.npymaps in meters; metric is delta1. Exposes a uniquemodel.calibrate(data=...)step that fits a metric-scale correction, thenmodel.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.