yolo-models · git:20260811.47343d9 · 2026-08-11 · sha256 28eaedb76c9c00fb
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---
name: yolo-models
description: >
Use when choosing or comparing Ultralytics models — picking a model family
(YOLO26/YOLO11/YOLOv8, YOLO-World, YOLOE, SAM/SAM2/FastSAM, RT-DETR, YOLO-NAS), a size
(n/s/m/l/x), or a task variant (-seg, -sem, -cls, -pose, -obb, -depth), doing
open-vocabulary or promptable detection/segmentation, or customizing architecture
YAMLs. Covers exact weight names, selection decision guidance, and family trade-offs.
---
# 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. Weights auto-download on
first use.
## 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`): labels are float32 `.npy` depth maps; 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:
```python
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 when you need the exact downloadable `.pt`
name for any family/size/task combination. Do not guess weight names.
## Verify against the installed version
Model availability moves fast. The installed version's authoritative list:
```bash
python -c "from ultralytics.utils.downloads import GITHUB_ASSETS_NAMES; print(*sorted(GITHUB_ASSETS_NAMES), sep='\\n')"
```
If a weight name 404s or a class import fails, check `yolo checks` (version) and prefer
what the error message offers over these tables.