yolo-models · git:20260904.9c3b8ec · 2026-09-04 · sha256 917364ce6527123e
yolo-models git:20260904.9c3b8ecA
Immutable. This exact content is served forever at /api/v1/blob/917364ce6527123e.
---
name: yolo-models
description: >
Use when choosing or comparing Ultralytics models in Platform or code — picking a
model family (YOLO26/YOLO11/YOLOv8, YOLO-World, YOLOE, SAM/SAM2/FastSAM, RT-DETR,
YOLO-NAS), size (n/s/m/l/x), task variant (-seg, -sem, -cls, -pose, -obb, -depth),
pretrained checkpoint, open-vocabulary or promptable detection/segmentation, or custom
architecture. Covers Platform Explore/model flows, weight names and availability,
selection 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. 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](https://platform.ultralytics.com/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](https://docs.ultralytics.com/platform/train/models) and
[Explore](https://docs.ultralytics.com/platform/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:
```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 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:
```bash
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.