Instructions to use DeveloperDobby/yolo26n-seg with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use DeveloperDobby/yolo26n-seg with ultralytics:
from huggingface_hub import hf_hub_download from ultralytics import YOLO # pick the weights file from this repo's "Files and versions" tab weights = hf_hub_download("DeveloperDobby/yolo26n-seg", "<weights>.pt") model = YOLO(weights) source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
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Download README.md from DeveloperDobby/yolo26n-seg: direct link, hf CLI and curl.
- Browser
- Download file 953 Bytes
-
https://huggingface.co/DeveloperDobby/yolo26n-seg/resolve/main/README.md
- Command line
-
hf download hf://DeveloperDobby/yolo26n-seg/README.md
-
curl -L -o README.md https://huggingface.co/DeveloperDobby/yolo26n-seg/resolve/main/README.md
953 Bytes
metadata
license: agpl-3.0
library_name: ultralytics
pipeline_tag: image-segmentation
tags:
- yolo
- yolo26
- onnx
- instance-segmentation
- coco
yolo26n-seg (ONNX)
The official Ultralytics YOLO26n-seg COCO weights (yolo26n-seg.pt), exported unchanged to ONNX so they can run in the
browser with onnxruntime-web. Not fine-tuned.
| Input | images — float32 [1, 3, 640, 640], RGB 0–1, letterboxed (pad 114) |
| Output 0 | [1, 116, 8400] = box cx, cy, w, h + 80 class scores + 32 mask coefficients. NMS is not included |
| Output 1 | [1, 32, 160, 160] mask prototypes — mask = sigmoid(coefficients · prototypes), cropped to the box |
| Size | ONNX 11.2 MB (opset 17, simplified) · source .pt 6.7 MB |
| Classes | COCO 80 |
Used by the model-card site AI Models Organize (YOLO26n-seg card → «돌려보기»).
License
AGPL-3.0, inherited from Ultralytics YOLO.