Image Segmentation
ultralytics
PyTorch
yolo
yolo11
deep-learning
satellite
rso-detection
custom_code
Eval Results (legacy)
Instructions to use rayh/astro-seg with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use rayh/astro-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("rayh/astro-seg", "<weights>.pt") model = YOLO(weights) source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from rayh/astro-seg: direct link, hf CLI and curl.
- Browser
- Download file 45.2 MB
-
https://huggingface.co/rayh/astro-seg/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://rayh/astro-seg/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/rayh/astro-seg/resolve/main/pytorch_model.bin
45.2 MB
- Xet hash:
- 5879b6125fff70d3f07827bac867a80041003e9893e92eae0c04a60abeeccacb
- Size of remote file:
- 45.2 MB
- SHA256:
- e0b98e151bfac7012bcf6e63e48ab0e9ed36f867ab9c4234ce1fa260f015d5ee
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