Image Segmentation
Transformers
English
Chinese
remote sensing
vision language model
semi-supervised
Instructions to use fluorites/SemiEarth with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fluorites/SemiEarth with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="fluorites/SemiEarth")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("fluorites/SemiEarth", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download SemiEarth_LoveDA_Small.pth from fluorites/SemiEarth: direct link, hf CLI and curl.
- Browser
- Download file 396 MB
-
https://huggingface.co/fluorites/SemiEarth/resolve/main/SemiEarth_LoveDA_Small.pth
- Command line
-
hf download hf://fluorites/SemiEarth/SemiEarth_LoveDA_Small.pth
-
curl -L -o SemiEarth_LoveDA_Small.pth https://huggingface.co/fluorites/SemiEarth/resolve/main/SemiEarth_LoveDA_Small.pth
396 MB
- Xet hash:
- 6a5b05c4c78ede2312ab2172fa52f8321964aea30e2a722a2c30bb36b2535077
- Size of remote file:
- 396 MB
- SHA256:
- be9c770cef76175dd4b166a84078bbe67c2f3b8ea8559d50ff69a1a2bf8e1d6e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.