Image Classification
Transformers
Safetensors
English
siglip
Structures
Desert
Glacier
Street
Ocean
Image-Classifier
art
Mountain
Instructions to use prithivMLmods/Multilabel-GeoSceneNet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prithivMLmods/Multilabel-GeoSceneNet with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="prithivMLmods/Multilabel-GeoSceneNet") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForImageClassification processor = AutoProcessor.from_pretrained("prithivMLmods/Multilabel-GeoSceneNet") model = AutoModelForImageClassification.from_pretrained("prithivMLmods/Multilabel-GeoSceneNet", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download checkpoint-1004/rng_state.pth from prithivMLmods/Multilabel-GeoSceneNet: direct link, hf CLI and curl.
- Browser
- Download file 14.2 kB
-
https://huggingface.co/prithivMLmods/Multilabel-GeoSceneNet/resolve/main/checkpoint-1004/rng_state.pth
- Command line
-
hf download hf://prithivMLmods/Multilabel-GeoSceneNet/checkpoint-1004/rng_state.pth
-
curl -L -o rng_state.pth https://huggingface.co/prithivMLmods/Multilabel-GeoSceneNet/resolve/main/checkpoint-1004/rng_state.pth
14.2 kB
- Xet hash:
- a69eaba85ac6e978818d2a66f1619b8dda6c401cebaea2a1f9390e9b2631e989
- Size of remote file:
- 14.2 kB
- SHA256:
- c40b0aacad198e52b153e48371141823bfad6760d269ffbeb53d4650fe06f051
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