Image Classification
Transformers
Safetensors
English
siglip
Gaofen-Image-Dataset
Land-Cover-Classification
Remote-Sensing-Images
Instructions to use prithivMLmods/GiD-Land-Cover-Classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prithivMLmods/GiD-Land-Cover-Classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="prithivMLmods/GiD-Land-Cover-Classification") 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/GiD-Land-Cover-Classification") model = AutoModelForImageClassification.from_pretrained("prithivMLmods/GiD-Land-Cover-Classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download checkpoint-938/optimizer.pt from prithivMLmods/GiD-Land-Cover-Classification: direct link, hf CLI and curl.
- Browser
- Download file 687 MB
-
https://huggingface.co/prithivMLmods/GiD-Land-Cover-Classification/resolve/main/checkpoint-938/optimizer.pt
- Command line
-
hf download hf://prithivMLmods/GiD-Land-Cover-Classification/checkpoint-938/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/prithivMLmods/GiD-Land-Cover-Classification/resolve/main/checkpoint-938/optimizer.pt
687 MB
- Xet hash:
- 0b994db92ddac9b12703aeeb37628cd68bfa003c5a2fa0de6b10d70b6b4b0d2e
- Size of remote file:
- 687 MB
- SHA256:
- eaf3b034b9edc40305d3066dabc34999ecd8cdbd5081792f5fe55bad69299f42
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