Instructions to use prithivMLmods/SAT-Landforms-Classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prithivMLmods/SAT-Landforms-Classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="prithivMLmods/SAT-Landforms-Classifier") 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/SAT-Landforms-Classifier") model = AutoModelForImageClassification.from_pretrained("prithivMLmods/SAT-Landforms-Classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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license: apache-2.0
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# **SAT-Landforms-Classifier**
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- **Environmental Studies:** Supporting researchers in tracking changes in vegetation and water bodies.
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- **Urban Planning:** Assisting planners in analyzing residential, industrial, and infrastructure distributions.
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- **Agricultural Analysis:** Helping assess crop distribution and pastureland areas.
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- **Disaster Management:** Providing insights into land coverage for emergency response and recovery planning.
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license: apache-2.0
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language:
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- en
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base_model:
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- google/siglip2-base-patch16-224
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pipeline_tag: image-classification
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library_name: transformers
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tags:
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- siglip2
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- SAT
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- Landforms
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---
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# **SAT-Landforms-Classifier**
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- **Environmental Studies:** Supporting researchers in tracking changes in vegetation and water bodies.
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- **Urban Planning:** Assisting planners in analyzing residential, industrial, and infrastructure distributions.
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- **Agricultural Analysis:** Helping assess crop distribution and pastureland areas.
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- **Disaster Management:** Providing insights into land coverage for emergency response and recovery planning.
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