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")# 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") - Notebooks
- Google Colab
- Kaggle
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# **GiD-Land-Cover-Classification**
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> **GiD-Land-Cover-Classification** is a multi-class image classification model based on `google/siglip2-base-patch16-224`, trained to detect **land cover types** in geographical or environmental imagery. This model can be used for **urban planning**, **agriculture monitoring**, and **environmental analysis**.
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## **Label Classes**
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# **GiD-Land-Cover-Classification**
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> **GiD-Land-Cover-Classification** is a multi-class image classification model based on `google/siglip2-base-patch16-224`, trained to detect **land cover types** in geographical or environmental imagery. This model can be used for **urban planning**, **agriculture monitoring**, and **environmental analysis**.
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## **Label Classes**
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