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
- Xet hash:
- 802913a29e0d5e3e1d75149edce63ac63ca45cf3bfe2058e0813f55bf93273bd
- Size of remote file:
- 14.2 kB
- SHA256:
- 78591742118927af7f549b0ae9fedb29cdbce11ed41b0b057c67d8d3fcf40bc6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.