Feature Extraction
Transformers
PyTorch
Safetensors
s2l8hModel
agriculture
remote sensing
earth observation
landsat
sentinel-2
custom_code
Instructions to use venkatesh-thiru/s2l8h-UNet-6depth-upsample with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use venkatesh-thiru/s2l8h-UNet-6depth-upsample with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="venkatesh-thiru/s2l8h-UNet-6depth-upsample", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("venkatesh-thiru/s2l8h-UNet-6depth-upsample", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e88bdbbf61bbaae3ef428093f858947a1a856536cecbae80e40ef1012c687f79
- Size of remote file:
- 464 MB
- SHA256:
- 33e653a774bf138b91e453ecd09a52cea593a036ae3c7e967f51bb707a59b2a8
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