Image Classification
Transformers
Safetensors
resnet
tengeop
SAR
EO
regression
sentinel-1
ocean
wave-height
earth-observation
remote-sensing
satellite-imagery
synthetic-aperture-radar
foundation-model
linear-probing
oceanography
marine-forecasting
open-source
ocean-wind
Instructions to use galeio-research/OceanSAR-1-tengeop with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use galeio-research/OceanSAR-1-tengeop with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="galeio-research/OceanSAR-1-tengeop") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("galeio-research/OceanSAR-1-tengeop") model = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-tengeop") - Notebooks
- Google Colab
- Kaggle

- Xet hash:
- 32c49bfc5c0a53b256648b6f913c57a80832ebac8cbf6d5d64fe9a80c0bed90d
- Size of remote file:
- 1.13 MB
- SHA256:
- 93d8f94e73721585e9fa251a29de5fd5fc9b0ffc903d9aa8890787fac6355323
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.