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
File size: 1,118 Bytes
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"architectures": [
"ResNetForImageClassification"
],
"depths": [
3,
4,
6,
3
],
"downsample_in_bottleneck": false,
"downsample_in_first_stage": false,
"embedding_size": 64,
"finetuning_task": "tengeop",
"hidden_act": "relu",
"hidden_size": 2048,
"hidden_sizes": [
256,
512,
1024,
2048
],
"id2label": {
"0": "LABEL_0",
"1": "LABEL_1",
"2": "LABEL_2",
"3": "LABEL_3",
"4": "LABEL_4",
"5": "LABEL_5",
"6": "LABEL_6",
"7": "LABEL_7",
"8": "LABEL_8",
"9": "LABEL_9"
},
"label2id": {
"LABEL_0": 0,
"LABEL_1": 1,
"LABEL_2": 2,
"LABEL_3": 3,
"LABEL_4": 4,
"LABEL_5": 5,
"LABEL_6": 6,
"LABEL_7": 7,
"LABEL_8": 8,
"LABEL_9": 9
},
"layer_type": "bottleneck",
"model_type": "resnet",
"num_channels": 1,
"out_features": [
"stage4"
],
"out_indices": [
4
],
"problem_type": "multi_label_classification",
"stage_names": [
"stem",
"stage1",
"stage2",
"stage3",
"stage4"
],
"torch_dtype": "float32",
"transformers_version": "4.51.1"
}
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