Image Segmentation
Transformers
Safetensors
actu
feature-extraction
climate
geospatial
remote-sensing
spatiotemporal
multi-modal
earth-observation
time-series
hydrology
custom_code
Instructions to use DarthReca/actu-direction-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DarthReca/actu-direction-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="DarthReca/actu-direction-classification", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("DarthReca/actu-direction-classification", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Upload ACTUForImageSegmentation
Browse files- config.json +36 -0
- model.safetensors +3 -0
config.json
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{
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"act_layer": "identity",
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"architectures": [
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"ACTUForImageSegmentation"
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],
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"backbone": "convnextv2_base",
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"batch_first": true,
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"bias": true,
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"bidirectional": false,
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"climate": false,
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"climate_input_dim": 6,
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"climate_seq_len": 5,
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"dem": false,
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"feature_dim": 512,
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"hidden_channels": 64,
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"in_channels": 6,
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"kernel_size": 3,
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"lr": 5e-06,
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"lstm_hidden_dim": 128,
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"model_type": "actu",
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"n_classes": 3,
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"num_lstm_layers": 1,
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"original_resolution": [
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256,
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256
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],
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"padding": 1,
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"skip_connections": true,
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"stride": 1,
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"target_length": 1,
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"task": "segmentation",
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"torch_dtype": "float32",
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"transformers_version": "4.53.2",
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"use_climate_branch": false,
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"use_dem_input": false
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:a3e210955dbff46e240bbdfbb39a7200021ce9774d6753194e6f117fac03ef46
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size 889607564
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