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, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 867 Bytes
0289342 b3520c0 0289342 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 | {
"act_layer": "identity",
"architectures": [
"ACTUForImageSegmentation"
],
"backbone": "convnextv2_base",
"batch_first": true,
"bias": true,
"bidirectional": false,
"climate": false,
"climate_input_dim": 6,
"climate_seq_len": 5,
"dem": false,
"feature_dim": 512,
"hidden_channels": 64,
"in_channels": 6,
"kernel_size": 3,
"lr": 5e-06,
"lstm_hidden_dim": 128,
"model_type": "actu",
"n_classes": 3,
"num_lstm_layers": 1,
"original_resolution": [
256,
256
],
"padding": 1,
"skip_connections": true,
"stride": 1,
"target_length": 1,
"task": "segmentation",
"torch_dtype": "float32",
"transformers_version": "4.53.2",
"use_climate_branch": false,
"use_dem_input": false,
"auto_map": {
"AutoModel": "modeling_actu.ACTUForImageSegmentation",
"AutoConfig": "modeling_actu.ACTUConfig"
}
}
|