Instructions to use rajlab/condensatenet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rajlab/condensatenet with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="rajlab/condensatenet", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("rajlab/condensatenet", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 502 Bytes
cf54e28 6fd38ba | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | {
"architectures": [
"CondensateNet"
],
"dropout_rate": 0.15,
"dtype": "float32",
"encoder_variant": "rw_s",
"model_type": "condensatenet",
"num_classes": 1,
"pyramid_channels": [
24,
48,
64,
160
],
"pyramid_dim": 32,
"spatial_kernel_size": 11,
"transformers_version": "4.57.3",
"use_spatial_attention": true,
"auto_map": {
"AutoConfig": "configuration_condensatenet.CondensateNetConfig",
"AutoModel": "modeling_condensatenet.CondensateNet"
}
} |