Image Segmentation
Transformers
Safetensors
metpredict_dpt
feature-extraction
pathology
dpt
custom_code
Instructions to use RendeiroLab/MetPredict-lung-structure-segmentation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RendeiroLab/MetPredict-lung-structure-segmentation with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="RendeiroLab/MetPredict-lung-structure-segmentation", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("RendeiroLab/MetPredict-lung-structure-segmentation", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 595 Bytes
b2be135 | 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 | {
"activation": null,
"architectures": [
"DPTForSegmentation"
],
"auto_map": {
"AutoConfig": "hf_model.DPTConfig",
"AutoModel": "hf_model.DPTForSegmentation"
},
"backbone": "hf-hub:bioptimus/H-optimus-0",
"class_names": [
"background",
"blood vessel",
"airway"
],
"decoder_fusion_channels": 224,
"decoder_intermediate_channels": [
224,
448,
896,
896
],
"decoder_readout": "cat",
"dtype": "float32",
"encoder_depth": 4,
"in_channels": 3,
"model_type": "metpredict_dpt",
"n_classes": 3,
"transformers_version": "5.3.0"
}
|