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, dtype="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "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" | |
| } | |