Image Segmentation
Transformers
Safetensors
cond_unet
ultrasound
medical-image-segmentation
attention-unet
custom-pipeline
custom_code
Instructions to use AImageLab-Zip/US_Cond-UNet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AImageLab-Zip/US_Cond-UNet with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="AImageLab-Zip/US_Cond-UNet", trust_remote_code=True)# Load model directly from transformers import AutoModelForImageSegmentation model = AutoModelForImageSegmentation.from_pretrained("AImageLab-Zip/US_Cond-UNet", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "CondUNetForSemanticSegmentation" | |
| ], | |
| "attn_start": 0, | |
| "auto_map": { | |
| "AutoConfig": "configuration_cond_unet.CondUNetConfig", | |
| "AutoImageProcessor": "image_processing_cond_unet.CondUNetImageProcessor", | |
| "AutoModelForImageSegmentation": "modeling_cond_unet.CondUNetForSemanticSegmentation" | |
| }, | |
| "custom_pipelines": { | |
| "image-segmentation": { | |
| "impl": "pipeline.CondUNetImageSegmentationPipeline", | |
| "pt": [ | |
| "AutoModelForImageSegmentation" | |
| ], | |
| "type": "image" | |
| } | |
| }, | |
| "depth": 5, | |
| "dtype": "float32", | |
| "dwt_bands": [ | |
| "LL", | |
| "LH", | |
| "HL", | |
| "HH" | |
| ], | |
| "emb_dim": 768, | |
| "id2label": { | |
| "0": "foreground" | |
| }, | |
| "image_size": 512, | |
| "in_channels": 3, | |
| "keep_aspect_ratio": true, | |
| "label2id": { | |
| "foreground": 0 | |
| }, | |
| "model_type": "cond_unet", | |
| "n_heads": 8, | |
| "n_organs": 10, | |
| "patch_size": 8, | |
| "self_normalize": true, | |
| "shape_res": 32, | |
| "size": 16, | |
| "transformers_version": "5.16.1", | |
| "unknown_organ_id": -1, | |
| "use_attn": true, | |
| "use_dwt": false, | |
| "use_shape": false, | |
| "wavelet": "haar" | |
| } | |