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
File size: 1,099 Bytes
8966f37 badc3e1 8966f37 badc3e1 8966f37 | 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 42 43 44 45 46 47 48 49 50 51 52 | {
"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"
}
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