Feature Extraction
Transformers
Safetensors
jolia
medical
radiology
ct
3d
vision
foundation-model
self-supervised
custom_code
Instructions to use raidium/Jolia with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use raidium/Jolia with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="raidium/Jolia", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("raidium/Jolia", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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@@ -90,7 +90,7 @@ image = pre(volume, resolution=(0.7, 0.7, 1.0)).unsqueeze(0) # (1, 11, 192, 19
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| Backbone | `MultiModalAtlas` — multi-scale 3D ViT, `dim=192`, heads `6`, stages `[2, 2, 8]` |
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| Patch embed | `6×6×6`, 11 input channels (CT windowing), `merge_ratio = 4³` |
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| Global embedding | 576-d |
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| Organ queries |
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| Parameters | ~22 M (89 MB `safetensors`) |
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| Input | `(B, 11, 192, 192, 192)` float32 |
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| Training data | INSPECT, CT-RATE, Stanford-Abdominal-CT (chest + abdomen CT) |
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| Backbone | `MultiModalAtlas` — multi-scale 3D ViT, `dim=192`, heads `6`, stages `[2, 2, 8]` |
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| Patch embed | `6×6×6`, 11 input channels (CT windowing), `merge_ratio = 4³` |
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| Global embedding | 576-d |
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| Organ queries | 102 slots × 192-d × 3 scales → 576-d |
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| Parameters | ~22 M (89 MB `safetensors`) |
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| Input | `(B, 11, 192, 192, 192)` float32 |
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| Training data | INSPECT, CT-RATE, Stanford-Abdominal-CT (chest + abdomen CT) |
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