Diffusers
TensorBoard
controlnet
stable-diffusion
medical-imaging
retinal-imaging
diabetic-retinopathy
counterfactual
Instructions to use KylianSu/vessel-bezier-retinal-weights with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use KylianSu/vessel-bezier-retinal-weights with Diffusers:
pip install -U diffusers transformers accelerate
from diffusers import ControlNetModel, StableDiffusionControlNetPipeline controlnet = ControlNetModel.from_pretrained("KylianSu/vessel-bezier-retinal-weights") pipe = StableDiffusionControlNetPipeline.from_pretrained( "fill-in-base-model", controlnet=controlnet ) - Notebooks
- Google Colab
- Kaggle
Clarify updated-rerun provenance
Browse files
README.md
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The private dataset repository
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[`KylianSu/btecf-dr-handoff`](https://huggingface.co/datasets/KylianSu/btecf-dr-handoff)
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preserves the recomputed AutoMorph masks, Bézier/CLIP features, fixed splits,
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Track 1/2 outputs, and both
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researcher can restore and checksum all of them with:
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```bash
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bash scripts/15_download_handoff_assets.sh
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```
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The
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## Other artifacts you still need (not in this repo)
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The private dataset repository
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[`KylianSu/btecf-dr-handoff`](https://huggingface.co/datasets/KylianSu/btecf-dr-handoff)
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preserves the recomputed AutoMorph masks, Bézier/CLIP features, fixed splits,
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Track 1/2 outputs, and both updated n=500 reruns. An authorized
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researcher can restore and checksum all of them with:
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```bash
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bash scripts/15_download_handoff_assets.sh
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```
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The current n=500 files are a reproducible post-submission rerun using the
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audited AutoMorph preprocessing. They preserve the fixed-start design and main
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tortuosity conclusion, with small numerical variation from the updated masks.
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They should be cited as the updated rerun rather than as a byte-identical copy
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of the submission snapshot. The archived n=50 files remain the direct evidence
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for the submitted Table 4/5 statistics.
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## Other artifacts you still need (not in this repo)
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