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
| config,mean,std,n,q25,q75,sem,ci95_lo,ci95_hi | |
| radius_x0.55,0.0014635593933074714,0.0028608083015178843,17,5.6023814977379516e-05,0.0010496774921193719,0.0006938479295177702,0.00010361745145264184,0.0028235013351623007 | |
| radius_x0.70,0.00232127017710916,0.0032082752206568192,17,0.00017406286497134715,0.0024784423876553774,0.0007781210359305808,0.0007961529466852218,0.003846387407533098 | |
| radius_x0.85,0.0029869318014414817,0.003839195916233907,17,0.0002126143081113696,0.004294764716178179,0.0009311417811807277,0.0011618939103272556,0.004811969692555708 | |
| pixdrop_20,0.003988645696474979,0.006046582822366006,17,0.00011753112630685791,0.005625469610095024,0.0014665117441564936,0.0011142826779282516,0.006863008715021707 | |
| pixdrop_30,0.004228562804254419,0.009189371274172034,17,7.177171210059896e-05,0.0030077097471803427,0.0022287499056722376,-0.00013978701086316686,0.008596912619372005 | |
| pixdrop_10,0.005797578684210106,0.015519321942159202,17,7.703826850047335e-05,0.0011491357581689954,0.003763988447381659,-0.001579838672657945,0.013174996041078158 | |
| tortuosity_1x,0.009198961978066056,0.01102805646933101,17,0.0023838661145418882,0.009441995993256569,0.002674696568725172,0.003956556703364719,0.014441367252767392 | |
| arc_drop_30,0.01178608558856307,0.025788423393278487,17,0.0006594053120352328,0.011429793201386929,0.006254611386390389,-0.00047295272876209105,0.02404512390588823 | |
| tortuosity_2x,0.013557586742535932,0.03720402133202491,17,0.0002839461085386574,0.005630641244351864,0.009023300567627728,-0.004128082370014413,0.031243255855086277 | |
| arc_drop_20,0.05579912378315759,0.16745153071018,17,0.0012334289494901896,0.03148302808403969,0.040612961664084214,-0.02380228107844746,0.13540052864476265 | |
| arc_drop_10,0.0816259546443368,0.19842854822348588,17,0.002797244116663933,0.02769959345459938,0.04812599196843528,-0.012700989613796343,0.17595289890246996 | |
| baseline,0.09705150105497416,0.13042207579209414,17,0.006241537164896727,0.088959701359272,0.03163199967077154,0.03505278170026194,0.1590502204096864 | |
| tortuosity_4x,0.43666406940011415,0.3751130354688387,17,0.09765235334634781,0.8268421292304993,0.09097827451671092,0.2583466513473608,0.6149814874528675 | |