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Regression on ZIP Code (test)

55.1High Cholesterol ScorePDFM
-73.652-40.226-6.826.626Oct 15, 2025

Evaluation results

MethodLinks
PDFMInput Modality=Google2025.1055.188.584.785.4929362.186.866.874.882.765.886.292.285.983.482.874.380.687.354.179.366.281.17462.665.769.997.947.872.874——
PDFMModality=Google2025.1055.188.584.785.4929362.186.866.874.882.765.886.292.285.983.482.874.380.687.354.179.366.281.17462.665.769.997.947.872.8——74
UrbanFusionPre-training Dataset=PP2-M2025.1052.982.872.170.583.884.244.278.666.662.166.454.779.983.380.779.772.560.175.478.231.269.556.97875.767.167.769.199.374.386.8——75.1
SatCLIPArchitecture Variant=L40, Pre-training Dataset=S2-100K2025.1052.570.561.250.167.781.944.771.330.462.257.254.371.475.569.361.662.357.368.455.615.859.139.376.475.963.143.159.699.875.787.8——68.8
UrbanFusion2025.1051.480.571.868.982.485.648.672.26965.567.257.674.88177.38072.760.172.479.342.869.662.981.474.860.166.669.299.668.884.274.3——
GAIR2025.1051.279.364.873.779.781.743.375.341.359.459.856.977.280.976.470.865.353.370.459.916.463.741.177.576.164.148.861.599.573.286.4——70.5
SatCLIPArchitecture Variant=L10, Pre-training Dataset=S2-100K2025.1050.5726149.969.981.944.470.735.362.155.554.571.173.266.657.962.65667.160.621.159.241.575.577.164.846.561.199.876.187.9——69.4
GeoCLIPPre-training Dataset=PP2-M2025.1050.28168.776.382.284.742.4794563.863.950.278.68377.873.170.555.768.97518.366.154.977.573.656.857.26499.760.179.9——70
SatCLIPArchitecture Variant=L40, Pre-training Dataset=PP2-M2025.1050.276.366.969.574.281.140.376.944.365.958.351.672.277.973.662.568.956.370.964.517.462.842.373.377.258.447.859.899.87386.4——69.7
SatCLIPArchitecture Variant=L10, Pre-training Dataset=PP2-M2025.1049.77259.466.270.580.746.269.140.363.254.854.271.473.369.859.559.754.268.963.725.560.644.875.776.760.850.461.799.871.185.4——69.2
GeoCLIPPre-training Dataset=MP-162025.1048.481.364.668.582.283.739.175.155.362.960.450.878.178.575.569.566.753.467.97526.364.947.978.372.75956.862.999.859.679.7——69.2
GeoCLIPPre-training Data=PP2-M2025.1046.879.267.176.381.784.740.278.74560.563.250.276.282.376.77168.555.766.675.111.264.650.276.8685457.261.299.751.575.667.1——
GeoCLIPPre-training Data=MP-162025.1046.279.462.868.580.283.7377353.760.858.348.276.977.472.469.264.750.367.973.425.963.345.275.969.955.455.560.499.854.17766.9——
GAIRPre-training Data=PP2-M2025.1037.778.161.270.374.281.529.973.152.656.454.341.674.577.17470.759.945.867.57517.460.647.474.661.548.359.158.299.550.57564.6——
CSPInput Modality=visual2025.102757.456.857.652.577.114.551.430.446.558.249.455.259.447.148.959.246.550.445.715.347.922.917.358.129.724.330.582.452.367.448.6——
CSPModality=visual2025.102757.456.857.652.577.114.551.430.446.558.249.455.259.447.148.959.246.550.445.715.347.922.917.358.129.724.330.582.452.367.4——48.6
SatCLIP_L40Pre-training Data=S2-100K2025.1026.655.345.553.354.2809.951.638.348.540.536.758625351.842.440.552.549.89.445.735.366.547.933.334.943.699.835.467.652.3——
IdentityProbe=MLP, Input Modalities=All available, Type=y ~ g(c)2025.1025.972.666.1827267.8-2.671.653.750.975.234.378.455.163.27859.347.570.151.934.957.550.127.234.257.865.94749.85351.4—52—
SatCLIP_L40Pre-training Data=PP2-M2025.1025.763.450.560.3668028.163.438.548.946.236.567.773.559.955.853.736.955.258.912.151.536.670.647.231.234.24499.835.867.854.4——
GPS2VecInput Modality=tag2025.1025.361.865.768.15683.14.368.8-54.650.454.838.469.276.163.568.158.942.263.664-15241.739.773.270.854.536.95599.540.770.155.6——
GPS2VecModality=tag2025.1025.361.865.768.15683.14.368.8-54.650.454.838.469.276.163.568.158.942.263.664-15241.739.773.270.854.536.95599.540.770.1——55.6
UrbanFusionProbe=MLP, Input Modalities=All available2025.1016.179.257.779.373.4598.372.657.650.372.227.681.560.665.468.963.939.764.770.322.356.752.965.763.860.958.660.444.9-24.310.3—42.5—
CSPInput Modality=iNat2025.1012.243.93523.944.278.215.341.831.435.22835.447.754.44247.83529.539.938.54.636.432.96112.830.629.333.399.81456.942.2——
CSPModality=iNat2025.1012.243.93523.944.278.215.341.831.435.22835.447.754.44247.83529.539.938.54.636.432.96112.830.629.333.399.81456.9——42.2
CSPInput Modality=FMoW2025.10040.635.719.643.375.214.638.629.532.430.11949.753.141.145.433.39.738.435.310.633.132.460.514.330.326.432.899.817.558.641.5——
CSPModality=FMoW2025.10040.635.719.643.375.214.638.629.532.430.11949.753.141.145.433.39.738.435.310.633.132.460.514.330.326.432.899.817.558.6——41.5
SatCLIP_L10Pre-training Data=PP2-M2025.10-1.349.840.749.74578.212.14235.242.533.935.746.860.444.348.838.337.846.243.610.64033.965.242.224.632.239.699.83667.949.2——
SatCLIP_L10Pre-training Data=S2-100K2025.10-1.742.430.940.444.677.210.238.227.327.622.628.446.355.539.244.429.128.431.930.65.933.331.564.714.92631.933.899.830.26544——
IdentityConfig=y ~ g(c)2025.10-5.1-0.51.84.26.354.69.35.80.5-4.58.210.47.620.63.29.9-1.53.1-117.317.20.90.2122.34.94.11.8-6.5-2.43——
IdentityMapping=y ~ g(c)2025.10-5.1-0.51.84.26.354.69.35.80.5-4.58.210.47.620.63.29.9-1.53.1-117.317.20.90.2122.34.94.11.8-6.5-2.4——3
SatCLIP L40Probe=MLP, Input Modalities=All available2025.10-7.37660.47572.3-64.9-1.269.562.343.772.9-5980.753.767.174.65628.164.463.9-82.538.464.965.735.22.157.14522.520.821.6—35—
GAIRProbe=MLP, Input Modalities=All available2025.10-49.47161.881.973.2-54.411.269.565.548.465.9-80.476.654.97073.959.920.256.169.3-80.636.456.669.549-112.464.425.453.2-47.13.1—21.6—
GeoCLIPProbe=MLP, Input Modalities=All available, Variant=PP2-M2025.10-58.152.850.5-110.449.2-17,348.52555.731.642.255.1-62.266.55047.147.7-11.328.4-7.740.3-34-809.144.45351.651.127.345.533.146.339.7—-241.3—
SatCLIP L10Probe=MLP, Input Modalities=All available2025.10-68.768.654.883.866.8-145-4.264.758.5-1.452.9-163.218.640.355.269.341.514.656.971-109.415.564.564.434.5-355.143.149.770.460—39.5—