The Prague Texture Segmentation Datagenerator and Benchmark - Result Details
optimized
for

It is possible that the resulting index maps don't correspond to the training set's indeces according to the data.xml file!
It leads to the wrong values of pixel-wise criteria as a result.


Benchmark - Colour [normal] CLresult byhuangyuan's unet (pspnet_ewt)
set \ criterionCSOSUSMENEOCCACOCCIIIEAMSRMCIGCELCEBCEGBCEBGMSCSSCVDLAVINVINMIMARIJCDCFMIWIWIINBDE
#3 - all99.780.000.000.000.0099.9699.950.060.110.1299.8935.110.11-31.743.350.110.440.350.530.4499.7899.5699.560.2299.780.271.5398.470.3099.3299.1099.5599.5599.5599.550.17
#4 - all98.930.000.000.000.0099.7699.810.120.240.2399.7628.550.23-37.148.270.232.121.982.262.1298.9397.8997.891.0798.861.094.8895.011.0697.2996.1098.0198.0197.9898.040.63
#5 - all99.350.000.000.000.0099.8699.870.050.120.1499.8813.720.11-37.2312.160.121.281.141.421.2899.3598.7298.720.6599.140.702.7197.060.5098.6798.0499.0199.0198.9299.100.30
#6 - all98.230.000.000.000.0099.9599.870.070.330.2599.6713.520.13-44.5921.040.203.442.644.303.4998.2396.5496.511.7797.651.585.5193.681.5395.8994.0096.9196.9196.2697.560.88
#7 - all97.590.000.000.000.00100.00100.000.080.130.3199.8713.910.17-43.979.730.194.654.205.124.6797.5995.3895.392.4196.212.076.6592.921.1995.8693.3796.5796.5796.7496.411.01
#8 - all98.960.000.000.000.00100.00100.000.100.220.1899.7814.050.19-42.465.820.202.051.822.292.0698.9697.9597.951.0498.801.063.1896.690.5297.8996.4698.2098.2098.2898.110.36
#9 - all96.2727.120.000.840.00100.00100.000.731.142.5398.8610.791.43-44.1712.101.625.994.687.896.5796.2793.6493.223.3586.582.567.2691.581.7494.0490.6595.0995.1095.6194.592.14
#10 - all97.900.000.000.000.0099.9499.940.120.210.2899.7910.360.23-43.634.550.244.083.404.804.1297.9095.9195.942.1097.491.945.2594.600.8195.9493.0596.4096.4096.8795.930.63
#11 - all98.190.000.000.000.00100.00100.000.210.360.5799.647.900.41-39.436.280.443.543.044.043.5498.1996.4796.481.8197.741.684.3795.410.6796.6994.3197.0797.0797.0997.050.52
#12 - all98.410.000.000.000.00100.00100.000.030.050.0799.956.770.06-44.859.710.063.092.423.803.1298.4196.9096.881.5997.321.453.6495.970.6397.1195.0697.4797.4797.6697.280.45
#6 - bark98.360.000.000.000.0099.9799.940.050.190.0999.8112.360.10-46.2615.300.123.202.763.663.2298.3696.8096.791.6497.831.515.2794.221.1796.5794.7997.3397.3397.2497.410.70
#6 - flowers97.770.000.000.000.0099.9099.730.270.540.6899.4613.100.53-41.9513.090.574.343.734.954.3497.7795.6995.692.2397.131.966.8292.721.3695.8593.6296.7196.7196.7296.700.85
#6 - glass97.010.000.000.000.0099.6899.710.290.451.2699.5515.750.58-40.2814.020.705.745.186.395.8397.0194.2994.232.9996.042.488.6590.712.0693.7090.4895.0095.0094.6695.351.56
#6 - man-made99.060.000.000.000.00100.00100.000.030.050.0699.9519.490.05-35.515.180.051.871.622.121.8899.0698.1398.130.9498.970.983.4396.360.7097.8396.6198.2898.2898.4098.150.45
#6 - nature99.240.000.000.000.00100.00100.000.020.030.0499.9713.500.03-20.8022.250.041.511.291.731.5199.2498.4998.490.7698.780.802.7796.090.7998.3498.0098.9998.9999.0298.960.35
#6 - plants97.070.000.000.000.0099.9999.990.020.040.0499.9616.440.04-35.315.080.045.493.917.215.6397.0794.4194.332.9397.322.167.5092.252.2892.5288.5493.9293.9293.2994.561.42
#6 - rock98.950.000.000.000.0099.8499.810.200.370.5299.6319.520.41-41.299.050.422.091.912.272.0998.9597.9297.921.0598.821.053.6796.140.7797.5596.1398.0398.0398.0698.000.41
#6 - stone96.660.000.002.551.89100.00100.000.030.090.1499.9110.590.06-47.2229.350.092.682.113.422.8698.4697.2097.101.5494.361.154.0294.440.7398.3997.9198.9498.9498.9398.950.69
#6 - textile99.470.000.000.000.0099.9299.900.060.090.1999.9119.800.11-40.2310.980.131.050.981.121.0599.4798.9598.960.5399.270.602.0897.740.3798.8998.2799.1399.1399.1199.140.22
#6 - wood94.646.900.003.950.00100.00100.000.000.000.00100.0013.780.00-39.0619.580.005.364.769.619.0194.6493.4390.854.4183.252.558.8889.642.3893.6190.8395.1995.2392.5697.981.69
mean98.091.700.000.370.0999.9499.930.130.240.3999.7615.450.25-39.8611.840.283.202.703.953.4498.1896.7196.551.7596.571.484.9094.581.0896.6094.7797.2997.2997.1597.440.77
criterionGraph
correct
over
under
missed
noise
F-measure
imageTexture mosaic (undegraded)Ground truthSegmentation
#3 - all
#4 - all
#5 - all
#6 - all
#7 - all
#8 - all
#9 - all
#10 - all
#11 - all
#12 - all
#6 - bark
#6 - flowers
#6 - glass
#6 - man-made
#6 - nature
#6 - plants
#6 - rock
#6 - stone
#6 - textile
#6 - wood