====================================================================== TRAINING SET EVALUATION RESULTS ====================================================================== Checkpoint: C:/Users/artem.chsherbakov/rgb_reconstruction/MST-plus-plus/train_code/exp/mst_plus_plus_apples_hsi/2025_11_19_18_53_05/net_76epoch.pth Training samples evaluated: 221 Individual Sample Results: ---------------------------------------------------------------------- Sample 1: MRAE=0.068635, RMSE=0.041800, PSNR=9.084662 Sample 2: MRAE=0.121362, RMSE=0.072447, PSNR=8.243711 Sample 3: MRAE=0.061621, RMSE=0.031029, PSNR=7.463352 Sample 4: MRAE=0.065211, RMSE=0.038742, PSNR=7.696421 Sample 5: MRAE=0.079514, RMSE=0.058748, PSNR=10.206064 Sample 6: MRAE=0.049597, RMSE=0.028550, PSNR=9.190248 Sample 7: MRAE=0.052692, RMSE=0.026305, PSNR=10.174209 Sample 8: MRAE=0.046381, RMSE=0.022661, PSNR=9.524340 Sample 9: MRAE=0.060569, RMSE=0.037615, PSNR=8.615671 Sample 10: MRAE=0.239529, RMSE=0.186710, PSNR=6.821447 Sample 11: MRAE=0.087834, RMSE=0.032994, PSNR=7.885696 Sample 12: MRAE=0.066965, RMSE=0.046494, PSNR=8.505877 Sample 13: MRAE=0.073310, RMSE=0.048648, PSNR=8.508020 Sample 14: MRAE=0.059789, RMSE=0.034404, PSNR=8.759315 Sample 15: MRAE=0.099571, RMSE=0.056582, PSNR=8.376325 Sample 16: MRAE=0.118479, RMSE=0.062431, PSNR=8.017539 Sample 17: MRAE=0.062342, RMSE=0.027277, PSNR=7.561791 Sample 18: MRAE=0.080217, RMSE=0.034917, PSNR=8.989779 Sample 19: MRAE=0.085246, RMSE=0.037012, PSNR=10.283434 Sample 20: MRAE=0.091772, RMSE=0.042196, PSNR=10.389731 Sample 21: MRAE=0.044669, RMSE=0.025053, PSNR=9.283949 Sample 22: MRAE=0.057772, RMSE=0.035155, PSNR=8.593445 Sample 23: MRAE=0.132950, RMSE=0.094638, PSNR=9.804320 Sample 24: MRAE=0.040570, RMSE=0.029439, PSNR=7.960147 Sample 25: MRAE=0.088582, RMSE=0.086131, PSNR=8.394528 Sample 26: MRAE=0.133534, RMSE=0.098513, PSNR=8.796194 Sample 27: MRAE=0.069610, RMSE=0.057096, PSNR=8.500711 Sample 28: MRAE=0.060285, RMSE=0.049881, PSNR=7.906453 Sample 29: MRAE=0.108824, RMSE=0.091936, PSNR=8.368457 Sample 30: MRAE=0.061371, RMSE=0.033407, PSNR=6.845534 Sample 31: MRAE=0.049890, RMSE=0.027138, PSNR=8.261063 Sample 32: MRAE=0.046444, RMSE=0.024560, PSNR=8.821125 Sample 33: MRAE=0.126380, RMSE=0.036647, PSNR=8.159251 Sample 34: MRAE=0.076347, RMSE=0.036602, PSNR=10.320315 Sample 35: MRAE=0.047695, RMSE=0.026048, PSNR=10.537378 Sample 36: MRAE=0.089611, RMSE=0.047540, PSNR=8.642241 Sample 37: MRAE=0.058080, RMSE=0.031372, PSNR=8.618449 Sample 38: MRAE=0.082725, RMSE=0.071358, PSNR=8.174376 Sample 39: MRAE=0.082939, RMSE=0.082494, PSNR=8.590860 Sample 40: MRAE=0.142012, RMSE=0.086248, PSNR=8.404302 Sample 41: MRAE=0.061869, RMSE=0.044915, PSNR=8.447906 Sample 42: MRAE=0.064955, RMSE=0.060249, PSNR=8.051315 Sample 43: MRAE=0.078096, RMSE=0.074678, PSNR=9.091430 Sample 44: MRAE=0.052160, RMSE=0.034521, PSNR=8.059448 Sample 45: MRAE=0.091208, RMSE=0.042334, PSNR=9.478468 Sample 46: MRAE=0.035830, RMSE=0.027519, PSNR=9.217566 Sample 47: MRAE=0.059283, RMSE=0.037603, PSNR=10.601501 Sample 48: MRAE=0.101051, RMSE=0.039207, PSNR=10.022116 Sample 49: MRAE=0.051586, RMSE=0.025018, PSNR=8.428059 Sample 50: MRAE=0.075678, RMSE=0.062679, PSNR=8.263218 Sample 51: MRAE=0.153950, RMSE=0.093294, PSNR=8.515207 Sample 52: MRAE=0.054225, RMSE=0.050261, PSNR=8.325880 Sample 53: MRAE=0.077540, RMSE=0.053528, PSNR=8.986863 Sample 54: MRAE=0.085132, RMSE=0.061006, PSNR=8.479319 Sample 55: MRAE=0.109561, RMSE=0.063662, PSNR=8.015192 Sample 56: MRAE=0.147855, RMSE=0.090810, PSNR=7.758502 Sample 57: MRAE=0.085158, RMSE=0.028365, PSNR=8.022969 Sample 58: MRAE=0.081896, RMSE=0.043465, PSNR=8.115351 Sample 59: MRAE=0.065488, RMSE=0.038021, PSNR=9.670060 Sample 60: MRAE=0.043653, RMSE=0.022341, PSNR=10.141331 Sample 61: MRAE=0.053536, RMSE=0.036222, PSNR=9.267990 Sample 62: MRAE=0.082081, RMSE=0.042619, PSNR=8.992334 Sample 63: MRAE=0.044585, RMSE=0.050736, PSNR=9.397146 Sample 64: MRAE=0.047529, RMSE=0.032211, PSNR=7.987847 Sample 65: MRAE=0.106080, RMSE=0.087490, PSNR=8.626697 Sample 66: MRAE=0.065254, RMSE=0.041333, PSNR=8.428360 Sample 67: MRAE=0.044655, RMSE=0.026840, PSNR=8.674053 Sample 68: MRAE=0.070217, RMSE=0.055421, PSNR=8.266386 Sample 69: MRAE=0.065570, RMSE=0.064391, PSNR=7.895462 Sample 70: MRAE=0.060580, RMSE=0.041997, PSNR=7.581284 Sample 71: MRAE=0.128543, RMSE=0.096189, PSNR=8.612600 Sample 72: MRAE=0.126239, RMSE=0.107091, PSNR=9.444067 Sample 73: MRAE=0.045394, RMSE=0.026557, PSNR=8.787673 Sample 74: MRAE=0.060664, RMSE=0.031359, PSNR=10.629897 Sample 75: MRAE=0.060246, RMSE=0.042562, PSNR=9.388870 Sample 76: MRAE=0.053589, RMSE=0.029438, PSNR=8.337927 Sample 77: MRAE=0.066286, RMSE=0.050352, PSNR=8.126797 Sample 78: MRAE=0.113861, RMSE=0.077195, PSNR=8.062019 Sample 79: MRAE=0.096797, RMSE=0.058773, PSNR=8.430447 Sample 80: MRAE=0.071689, RMSE=0.056403, PSNR=8.825507 Sample 81: MRAE=0.098015, RMSE=0.065356, PSNR=8.209462 Sample 82: MRAE=0.055034, RMSE=0.042495, PSNR=7.818182 Sample 83: MRAE=0.129059, RMSE=0.062421, PSNR=8.002535 Sample 84: MRAE=0.042342, RMSE=0.032869, PSNR=8.055195 Sample 85: MRAE=0.065267, RMSE=0.036710, PSNR=8.735329 Sample 86: MRAE=0.108006, RMSE=0.049287, PSNR=9.680676 Sample 87: MRAE=0.074866, RMSE=0.040745, PSNR=9.888895 Sample 88: MRAE=0.117889, RMSE=0.057119, PSNR=8.821930 Sample 89: MRAE=0.069540, RMSE=0.062799, PSNR=7.857996 Sample 90: MRAE=0.082642, RMSE=0.043778, PSNR=7.553163 Sample 91: MRAE=0.083503, RMSE=0.063355, PSNR=8.410181 Sample 92: MRAE=0.084744, RMSE=0.050086, PSNR=7.412497 Sample 93: MRAE=0.106194, RMSE=0.053162, PSNR=8.916862 Sample 94: MRAE=0.058412, RMSE=0.032646, PSNR=9.139701 Sample 95: MRAE=0.059337, RMSE=0.033803, PSNR=8.087349 Sample 96: MRAE=0.069189, RMSE=0.060759, PSNR=7.840010 Sample 97: MRAE=0.069685, RMSE=0.036514, PSNR=7.546544 Sample 98: MRAE=0.062518, RMSE=0.037854, PSNR=8.853647 Sample 99: MRAE=0.048342, RMSE=0.026323, PSNR=9.432217 Sample 100: MRAE=0.134962, RMSE=0.064677, PSNR=9.795382 Sample 101: MRAE=0.131941, RMSE=0.063682, PSNR=7.365636 Sample 102: MRAE=0.056841, RMSE=0.035150, PSNR=8.414966 Sample 103: MRAE=0.042774, RMSE=0.027811, PSNR=8.937756 Sample 104: MRAE=0.055073, RMSE=0.038852, PSNR=9.687836 Sample 105: MRAE=0.070497, RMSE=0.056915, PSNR=8.087305 Sample 106: MRAE=0.060777, RMSE=0.039713, PSNR=8.592096 Sample 107: MRAE=0.076942, RMSE=0.048972, PSNR=8.252454 Sample 108: MRAE=0.068424, RMSE=0.044168, PSNR=8.055513 Sample 109: MRAE=0.102127, RMSE=0.073870, PSNR=8.415294 Sample 110: MRAE=0.181484, RMSE=0.079599, PSNR=9.918492 Sample 111: MRAE=0.139160, RMSE=0.061475, PSNR=8.966675 Sample 112: MRAE=0.112147, RMSE=0.045596, PSNR=6.474732 Sample 113: MRAE=0.087092, RMSE=0.045082, PSNR=7.465812 Sample 114: MRAE=0.072694, RMSE=0.041369, PSNR=7.702407 Sample 115: MRAE=0.048456, RMSE=0.036553, PSNR=9.612191 Sample 116: MRAE=0.046988, RMSE=0.031483, PSNR=8.368271 Sample 117: MRAE=0.277933, RMSE=0.149831, PSNR=6.971536 Sample 118: MRAE=0.074909, RMSE=0.032105, PSNR=7.987525 Sample 119: MRAE=0.084794, RMSE=0.058692, PSNR=8.133017 Sample 120: MRAE=0.146285, RMSE=0.115547, PSNR=7.607082 Sample 121: MRAE=0.092391, RMSE=0.069250, PSNR=7.902336 Sample 122: MRAE=0.056124, RMSE=0.032314, PSNR=8.193790 Sample 123: MRAE=0.089287, RMSE=0.052465, PSNR=7.249237 Sample 124: MRAE=0.052276, RMSE=0.032682, PSNR=8.364600 Sample 125: MRAE=0.052312, RMSE=0.037844, PSNR=8.879766 Sample 126: MRAE=0.152538, RMSE=0.069129, PSNR=9.597974 Sample 127: MRAE=0.114974, RMSE=0.055046, PSNR=10.081017 Sample 128: MRAE=0.045042, RMSE=0.033000, PSNR=9.323060 Sample 129: MRAE=0.066576, RMSE=0.043583, PSNR=7.977243 Sample 130: MRAE=0.135547, RMSE=0.066868, PSNR=7.283079 Sample 131: MRAE=0.124384, RMSE=0.038548, PSNR=7.262955 Sample 132: MRAE=0.118299, RMSE=0.075097, PSNR=8.137776 Sample 133: MRAE=0.094715, RMSE=0.055418, PSNR=7.975596 Sample 134: MRAE=0.099835, RMSE=0.060348, PSNR=8.007667 Sample 135: MRAE=0.117094, RMSE=0.066784, PSNR=8.242079 Sample 136: MRAE=0.106247, RMSE=0.038640, PSNR=7.742271 Sample 137: MRAE=0.103522, RMSE=0.050957, PSNR=8.494853 Sample 138: MRAE=0.088326, RMSE=0.041276, PSNR=8.378450 Sample 139: MRAE=0.157911, RMSE=0.073364, PSNR=10.157256 Sample 140: MRAE=0.118103, RMSE=0.062887, PSNR=7.376157 Sample 141: MRAE=0.052016, RMSE=0.032025, PSNR=8.980537 Sample 142: MRAE=0.040953, RMSE=0.026778, PSNR=8.524938 Sample 143: MRAE=0.064216, RMSE=0.040099, PSNR=7.697602 Sample 144: MRAE=0.071881, RMSE=0.042514, PSNR=6.503532 Sample 145: MRAE=0.092705, RMSE=0.041441, PSNR=6.922041 Sample 146: MRAE=0.083157, RMSE=0.056474, PSNR=7.905920 Sample 147: MRAE=0.134217, RMSE=0.081595, PSNR=8.156103 Sample 148: MRAE=0.113038, RMSE=0.071167, PSNR=8.019299 Sample 149: MRAE=0.056403, RMSE=0.024000, PSNR=7.557703 Sample 150: MRAE=0.153074, RMSE=0.077094, PSNR=6.666243 Sample 151: MRAE=0.184770, RMSE=0.092356, PSNR=7.375814 Sample 152: MRAE=0.086917, RMSE=0.048198, PSNR=9.034969 Sample 153: MRAE=0.090375, RMSE=0.050153, PSNR=9.586740 Sample 154: MRAE=0.045143, RMSE=0.026284, PSNR=10.130412 Sample 155: MRAE=0.085673, RMSE=0.047766, PSNR=9.080666 Sample 156: MRAE=0.069323, RMSE=0.033000, PSNR=7.876578 Sample 157: MRAE=0.134917, RMSE=0.074668, PSNR=6.699115 Sample 158: MRAE=0.105827, RMSE=0.036553, PSNR=8.906585 Sample 159: MRAE=0.115410, RMSE=0.076404, PSNR=8.154876 Sample 160: MRAE=0.160544, RMSE=0.119296, PSNR=8.059774 Sample 161: MRAE=0.082953, RMSE=0.059691, PSNR=8.429387 Sample 162: MRAE=0.144309, RMSE=0.067920, PSNR=7.286351 Sample 163: MRAE=0.097659, RMSE=0.045316, PSNR=6.488842 Sample 164: MRAE=0.071142, RMSE=0.039997, PSNR=7.273733 Sample 165: MRAE=0.044828, RMSE=0.027752, PSNR=8.292791 Sample 166: MRAE=0.049759, RMSE=0.031653, PSNR=8.839759 Sample 167: MRAE=0.054415, RMSE=0.032973, PSNR=9.341039 Sample 168: MRAE=0.051942, RMSE=0.031238, PSNR=8.627955 Sample 169: MRAE=0.061872, RMSE=0.037220, PSNR=8.059312 Sample 170: MRAE=0.146370, RMSE=0.082227, PSNR=7.317153 Sample 171: MRAE=0.179647, RMSE=0.064643, PSNR=7.348540 Sample 172: MRAE=0.179982, RMSE=0.123186, PSNR=7.929003 Sample 173: MRAE=0.051183, RMSE=0.042707, PSNR=8.141586 Sample 174: MRAE=0.057252, RMSE=0.049305, PSNR=8.182627 Sample 175: MRAE=0.059904, RMSE=0.044424, PSNR=7.705759 Sample 176: MRAE=0.083189, RMSE=0.040079, PSNR=7.531192 Sample 177: MRAE=0.148961, RMSE=0.073904, PSNR=6.573454 Sample 178: MRAE=0.045492, RMSE=0.025363, PSNR=7.772922 Sample 179: MRAE=0.052372, RMSE=0.027771, PSNR=9.267268 Sample 180: MRAE=0.057800, RMSE=0.032941, PSNR=10.439747 Sample 181: MRAE=0.054604, RMSE=0.026336, PSNR=10.204439 Sample 182: MRAE=0.122635, RMSE=0.058370, PSNR=9.261060 Sample 183: MRAE=0.070228, RMSE=0.039984, PSNR=8.614073 Sample 184: MRAE=0.041578, RMSE=0.025654, PSNR=8.040407 Sample 185: MRAE=0.043498, RMSE=0.031899, PSNR=6.913839 Sample 186: MRAE=0.063705, RMSE=0.040746, PSNR=7.890941 Sample 187: MRAE=0.072079, RMSE=0.044826, PSNR=8.076179 Sample 188: MRAE=0.089956, RMSE=0.062749, PSNR=8.595047 Sample 189: MRAE=0.113531, RMSE=0.062156, PSNR=7.361894 Sample 190: MRAE=0.054053, RMSE=0.048361, PSNR=7.613191 Sample 191: MRAE=0.054835, RMSE=0.031397, PSNR=7.483241 Sample 192: MRAE=0.186007, RMSE=0.085804, PSNR=6.528930 Sample 193: MRAE=0.132465, RMSE=0.047390, PSNR=8.348166 Sample 194: MRAE=0.125309, RMSE=0.057038, PSNR=9.816593 Sample 195: MRAE=0.169097, RMSE=0.074972, PSNR=10.261738 Sample 196: MRAE=0.061346, RMSE=0.023122, PSNR=8.527294 Sample 197: MRAE=0.165236, RMSE=0.070474, PSNR=7.162570 Sample 198: MRAE=0.121102, RMSE=0.067758, PSNR=6.822510 Sample 199: MRAE=0.121421, RMSE=0.034256, PSNR=7.738821 Sample 200: MRAE=0.109886, RMSE=0.082016, PSNR=8.264444 Sample 201: MRAE=0.131052, RMSE=0.100472, PSNR=8.154277 Sample 202: MRAE=0.073400, RMSE=0.043643, PSNR=7.782423 Sample 203: MRAE=0.044733, RMSE=0.026220, PSNR=6.996742 Sample 204: MRAE=0.095250, RMSE=0.040382, PSNR=7.951826 Sample 205: MRAE=0.097671, RMSE=0.040421, PSNR=8.642898 Sample 206: MRAE=0.137733, RMSE=0.107422, PSNR=8.100001 Sample 207: MRAE=0.063932, RMSE=0.039095, PSNR=8.240196 Sample 208: MRAE=0.061577, RMSE=0.035195, PSNR=8.654260 Sample 209: MRAE=0.157229, RMSE=0.105858, PSNR=8.297215 Sample 210: MRAE=0.126084, RMSE=0.060036, PSNR=7.610888 Sample 211: MRAE=0.122640, RMSE=0.068740, PSNR=7.053859 Sample 212: MRAE=0.044536, RMSE=0.026467, PSNR=8.451796 Sample 213: MRAE=0.061121, RMSE=0.033710, PSNR=8.962964 Sample 214: MRAE=0.060033, RMSE=0.036500, PSNR=10.127442 Sample 215: MRAE=0.102367, RMSE=0.053214, PSNR=10.752719 Sample 216: MRAE=0.057878, RMSE=0.031808, PSNR=9.983028 Sample 217: MRAE=0.054267, RMSE=0.026189, PSNR=8.465268 Sample 218: MRAE=0.114438, RMSE=0.066808, PSNR=7.695365 Sample 219: MRAE=0.124504, RMSE=0.083814, PSNR=7.795519 Sample 220: MRAE=0.091528, RMSE=0.043489, PSNR=7.642610 Sample 221: MRAE=0.059891, RMSE=0.036116, PSNR=9.190834 ---------------------------------------------------------------------- Average Results: Average MRAE: 0.087642 Average RMSE: 0.051680 Average PSNR: 8.421735 MRAE Statistics: Min: 0.035830 Max: 0.277933 Median: 0.076942 Std: 0.038686 ======================================================================