不同mean std的pk
数据集
- 训练集：clothes std 2.1
- 验证集：LIP986
炼丹参数
- config.input_w = 384
- config.input_h = 384
- config.core.cls_num = 4
- config.aug.ImageWithMasksRandomRotateAug.max_angle = 45
- config.core.batch_size = 16
- config.core.model_path = "/opt/public/pretrain/ESPNetv2/imagenet/espnetv2_s_2.0.pth"
- config.core.optimizer = torch.optim.Adam(config.core.net.parameters(), 3e-4, (0.9, 0.999), eps=1e-08, weight_decay=5e-4)
- lambda_lr = lambda epoch: round ((1 - epoch/config.core.epoch_num) ** 0.9, 8)
- config.core.scheduler = optim.lr_scheduler.LambdaLR(config.core.optimizer, lr_lambda=lambda_lr)
- config.core.criterion = torch.nn.CrossEntropyLoss(weight)
- AugFactory('SpeckleAug@0.1 => GaussianAug@0.1 => HorlineAug@0.1 => VerlineAug@0.1 => LRmotionAug@0.1 =>UDmotionAug@0.1 => NoisyAug@0.1 => DarkAug@0.1 => ColorJitterAug@0.15 => BrightnessJitterAug@0.15 =>ContrastJitterAug@0.15 => ImageWithMasksRandomRotateAug@0.6 => ImageWithMasksNormalizeAug =>ImageWithMasksCenterCropAug => ImageWithMasksScaleAug => ImageWithMasksHFlipAug@0.5 =>ImageWithMasksToTensorAug', deepvac_config)
TRAIN
config.core.mean = config.data['mean'] && config.core.std = config.data['std']
Epoch No.: 0 TRAIN Loss = 0.8305 TRAIN mIOU = 0.6219
Epoch No.: 1 TRAIN Loss = 0.6207 TRAIN mIOU = 0.6758
Epoch No.: 2 TRAIN Loss = 0.5139 TRAIN mIOU = 0.7286
Epoch No.: 3 TRAIN Loss = 0.4646 TRAIN mIOU = 0.7510
Epoch No.: 4 TRAIN Loss = 0.4634 TRAIN mIOU = 0.7521
Epoch No.: 5 TRAIN Loss = 0.4377 TRAIN mIOU = 0.7588
Epoch No.: 6 TRAIN Loss = 0.4065 TRAIN mIOU = 0.7717
Epoch No.: 7 TRAIN Loss = 0.4041 TRAIN mIOU = 0.7728
Epoch No.: 8 TRAIN Loss = 0.4023 TRAIN mIOU = 0.7758
Epoch No.: 9 TRAIN Loss = 0.3936 TRAIN mIOU = 0.7782
Epoch No.: 10 TRAIN Loss = 0.3864 TRAIN mIOU = 0.7768
Epoch No.: 11 TRAIN Loss = 0.3854 TRAIN mIOU = 0.7785
Epoch No.: 12 TRAIN Loss = 0.3883 TRAIN mIOU = 0.7790
Epoch No.: 13 TRAIN Loss = 0.3796 TRAIN mIOU = 0.7800
Epoch No.: 14 TRAIN Loss = 0.3667 TRAIN mIOU = 0.7873
Epoch No.: 15 TRAIN Loss = 0.3524 TRAIN mIOU = 0.7932
Epoch No.: 16 TRAIN Loss = 0.3539 TRAIN mIOU = 0.7935
Epoch No.: 17 TRAIN Loss = 0.3466 TRAIN mIOU = 0.7947
Epoch No.: 18 TRAIN Loss = 0.3523 TRAIN mIOU = 0.7939
Epoch No.: 19 TRAIN Loss = 0.3518 TRAIN mIOU = 0.7919
Epoch No.: 20 TRAIN Loss = 0.3384 TRAIN mIOU = 0.8006
Epoch No.: 21 TRAIN Loss = 0.3494 TRAIN mIOU = 0.7959
Epoch No.: 22 TRAIN Loss = 0.3488 TRAIN mIOU = 0.7953
Epoch No.: 23 TRAIN Loss = 0.3383 TRAIN mIOU = 0.8008
Epoch No.: 24 TRAIN Loss = 0.3317 TRAIN mIOU = 0.8019
Epoch No.: 25 TRAIN Loss = 0.3454 TRAIN mIOU = 0.7980
Epoch No.: 26 TRAIN Loss = 0.3399 TRAIN mIOU = 0.8008
Epoch No.: 27 TRAIN Loss = 0.3283 TRAIN mIOU = 0.8043
Epoch No.: 28 TRAIN Loss = 0.3313 TRAIN mIOU = 0.8052
Epoch No.: 29 TRAIN Loss = 0.3149 TRAIN mIOU = 0.8144
Epoch No.: 30 TRAIN Loss = 0.3252 TRAIN mIOU = 0.8099
Epoch No.: 31 TRAIN Loss = 0.3250 TRAIN mIOU = 0.8086
Epoch No.: 32 TRAIN Loss = 0.3217 TRAIN mIOU = 0.8123
Epoch No.: 33 TRAIN Loss = 0.3145 TRAIN mIOU = 0.8153
Epoch No.: 34 TRAIN Loss = 0.3095 TRAIN mIOU = 0.8145
Epoch No.: 35 TRAIN Loss = 0.3217 TRAIN mIOU = 0.8118
Epoch No.: 36 TRAIN Loss = 0.3023 TRAIN mIOU = 0.8188
Epoch No.: 37 TRAIN Loss = 0.3479 TRAIN mIOU = 0.8003
Epoch No.: 38 TRAIN Loss = 0.3139 TRAIN mIOU = 0.8162
Epoch No.: 39 TRAIN Loss = 0.3228 TRAIN mIOU = 0.8084
Epoch No.: 40 TRAIN Loss = 0.3105 TRAIN mIOU = 0.8169
Epoch No.: 41 TRAIN Loss = 0.3098 TRAIN mIOU = 0.8176
Epoch No.: 42 TRAIN Loss = 0.3020 TRAIN mIOU = 0.8221
Epoch No.: 43 TRAIN Loss = 0.3232 TRAIN mIOU = 0.8106
Epoch No.: 44 TRAIN Loss = 0.3113 TRAIN mIOU = 0.8178
Epoch No.: 45 TRAIN Loss = 0.3171 TRAIN mIOU = 0.8139
Epoch No.: 46 TRAIN Loss = 0.3011 TRAIN mIOU = 0.8238
Epoch No.: 47 TRAIN Loss = 0.3028 TRAIN mIOU = 0.8196
Epoch No.: 48 TRAIN Loss = 0.2992 TRAIN mIOU = 0.8217
Epoch No.: 49 TRAIN Loss = 0.3661 TRAIN mIOU = 0.7944
Epoch No.: 50 TRAIN Loss = 0.2963 TRAIN mIOU = 0.8231
Epoch No.: 51 TRAIN Loss = 0.2979 TRAIN mIOU = 0.8209
Epoch No.: 52 TRAIN Loss = 0.2880 TRAIN mIOU = 0.8268
Epoch No.: 53 TRAIN Loss = 0.3200 TRAIN mIOU = 0.8141
Epoch No.: 54 TRAIN Loss = 0.2867 TRAIN mIOU = 0.8280
Epoch No.: 55 TRAIN Loss = 0.2851 TRAIN mIOU = 0.8299
Epoch No.: 56 TRAIN Loss = 0.2963 TRAIN mIOU = 0.8249
Epoch No.: 57 TRAIN Loss = 0.2867 TRAIN mIOU = 0.8282
Epoch No.: 58 TRAIN Loss = 0.2872 TRAIN mIOU = 0.8284
Epoch No.: 59 TRAIN Loss = 0.2830 TRAIN mIOU = 0.8283
Epoch No.: 60 TRAIN Loss = 0.2824 TRAIN mIOU = 0.8320
Epoch No.: 61 TRAIN Loss = 0.2788 TRAIN mIOU = 0.8347
Epoch No.: 62 TRAIN Loss = 0.3024 TRAIN mIOU = 0.8256
Epoch No.: 63 TRAIN Loss = 0.2851 TRAIN mIOU = 0.8298
Epoch No.: 64 TRAIN Loss = 0.2906 TRAIN mIOU = 0.8269
Epoch No.: 65 TRAIN Loss = 0.2852 TRAIN mIOU = 0.8279
Epoch No.: 66 TRAIN Loss = 0.2748 TRAIN mIOU = 0.8362
Epoch No.: 67 TRAIN Loss = 0.2889 TRAIN mIOU = 0.8313
Epoch No.: 68 TRAIN Loss = 0.2904 TRAIN mIOU = 0.8271
Epoch No.: 69 TRAIN Loss = 0.2932 TRAIN mIOU = 0.8270
Epoch No.: 70 TRAIN Loss = 0.2758 TRAIN mIOU = 0.8387
Epoch No.: 71 TRAIN Loss = 0.2819 TRAIN mIOU = 0.8336
config.core.mean = np.array([0.406, 0.456, 0.485]) * 255 && config.core.std = np.array([0.224, 0.225, 0.229]) * 255
Epoch No.: 0 TRAIN Loss = 0.8366 TRAIN mIOU = 0.6301
Epoch No.: 1 TRAIN Loss = 0.5948 TRAIN mIOU = 0.6986
Epoch No.: 2 TRAIN Loss = 0.5243 TRAIN mIOU = 0.7221
Epoch No.: 3 TRAIN Loss = 0.4927 TRAIN mIOU = 0.7373
Epoch No.: 4 TRAIN Loss = 0.4506 TRAIN mIOU = 0.7494
Epoch No.: 5 TRAIN Loss = 0.4363 TRAIN mIOU = 0.7573
Epoch No.: 6 TRAIN Loss = 0.4201 TRAIN mIOU = 0.7674
Epoch No.: 7 TRAIN Loss = 0.4050 TRAIN mIOU = 0.7667
Epoch No.: 8 TRAIN Loss = 0.3794 TRAIN mIOU = 0.7790
Epoch No.: 9 TRAIN Loss = 0.4004 TRAIN mIOU = 0.7725
Epoch No.: 10 TRAIN Loss = 0.3861 TRAIN mIOU = 0.7744
Epoch No.: 11 TRAIN Loss = 0.3822 TRAIN mIOU = 0.7762
Epoch No.: 12 TRAIN Loss = 0.3700 TRAIN mIOU = 0.7805
Epoch No.: 13 TRAIN Loss = 0.3603 TRAIN mIOU = 0.7861
Epoch No.: 14 TRAIN Loss = 0.3559 TRAIN mIOU = 0.7890
Epoch No.: 15 TRAIN Loss = 0.3692 TRAIN mIOU = 0.7817
Epoch No.: 16 TRAIN Loss = 0.3653 TRAIN mIOU = 0.7813
Epoch No.: 17 TRAIN Loss = 0.3682 TRAIN mIOU = 0.7851
Epoch No.: 18 TRAIN Loss = 0.3405 TRAIN mIOU = 0.7950
Epoch No.: 19 TRAIN Loss = 0.3447 TRAIN mIOU = 0.7958
Epoch No.: 20 TRAIN Loss = 0.3428 TRAIN mIOU = 0.7926
Epoch No.: 21 TRAIN Loss = 0.3439 TRAIN mIOU = 0.7919
Epoch No.: 22 TRAIN Loss = 0.3546 TRAIN mIOU = 0.7839
Epoch No.: 23 TRAIN Loss = 0.3414 TRAIN mIOU = 0.7954
Epoch No.: 24 TRAIN Loss = 0.3426 TRAIN mIOU = 0.7926
Epoch No.: 25 TRAIN Loss = 0.3539 TRAIN mIOU = 0.7960
Epoch No.: 26 TRAIN Loss = 0.3400 TRAIN mIOU = 0.7940
Epoch No.: 27 TRAIN Loss = 0.3334 TRAIN mIOU = 0.7996
Epoch No.: 28 TRAIN Loss = 0.3527 TRAIN mIOU = 0.7933
Epoch No.: 29 TRAIN Loss = 0.3269 TRAIN mIOU = 0.8054
Epoch No.: 30 TRAIN Loss = 0.3221 TRAIN mIOU = 0.8070
Epoch No.: 31 TRAIN Loss = 0.3345 TRAIN mIOU = 0.7999
Epoch No.: 32 TRAIN Loss = 0.3271 TRAIN mIOU = 0.8024
Epoch No.: 33 TRAIN Loss = 0.3218 TRAIN mIOU = 0.8073
Epoch No.: 34 TRAIN Loss = 0.3178 TRAIN mIOU = 0.8121
Epoch No.: 35 TRAIN Loss = 0.3109 TRAIN mIOU = 0.8157
Epoch No.: 36 TRAIN Loss = 0.3431 TRAIN mIOU = 0.8027
Epoch No.: 37 TRAIN Loss = 0.3178 TRAIN mIOU = 0.8111
Epoch No.: 38 TRAIN Loss = 0.3020 TRAIN mIOU = 0.8208
Epoch No.: 39 TRAIN Loss = 0.3194 TRAIN mIOU = 0.8142
Epoch No.: 40 TRAIN Loss = 0.3122 TRAIN mIOU = 0.8157
Epoch No.: 41 TRAIN Loss = 0.3096 TRAIN mIOU = 0.8186
Epoch No.: 42 TRAIN Loss = 0.2946 TRAIN mIOU = 0.8237
Epoch No.: 43 TRAIN Loss = 0.3123 TRAIN mIOU = 0.8167
Epoch No.: 44 TRAIN Loss = 0.2982 TRAIN mIOU = 0.8211
Epoch No.: 45 TRAIN Loss = 0.3216 TRAIN mIOU = 0.8107
Epoch No.: 46 TRAIN Loss = 0.3048 TRAIN mIOU = 0.8212
Epoch No.: 47 TRAIN Loss = 0.2985 TRAIN mIOU = 0.8211
Epoch No.: 48 TRAIN Loss = 0.3184 TRAIN mIOU = 0.8148
Epoch No.: 49 TRAIN Loss = 0.2981 TRAIN mIOU = 0.8221
Epoch No.: 50 TRAIN Loss = 0.2956 TRAIN mIOU = 0.8196
Epoch No.: 51 TRAIN Loss = 0.3136 TRAIN mIOU = 0.8112
Epoch No.: 52 TRAIN Loss = 0.3168 TRAIN mIOU = 0.8119
Epoch No.: 53 TRAIN Loss = 0.2957 TRAIN mIOU = 0.8197
Epoch No.: 54 TRAIN Loss = 0.2979 TRAIN mIOU = 0.8232
Epoch No.: 55 TRAIN Loss = 0.3038 TRAIN mIOU = 0.8206
Epoch No.: 56 TRAIN Loss = 0.2890 TRAIN mIOU = 0.8256
Epoch No.: 57 TRAIN Loss = 0.2870 TRAIN mIOU = 0.8271
Epoch No.: 58 TRAIN Loss = 0.2857 TRAIN mIOU = 0.8259
Epoch No.: 59 TRAIN Loss = 0.2945 TRAIN mIOU = 0.8229
Epoch No.: 60 TRAIN Loss = 0.2899 TRAIN mIOU = 0.8265
Epoch No.: 61 TRAIN Loss = 0.2808 TRAIN mIOU = 0.8332
Epoch No.: 62 TRAIN Loss = 0.2891 TRAIN mIOU = 0.8296
Epoch No.: 63 TRAIN Loss = 0.2807 TRAIN mIOU = 0.8308
Epoch No.: 64 TRAIN Loss = 0.2798 TRAIN mIOU = 0.8298
Epoch No.: 65 TRAIN Loss = 0.2956 TRAIN mIOU = 0.8232
Epoch No.: 66 TRAIN Loss = 0.2755 TRAIN mIOU = 0.8371
Epoch No.: 67 TRAIN Loss = 0.2910 TRAIN mIOU = 0.8253
Epoch No.: 68 TRAIN Loss = 0.2880 TRAIN mIOU = 0.8264
Epoch No.: 69 TRAIN Loss = 0.2793 TRAIN mIOU = 0.8322
Epoch No.: 70 TRAIN Loss = 0.2931 TRAIN mIOU = 0.8288
Epoch No.: 71 TRAIN Loss = 0.3069 TRAIN mIOU = 0.8180
VAL
config.core.mean = config.data['mean'] && config.core.std = config.data['std']
Epoch No.: 0 VAL Loss = 0.5173 VAL mIOU = 0.6432
Epoch No.: 1 VAL Loss = 0.7352 VAL mIOU = 0.6698
Epoch No.: 2 VAL Loss = 0.9506 VAL mIOU = 0.6749
Epoch No.: 3 VAL Loss = 0.4759 VAL mIOU = 0.7032
Epoch No.: 4 VAL Loss = 0.4187 VAL mIOU = 0.6993
Epoch No.: 5 VAL Loss = 0.5501 VAL mIOU = 0.6915
Epoch No.: 6 VAL Loss = 0.5055 VAL mIOU = 0.6912
Epoch No.: 7 VAL Loss = 0.3540 VAL mIOU = 0.7052
Epoch No.: 8 VAL Loss = 0.3820 VAL mIOU = 0.7039
Epoch No.: 9 VAL Loss = 0.4253 VAL mIOU = 0.6960
Epoch No.: 10 VAL Loss = 0.3428 VAL mIOU = 0.7053
Epoch No.: 11 VAL Loss = 0.3238 VAL mIOU = 0.7105
Epoch No.: 12 VAL Loss = 0.4548 VAL mIOU = 0.7008
Epoch No.: 13 VAL Loss = 0.2605 VAL mIOU = 0.7088
Epoch No.: 14 VAL Loss = 0.2577 VAL mIOU = 0.7024
Epoch No.: 15 VAL Loss = 0.4431 VAL mIOU = 0.6901
Epoch No.: 16 VAL Loss = 0.6530 VAL mIOU = 0.7028
Epoch No.: 17 VAL Loss = 0.3773 VAL mIOU = 0.7138
Epoch No.: 18 VAL Loss = 0.3961 VAL mIOU = 0.7098
Epoch No.: 19 VAL Loss = 0.5007 VAL mIOU = 0.7003
Epoch No.: 20 VAL Loss = 0.4277 VAL mIOU = 0.7065
Epoch No.: 21 VAL Loss = 0.3111 VAL mIOU = 0.7018
Epoch No.: 22 VAL Loss = 0.4636 VAL mIOU = 0.7042
Epoch No.: 23 VAL Loss = 0.3106 VAL mIOU = 0.7164
Epoch No.: 24 VAL Loss = 0.4415 VAL mIOU = 0.7028
Epoch No.: 25 VAL Loss = 0.4288 VAL mIOU = 0.7096
Epoch No.: 26 VAL Loss = 0.4786 VAL mIOU = 0.7097
Epoch No.: 27 VAL Loss = 0.2658 VAL mIOU = 0.7024
Epoch No.: 28 VAL Loss = 0.3159 VAL mIOU = 0.7124
Epoch No.: 29 VAL Loss = 0.2794 VAL mIOU = 0.6917
Epoch No.: 30 VAL Loss = 0.4634 VAL mIOU = 0.7142
Epoch No.: 31 VAL Loss = 0.3339 VAL mIOU = 0.7104
Epoch No.: 32 VAL Loss = 0.3904 VAL mIOU = 0.7146
Epoch No.: 33 VAL Loss = 0.1813 VAL mIOU = 0.7036
Epoch No.: 34 VAL Loss = 0.2867 VAL mIOU = 0.7112
Epoch No.: 35 VAL Loss = 0.3358 VAL mIOU = 0.7012
Epoch No.: 36 VAL Loss = 0.3156 VAL mIOU = 0.7102
Epoch No.: 37 VAL Loss = 0.6730 VAL mIOU = 0.7118
Epoch No.: 38 VAL Loss = 0.3842 VAL mIOU = 0.7124
Epoch No.: 39 VAL Loss = 0.2802 VAL mIOU = 0.7041
Epoch No.: 40 VAL Loss = 0.3072 VAL mIOU = 0.7008
Epoch No.: 41 VAL Loss = 0.2515 VAL mIOU = 0.7058
Epoch No.: 42 VAL Loss = 0.3218 VAL mIOU = 0.7141
Epoch No.: 43 VAL Loss = 0.3874 VAL mIOU = 0.7141
Epoch No.: 44 VAL Loss = 0.3264 VAL mIOU = 0.7046
Epoch No.: 45 VAL Loss = 0.2222 VAL mIOU = 0.7133
Epoch No.: 46 VAL Loss = 0.2573 VAL mIOU = 0.7175
Epoch No.: 47 VAL Loss = 0.2777 VAL mIOU = 0.7070
Epoch No.: 48 VAL Loss = 0.2893 VAL mIOU = 0.7089
Epoch No.: 49 VAL Loss = 0.3707 VAL mIOU = 0.7077
Epoch No.: 50 VAL Loss = 0.2880 VAL mIOU = 0.7086
Epoch No.: 51 VAL Loss = 0.2251 VAL mIOU = 0.7092
Epoch No.: 52 VAL Loss = 0.2015 VAL mIOU = 0.7095
Epoch No.: 53 VAL Loss = 0.3612 VAL mIOU = 0.7125
Epoch No.: 54 VAL Loss = 0.4589 VAL mIOU = 0.7110
Epoch No.: 55 VAL Loss = 0.1923 VAL mIOU = 0.7142
Epoch No.: 56 VAL Loss = 0.3266 VAL mIOU = 0.7219
Epoch No.: 57 VAL Loss = 0.4174 VAL mIOU = 0.7158
Epoch No.: 58 VAL Loss = 0.2626 VAL mIOU = 0.7116
Epoch No.: 59 VAL Loss = 0.2723 VAL mIOU = 0.7077
Epoch No.: 60 VAL Loss = 0.2446 VAL mIOU = 0.7148
Epoch No.: 61 VAL Loss = 0.2165 VAL mIOU = 0.7193
Epoch No.: 62 VAL Loss = 0.2425 VAL mIOU = 0.7183
Epoch No.: 63 VAL Loss = 0.1794 VAL mIOU = 0.7070
Epoch No.: 64 VAL Loss = 0.2505 VAL mIOU = 0.7088
Epoch No.: 65 VAL Loss = 0.2996 VAL mIOU = 0.7113
Epoch No.: 66 VAL Loss = 0.2840 VAL mIOU = 0.7256
Epoch No.: 67 VAL Loss = 0.1732 VAL mIOU = 0.7209
Epoch No.: 68 VAL Loss = 0.3359 VAL mIOU = 0.7211
Epoch No.: 69 VAL Loss = 0.2815 VAL mIOU = 0.7205
Epoch No.: 70 VAL Loss = 0.3863 VAL mIOU = 0.7236
Epoch No.: 71 VAL Loss = 0.4573 VAL mIOU = 0.7125
config.core.mean = np.array([0.406, 0.456, 0.485]) * 255 && config.core.std = np.array([0.224, 0.225, 0.229]) * 255
Epoch No.: 0 VAL Loss = 1.0027 VAL mIOU = 0.6609
Epoch No.: 1 VAL Loss = 0.4098 VAL mIOU = 0.6891
Epoch No.: 2 VAL Loss = 0.4527 VAL mIOU = 0.6816
Epoch No.: 3 VAL Loss = 0.4802 VAL mIOU = 0.6926
Epoch No.: 4 VAL Loss = 0.4131 VAL mIOU = 0.7090
Epoch No.: 5 VAL Loss = 0.3669 VAL mIOU = 0.6910
Epoch No.: 6 VAL Loss = 0.3852 VAL mIOU = 0.7039
Epoch No.: 7 VAL Loss = 0.4639 VAL mIOU = 0.7010
Epoch No.: 8 VAL Loss = 0.2421 VAL mIOU = 0.7056
Epoch No.: 9 VAL Loss = 0.3121 VAL mIOU = 0.7064
Epoch No.: 10 VAL Loss = 0.4149 VAL mIOU = 0.6891
Epoch No.: 11 VAL Loss = 0.4101 VAL mIOU = 0.6920
Epoch No.: 12 VAL Loss = 0.5366 VAL mIOU = 0.7054
Epoch No.: 13 VAL Loss = 0.2483 VAL mIOU = 0.7016
Epoch No.: 14 VAL Loss = 0.3290 VAL mIOU = 0.7000
Epoch No.: 15 VAL Loss = 0.4951 VAL mIOU = 0.7046
Epoch No.: 16 VAL Loss = 0.2388 VAL mIOU = 0.7102
Epoch No.: 17 VAL Loss = 0.4184 VAL mIOU = 0.7130
Epoch No.: 18 VAL Loss = 0.2305 VAL mIOU = 0.6993
Epoch No.: 19 VAL Loss = 0.2024 VAL mIOU = 0.7130
Epoch No.: 20 VAL Loss = 0.2712 VAL mIOU = 0.7119
Epoch No.: 21 VAL Loss = 0.3688 VAL mIOU = 0.7067
Epoch No.: 22 VAL Loss = 0.2932 VAL mIOU = 0.7037
Epoch No.: 23 VAL Loss = 0.3351 VAL mIOU = 0.7116
Epoch No.: 24 VAL Loss = 0.3120 VAL mIOU = 0.6907
Epoch No.: 25 VAL Loss = 0.3701 VAL mIOU = 0.7075
Epoch No.: 26 VAL Loss = 0.2840 VAL mIOU = 0.7055
Epoch No.: 27 VAL Loss = 0.1913 VAL mIOU = 0.7009
Epoch No.: 28 VAL Loss = 0.3571 VAL mIOU = 0.7058
Epoch No.: 29 VAL Loss = 0.2842 VAL mIOU = 0.6992
Epoch No.: 30 VAL Loss = 0.2221 VAL mIOU = 0.6947
Epoch No.: 31 VAL Loss = 0.4760 VAL mIOU = 0.7040
Epoch No.: 32 VAL Loss = 0.3142 VAL mIOU = 0.7053
Epoch No.: 33 VAL Loss = 0.3420 VAL mIOU = 0.7011
Epoch No.: 34 VAL Loss = 0.2332 VAL mIOU = 0.7141
Epoch No.: 35 VAL Loss = 0.3380 VAL mIOU = 0.7150
Epoch No.: 36 VAL Loss = 0.3132 VAL mIOU = 0.6955
Epoch No.: 37 VAL Loss = 0.2566 VAL mIOU = 0.7078
Epoch No.: 38 VAL Loss = 0.3091 VAL mIOU = 0.7179
Epoch No.: 39 VAL Loss = 0.2164 VAL mIOU = 0.7153
Epoch No.: 40 VAL Loss = 0.2183 VAL mIOU = 0.7151
Epoch No.: 41 VAL Loss = 0.3004 VAL mIOU = 0.7105
Epoch No.: 42 VAL Loss = 0.2718 VAL mIOU = 0.7142
Epoch No.: 43 VAL Loss = 0.2290 VAL mIOU = 0.7099
Epoch No.: 44 VAL Loss = 0.3576 VAL mIOU = 0.7211
Epoch No.: 45 VAL Loss = 0.4168 VAL mIOU = 0.7007
Epoch No.: 46 VAL Loss = 0.3234 VAL mIOU = 0.7180
Epoch No.: 47 VAL Loss = 0.4667 VAL mIOU = 0.6993
Epoch No.: 48 VAL Loss = 0.4768 VAL mIOU = 0.6966
Epoch No.: 49 VAL Loss = 0.2444 VAL mIOU = 0.7161
Epoch No.: 50 VAL Loss = 0.2509 VAL mIOU = 0.7061
Epoch No.: 51 VAL Loss = 0.3836 VAL mIOU = 0.7025
Epoch No.: 52 VAL Loss = 0.2030 VAL mIOU = 0.7112
Epoch No.: 53 VAL Loss = 0.3260 VAL mIOU = 0.7081
Epoch No.: 54 VAL Loss = 0.5848 VAL mIOU = 0.7049
Epoch No.: 55 VAL Loss = 0.2594 VAL mIOU = 0.7151
Epoch No.: 56 VAL Loss = 0.2592 VAL mIOU = 0.7108
Epoch No.: 57 VAL Loss = 0.2903 VAL mIOU = 0.7142
Epoch No.: 58 VAL Loss = 0.2276 VAL mIOU = 0.7182
Epoch No.: 59 VAL Loss = 0.2374 VAL mIOU = 0.7116
Epoch No.: 60 VAL Loss = 0.2516 VAL mIOU = 0.7113
Epoch No.: 61 VAL Loss = 0.4056 VAL mIOU = 0.7084
Epoch No.: 62 VAL Loss = 0.2798 VAL mIOU = 0.7176
Epoch No.: 63 VAL Loss = 0.4840 VAL mIOU = 0.7171
Epoch No.: 64 VAL Loss = 0.2694 VAL mIOU = 0.7125
Epoch No.: 65 VAL Loss = 0.2726 VAL mIOU = 0.7120
Epoch No.: 66 VAL Loss = 0.2584 VAL mIOU = 0.7047
Epoch No.: 67 VAL Loss = 0.2813 VAL mIOU = 0.7164
Epoch No.: 68 VAL Loss = 0.2304 VAL mIOU = 0.7129
Epoch No.: 69 VAL Loss = 0.2463 VAL mIOU = 0.7255
Epoch No.: 70 VAL Loss = 0.2372 VAL mIOU = 0.7171
Epoch No.: 71 VAL Loss = 0.3434 VAL mIOU = 0.7097
