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  1. experiments/data/AffinityNet@[email protected] +274 -0
  2. experiments/data/DeepLabv3+@ResNet-50@[email protected] +996 -0
  3. experiments/data/ResNet50@[email protected] +1761 -0
  4. experiments/logs/AffinityNet@[email protected] +50 -0
  5. experiments/logs/DeepLabv3+@ResNet-50@[email protected] +223 -0
  6. experiments/logs/ResNet50@[email protected] +200 -0
  7. experiments/models/DeepLabv3+@ResNet-50@[email protected] +3 -0
  8. experiments/tensorboards/DeepLabv3+@ResNet-50@Fix@GN/events.out.tfevents.1732202846.fa3f79e7e409.152433.0 +3 -0
  9. experiments/tensorboards/DeepLabv3+@ResNet-50@Fix@GN/events.out.tfevents.1732203578.fa3f79e7e409.155686.0 +3 -0
  10. requirements.txt +0 -0
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1752
+ },
1753
+ {
1754
+ "iteration": 9915,
1755
+ "threshold": 0.1,
1756
+ "train_mIoU": 46.11233098931772,
1757
+ "best_train_mIoU": 46.422600225610935,
1758
+ "time": 18
1759
+ }
1760
+ ]
1761
+ }
experiments/logs/AffinityNet@[email protected] ADDED
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1
+ [i] AffinityNet@ResNet-50@Puzzle
2
+
3
+ [i] mean values is [0.485, 0.456, 0.406]
4
+ [i] std values is [0.229, 0.224, 0.225]
5
+ [i] The number of class is 20
6
+ [i] train_transform is Compose(
7
+ <tools.ai.augment_utils.RandomResize_For_Segmentation object at 0x79be15df3430>
8
+ <tools.ai.augment_utils.RandomHorizontalFlip_For_Segmentation object at 0x79be15df3400>
9
+ <tools.ai.augment_utils.Normalize_For_Segmentation object at 0x79be15df3490>
10
+ <tools.ai.augment_utils.RandomCrop_For_Segmentation object at 0x79be15df3550>
11
+ <tools.ai.augment_utils.Transpose_For_Segmentation object at 0x79be15df35b0>
12
+ <tools.ai.augment_utils.Resize_For_Mask object at 0x79be15df35e0>
13
+ )
14
+
15
+ [i] log_iteration : 33
16
+ [i] val_iteration : 330
17
+ [i] max_iteration : 990
18
+ [i] Architecture is resnet50
19
+ [i] Total Params: 23.63M
20
+
21
+ [i] iteration=33, learning_rate=0.0971, loss=0.5866, bg_loss=0.5234, fg_loss=0.7140, neg_loss=0.5546, time=21sec
22
+ [i] iteration=66, learning_rate=0.0941, loss=0.4096, bg_loss=0.3311, fg_loss=0.5322, neg_loss=0.3876, time=18sec
23
+ [i] iteration=99, learning_rate=0.0910, loss=0.3690, bg_loss=0.2907, fg_loss=0.4917, neg_loss=0.3468, time=18sec
24
+ [i] iteration=132, learning_rate=0.0880, loss=0.3705, bg_loss=0.2945, fg_loss=0.4954, neg_loss=0.3460, time=18sec
25
+ [i] iteration=165, learning_rate=0.0850, loss=0.3655, bg_loss=0.2897, fg_loss=0.4852, neg_loss=0.3436, time=19sec
26
+ [i] iteration=198, learning_rate=0.0819, loss=0.3529, bg_loss=0.2799, fg_loss=0.4650, neg_loss=0.3334, time=19sec
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+ [i] iteration=231, learning_rate=0.0788, loss=0.3469, bg_loss=0.2809, fg_loss=0.4627, neg_loss=0.3220, time=19sec
28
+ [i] iteration=264, learning_rate=0.0757, loss=0.3695, bg_loss=0.2976, fg_loss=0.4803, neg_loss=0.3500, time=19sec
29
+ [i] iteration=297, learning_rate=0.0726, loss=0.3496, bg_loss=0.2698, fg_loss=0.4781, neg_loss=0.3252, time=19sec
30
+ [i] iteration=330, learning_rate=0.0695, loss=0.3384, bg_loss=0.2712, fg_loss=0.4460, neg_loss=0.3183, time=19sec
31
+ [i] iteration=363, learning_rate=0.0664, loss=0.3259, bg_loss=0.2599, fg_loss=0.4418, neg_loss=0.3010, time=21sec
32
+ [i] iteration=396, learning_rate=0.0632, loss=0.3375, bg_loss=0.2621, fg_loss=0.4632, neg_loss=0.3123, time=18sec
33
+ [i] iteration=429, learning_rate=0.0601, loss=0.3277, bg_loss=0.2583, fg_loss=0.4373, neg_loss=0.3076, time=18sec
34
+ [i] iteration=462, learning_rate=0.0569, loss=0.3313, bg_loss=0.2533, fg_loss=0.4549, neg_loss=0.3084, time=18sec
35
+ [i] iteration=495, learning_rate=0.0537, loss=0.3301, bg_loss=0.2494, fg_loss=0.4540, neg_loss=0.3085, time=19sec
36
+ [i] iteration=528, learning_rate=0.0505, loss=0.3229, bg_loss=0.2521, fg_loss=0.4341, neg_loss=0.3028, time=19sec
37
+ [i] iteration=561, learning_rate=0.0472, loss=0.3174, bg_loss=0.2464, fg_loss=0.4381, neg_loss=0.2925, time=19sec
38
+ [i] iteration=594, learning_rate=0.0439, loss=0.3270, bg_loss=0.2472, fg_loss=0.4452, neg_loss=0.3079, time=19sec
39
+ [i] iteration=627, learning_rate=0.0406, loss=0.3237, bg_loss=0.2511, fg_loss=0.4465, neg_loss=0.2987, time=19sec
40
+ [i] iteration=660, learning_rate=0.0373, loss=0.3258, bg_loss=0.2443, fg_loss=0.4472, neg_loss=0.3058, time=19sec
41
+ [i] iteration=693, learning_rate=0.0339, loss=0.3254, bg_loss=0.2507, fg_loss=0.4396, neg_loss=0.3056, time=21sec
42
+ [i] iteration=726, learning_rate=0.0305, loss=0.3242, bg_loss=0.2441, fg_loss=0.4472, neg_loss=0.3027, time=18sec
43
+ [i] iteration=759, learning_rate=0.0271, loss=0.3185, bg_loss=0.2413, fg_loss=0.4289, neg_loss=0.3019, time=19sec
44
+ [i] iteration=792, learning_rate=0.0236, loss=0.3287, bg_loss=0.2424, fg_loss=0.4540, neg_loss=0.3091, time=19sec
45
+ [i] iteration=825, learning_rate=0.0200, loss=0.3113, bg_loss=0.2352, fg_loss=0.4350, neg_loss=0.2875, time=19sec
46
+ [i] iteration=858, learning_rate=0.0164, loss=0.3110, bg_loss=0.2417, fg_loss=0.4271, neg_loss=0.2876, time=19sec
47
+ [i] iteration=891, learning_rate=0.0127, loss=0.3182, bg_loss=0.2422, fg_loss=0.4330, neg_loss=0.2987, time=19sec
48
+ [i] iteration=924, learning_rate=0.0089, loss=0.3107, bg_loss=0.2489, fg_loss=0.4280, neg_loss=0.2829, time=19sec
49
+ [i] iteration=957, learning_rate=0.0048, loss=0.3141, bg_loss=0.2364, fg_loss=0.4319, neg_loss=0.2940, time=19sec
50
+ [i] iteration=990, learning_rate=0.0002, loss=0.3115, bg_loss=0.2404, fg_loss=0.4173, neg_loss=0.2942, time=19sec
experiments/logs/DeepLabv3+@ResNet-50@[email protected] ADDED
@@ -0,0 +1,223 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ [i] DeepLabv3+@ResNet-50@Fix@GN
2
+
3
+ [i] mean values is [0.485, 0.456, 0.406]
4
+ [i] std values is [0.229, 0.224, 0.225]
5
+ [i] The number of class is 20
6
+ [i] train_transform is Compose(
7
+ <tools.ai.augment_utils.RandomResize_For_Segmentation object at 0x7a0c41d09750>
8
+ <tools.ai.augment_utils.RandomHorizontalFlip_For_Segmentation object at 0x7a0c41d09720>
9
+ <tools.ai.augment_utils.Normalize_For_Segmentation object at 0x7a0c41d097e0>
10
+ <tools.ai.augment_utils.RandomCrop_For_Segmentation object at 0x7a0c41d09870>
11
+ <tools.ai.augment_utils.Transpose_For_Segmentation object at 0x7a0c41d098d0>
12
+ )
13
+
14
+ [i] log_iteration : 66
15
+ [i] val_iteration : 661
16
+ [i] max_iteration : 9,915
17
+ [i] Architecture is DeepLabv3+
18
+ [i] Total Params: 40.35M
19
+
20
+ [i] iteration=66, learning_rate=0.0070, loss=1.6635, time=18sec
21
+ [i] iteration=132, learning_rate=0.0069, loss=1.1385, time=17sec
22
+ [i] iteration=198, learning_rate=0.0069, loss=0.9417, time=17sec
23
+ [i] iteration=264, learning_rate=0.0068, loss=0.8400, time=17sec
24
+ [i] iteration=330, learning_rate=0.0068, loss=0.7766, time=17sec
25
+ [i] iteration=396, learning_rate=0.0067, loss=0.6704, time=17sec
26
+ [i] iteration=462, learning_rate=0.0067, loss=0.6700, time=17sec
27
+ [i] iteration=528, learning_rate=0.0067, loss=0.6208, time=17sec
28
+ [i] iteration=594, learning_rate=0.0066, loss=0.6388, time=17sec
29
+ [i] iteration=660, learning_rate=0.0066, loss=0.6167, time=17sec
30
+ [i] DeepLabv3+@ResNet-50@Fix@GN
31
+
32
+ [i] mean values is [0.485, 0.456, 0.406]
33
+ [i] std values is [0.229, 0.224, 0.225]
34
+ [i] The number of class is 20
35
+ [i] train_transform is Compose(
36
+ <tools.ai.augment_utils.RandomResize_For_Segmentation object at 0x7b724931be20>
37
+ <tools.ai.augment_utils.RandomHorizontalFlip_For_Segmentation object at 0x7b724931bd60>
38
+ <tools.ai.augment_utils.Normalize_For_Segmentation object at 0x7b724931bdc0>
39
+ <tools.ai.augment_utils.RandomCrop_For_Segmentation object at 0x7b724931be50>
40
+ <tools.ai.augment_utils.Transpose_For_Segmentation object at 0x7b724931beb0>
41
+ )
42
+
43
+ [i] log_iteration : 66
44
+ [i] val_iteration : 661
45
+ [i] max_iteration : 9,915
46
+ [i] Architecture is DeepLabv3+
47
+ [i] Total Params: 40.35M
48
+
49
+ [i] iteration=66, learning_rate=0.0070, loss=1.6640, time=18sec
50
+ [i] iteration=132, learning_rate=0.0069, loss=1.1375, time=17sec
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+ [i] iteration=198, learning_rate=0.0069, loss=0.9249, time=17sec
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+ [i] iteration=264, learning_rate=0.0068, loss=0.7839, time=17sec
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+ [i] iteration=330, learning_rate=0.0068, loss=0.8084, time=17sec
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+ [i] iteration=396, learning_rate=0.0067, loss=0.6803, time=17sec
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+ [i] iteration=462, learning_rate=0.0067, loss=0.6661, time=17sec
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+ [i] iteration=528, learning_rate=0.0067, loss=0.6199, time=17sec
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+ [i] iteration=594, learning_rate=0.0066, loss=0.6303, time=17sec
58
+ [i] iteration=660, learning_rate=0.0066, loss=0.6040, time=17sec
59
+ [i] save model
60
+ [i] iteration=661, mIoU=40.20%, best_valid_mIoU=40.20%, time=38sec
61
+ [i] iteration=726, learning_rate=0.0065, loss=0.5300, time=55sec
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+ [i] iteration=792, learning_rate=0.0065, loss=0.5670, time=17sec
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+ [i] iteration=858, learning_rate=0.0065, loss=0.5230, time=17sec
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+ [i] iteration=924, learning_rate=0.0064, loss=0.5736, time=17sec
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+ [i] iteration=990, learning_rate=0.0064, loss=0.5719, time=17sec
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+ [i] iteration=1,056, learning_rate=0.0063, loss=0.5016, time=17sec
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+ [i] iteration=1,122, learning_rate=0.0063, loss=0.5114, time=17sec
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+ [i] iteration=1,188, learning_rate=0.0062, loss=0.5154, time=17sec
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+ [i] iteration=1,254, learning_rate=0.0062, loss=0.4591, time=17sec
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+ [i] iteration=1,320, learning_rate=0.0062, loss=0.5086, time=17sec
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+ [i] save model
72
+ [i] iteration=1,322, mIoU=46.93%, best_valid_mIoU=46.93%, time=38sec
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+ [i] iteration=1,386, learning_rate=0.0061, loss=0.4478, time=56sec
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+ [i] iteration=1,452, learning_rate=0.0061, loss=0.4730, time=17sec
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+ [i] iteration=1,518, learning_rate=0.0060, loss=0.5368, time=17sec
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+ [i] iteration=1,584, learning_rate=0.0060, loss=0.4908, time=17sec
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+ [i] iteration=1,650, learning_rate=0.0059, loss=0.4658, time=17sec
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+ [i] iteration=1,716, learning_rate=0.0059, loss=0.5231, time=17sec
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+ [i] iteration=1,782, learning_rate=0.0059, loss=0.4553, time=17sec
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+ [i] iteration=1,848, learning_rate=0.0058, loss=0.4160, time=17sec
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+ [i] iteration=1,914, learning_rate=0.0058, loss=0.4270, time=17sec
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+ [i] iteration=1,980, learning_rate=0.0057, loss=0.4344, time=17sec
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+ [i] save model
84
+ [i] iteration=1,983, mIoU=50.79%, best_valid_mIoU=50.79%, time=38sec
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+ [i] iteration=2,046, learning_rate=0.0057, loss=0.4106, time=56sec
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+ [i] iteration=2,112, learning_rate=0.0056, loss=0.4332, time=17sec
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+ [i] iteration=2,178, learning_rate=0.0056, loss=0.4205, time=17sec
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+ [i] iteration=2,244, learning_rate=0.0056, loss=0.3834, time=17sec
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+ [i] iteration=2,310, learning_rate=0.0055, loss=0.3868, time=17sec
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+ [i] iteration=2,376, learning_rate=0.0055, loss=0.4297, time=17sec
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+ [i] iteration=2,442, learning_rate=0.0054, loss=0.3851, time=17sec
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+ [i] iteration=2,508, learning_rate=0.0054, loss=0.4366, time=17sec
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+ [i] iteration=2,574, learning_rate=0.0053, loss=0.4140, time=17sec
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+ [i] iteration=2,640, learning_rate=0.0053, loss=0.3849, time=17sec
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+ [i] save model
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+ [i] iteration=2,644, mIoU=51.25%, best_valid_mIoU=51.25%, time=38sec
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+ [i] iteration=2,706, learning_rate=0.0053, loss=0.3532, time=56sec
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+ [i] iteration=2,772, learning_rate=0.0052, loss=0.3969, time=17sec
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+ [i] iteration=2,838, learning_rate=0.0052, loss=0.3573, time=17sec
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+ [i] iteration=2,904, learning_rate=0.0051, loss=0.3661, time=17sec
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+ [i] iteration=2,970, learning_rate=0.0051, loss=0.3844, time=17sec
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+ [i] iteration=3,036, learning_rate=0.0050, loss=0.3913, time=17sec
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+ [i] iteration=3,102, learning_rate=0.0050, loss=0.3605, time=17sec
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+ [i] iteration=3,168, learning_rate=0.0050, loss=0.4062, time=17sec
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+ [i] iteration=3,234, learning_rate=0.0049, loss=0.3763, time=17sec
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+ [i] iteration=3,300, learning_rate=0.0049, loss=0.3885, time=17sec
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+ [i] iteration=3,305, mIoU=49.77%, best_valid_mIoU=51.25%, time=38sec
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+ [i] iteration=3,366, learning_rate=0.0048, loss=0.3577, time=56sec
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+ [i] iteration=3,432, learning_rate=0.0048, loss=0.3556, time=17sec
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+ [i] iteration=3,498, learning_rate=0.0047, loss=0.3837, time=17sec
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+ [i] iteration=3,564, learning_rate=0.0047, loss=0.3703, time=17sec
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+ [i] iteration=3,630, learning_rate=0.0046, loss=0.3720, time=17sec
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+ [i] iteration=3,696, learning_rate=0.0046, loss=0.3600, time=17sec
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+ [i] iteration=3,762, learning_rate=0.0046, loss=0.3618, time=17sec
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+ [i] iteration=3,828, learning_rate=0.0045, loss=0.3650, time=17sec
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+ [i] iteration=3,894, learning_rate=0.0045, loss=0.4094, time=17sec
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+ [i] iteration=3,960, learning_rate=0.0044, loss=0.3648, time=17sec
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+ [i] save model
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+ [i] iteration=3,966, mIoU=53.03%, best_valid_mIoU=53.03%, time=38sec
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+ [i] iteration=4,026, learning_rate=0.0044, loss=0.3729, time=56sec
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+ [i] iteration=4,092, learning_rate=0.0043, loss=0.3564, time=17sec
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+ [i] iteration=4,158, learning_rate=0.0043, loss=0.3481, time=17sec
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+ [i] iteration=4,224, learning_rate=0.0042, loss=0.3464, time=17sec
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+ [i] iteration=4,290, learning_rate=0.0042, loss=0.3544, time=17sec
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+ [i] iteration=4,356, learning_rate=0.0042, loss=0.3619, time=17sec
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+ [i] iteration=4,422, learning_rate=0.0041, loss=0.3461, time=17sec
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+ [i] iteration=4,488, learning_rate=0.0041, loss=0.3715, time=17sec
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+ [i] iteration=4,554, learning_rate=0.0040, loss=0.3182, time=17sec
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+ [i] iteration=4,620, learning_rate=0.0040, loss=0.3448, time=17sec
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+ [i] save model
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+ [i] iteration=4,627, mIoU=53.53%, best_valid_mIoU=53.53%, time=38sec
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+ [i] iteration=4,686, learning_rate=0.0039, loss=0.3394, time=56sec
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+ [i] iteration=4,752, learning_rate=0.0039, loss=0.3264, time=17sec
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+ [i] iteration=4,818, learning_rate=0.0038, loss=0.3340, time=17sec
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+ [i] iteration=4,884, learning_rate=0.0038, loss=0.3304, time=17sec
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+ [i] iteration=4,950, learning_rate=0.0038, loss=0.3350, time=17sec
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+ [i] iteration=5,016, learning_rate=0.0037, loss=0.3412, time=17sec
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+ [i] iteration=5,082, learning_rate=0.0037, loss=0.3317, time=17sec
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+ [i] iteration=5,148, learning_rate=0.0036, loss=0.3452, time=17sec
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+ [i] iteration=5,214, learning_rate=0.0036, loss=0.3681, time=17sec
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+ [i] iteration=5,280, learning_rate=0.0035, loss=0.3098, time=17sec
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+ [i] save model
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+ [i] iteration=5,288, mIoU=53.69%, best_valid_mIoU=53.69%, time=38sec
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+ [i] iteration=5,346, learning_rate=0.0035, loss=0.2972, time=56sec
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+ [i] iteration=5,412, learning_rate=0.0034, loss=0.3036, time=17sec
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+ [i] iteration=5,478, learning_rate=0.0034, loss=0.3157, time=17sec
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+ [i] iteration=5,544, learning_rate=0.0034, loss=0.3159, time=17sec
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+ [i] iteration=5,610, learning_rate=0.0033, loss=0.3297, time=17sec
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+ [i] iteration=5,676, learning_rate=0.0033, loss=0.3275, time=17sec
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+ [i] iteration=5,742, learning_rate=0.0032, loss=0.3331, time=17sec
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+ [i] iteration=5,808, learning_rate=0.0032, loss=0.3217, time=17sec
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+ [i] iteration=5,874, learning_rate=0.0031, loss=0.3111, time=17sec
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+ [i] iteration=5,940, learning_rate=0.0031, loss=0.3477, time=17sec
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+ [i] save model
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+ [i] iteration=5,949, mIoU=53.96%, best_valid_mIoU=53.96%, time=38sec
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+ [i] iteration=6,006, learning_rate=0.0030, loss=0.3148, time=56sec
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+ [i] iteration=6,072, learning_rate=0.0030, loss=0.2941, time=17sec
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+ [i] iteration=6,138, learning_rate=0.0029, loss=0.3113, time=17sec
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+ [i] iteration=6,204, learning_rate=0.0029, loss=0.3195, time=17sec
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+ [i] iteration=6,270, learning_rate=0.0028, loss=0.3025, time=17sec
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+ [i] iteration=6,336, learning_rate=0.0028, loss=0.3077, time=17sec
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+ [i] iteration=6,402, learning_rate=0.0028, loss=0.3175, time=17sec
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+ [i] iteration=6,468, learning_rate=0.0027, loss=0.3168, time=17sec
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+ [i] iteration=6,534, learning_rate=0.0027, loss=0.3162, time=17sec
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+ [i] iteration=6,600, learning_rate=0.0026, loss=0.3190, time=17sec
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+ [i] iteration=6,610, mIoU=53.62%, best_valid_mIoU=53.96%, time=37sec
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+ [i] iteration=6,666, learning_rate=0.0026, loss=0.3074, time=55sec
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+ [i] iteration=6,732, learning_rate=0.0025, loss=0.3036, time=17sec
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+ [i] iteration=6,798, learning_rate=0.0025, loss=0.3042, time=17sec
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+ [i] iteration=6,864, learning_rate=0.0024, loss=0.3041, time=17sec
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+ [i] iteration=6,930, learning_rate=0.0024, loss=0.3093, time=17sec
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+ [i] iteration=6,996, learning_rate=0.0023, loss=0.3124, time=17sec
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+ [i] iteration=7,062, learning_rate=0.0023, loss=0.3095, time=17sec
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+ [i] iteration=7,128, learning_rate=0.0022, loss=0.2930, time=17sec
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+ [i] iteration=7,194, learning_rate=0.0022, loss=0.2987, time=17sec
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+ [i] iteration=7,260, learning_rate=0.0021, loss=0.2987, time=17sec
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+ [i] iteration=7,271, mIoU=53.70%, best_valid_mIoU=53.96%, time=37sec
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+ [i] iteration=7,326, learning_rate=0.0021, loss=0.2850, time=55sec
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+ [i] iteration=7,392, learning_rate=0.0020, loss=0.2973, time=17sec
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+ [i] iteration=7,458, learning_rate=0.0020, loss=0.2897, time=17sec
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+ [i] iteration=7,524, learning_rate=0.0019, loss=0.2875, time=17sec
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+ [i] iteration=7,590, learning_rate=0.0019, loss=0.2958, time=17sec
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+ [i] iteration=7,656, learning_rate=0.0018, loss=0.2981, time=17sec
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+ [i] iteration=7,722, learning_rate=0.0018, loss=0.3028, time=17sec
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+ [i] iteration=7,788, learning_rate=0.0018, loss=0.2850, time=17sec
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+ [i] iteration=7,854, learning_rate=0.0017, loss=0.2987, time=17sec
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+ [i] iteration=7,920, learning_rate=0.0017, loss=0.2677, time=17sec
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+ [i] save model
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+ [i] iteration=7,932, mIoU=54.37%, best_valid_mIoU=54.37%, time=38sec
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+ [i] iteration=7,986, learning_rate=0.0016, loss=0.2964, time=56sec
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+ [i] iteration=8,052, learning_rate=0.0016, loss=0.2886, time=17sec
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+ [i] iteration=8,118, learning_rate=0.0015, loss=0.2892, time=17sec
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+ [i] iteration=8,184, learning_rate=0.0015, loss=0.2826, time=17sec
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+ [i] iteration=8,250, learning_rate=0.0014, loss=0.2820, time=17sec
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+ [i] iteration=8,316, learning_rate=0.0014, loss=0.2768, time=17sec
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+ [i] iteration=8,382, learning_rate=0.0013, loss=0.2835, time=17sec
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+ [i] iteration=8,448, learning_rate=0.0013, loss=0.2853, time=17sec
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+ [i] iteration=8,514, learning_rate=0.0012, loss=0.2774, time=17sec
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+ [i] iteration=8,580, learning_rate=0.0012, loss=0.2760, time=17sec
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+ [i] iteration=8,593, mIoU=54.07%, best_valid_mIoU=54.37%, time=37sec
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+ [i] iteration=8,646, learning_rate=0.0011, loss=0.2826, time=55sec
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+ [i] iteration=8,712, learning_rate=0.0010, loss=0.2559, time=17sec
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+ [i] iteration=8,778, learning_rate=0.0010, loss=0.2634, time=17sec
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+ [i] iteration=8,844, learning_rate=0.0009, loss=0.2811, time=17sec
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+ [i] iteration=8,910, learning_rate=0.0009, loss=0.2935, time=17sec
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+ [i] iteration=8,976, learning_rate=0.0008, loss=0.2786, time=17sec
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+ [i] iteration=9,042, learning_rate=0.0008, loss=0.2715, time=17sec
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+ [i] iteration=9,108, learning_rate=0.0007, loss=0.2898, time=17sec
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+ [i] iteration=9,174, learning_rate=0.0007, loss=0.2847, time=17sec
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+ [i] iteration=9,240, learning_rate=0.0006, loss=0.2928, time=17sec
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+ [i] save model
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+ [i] iteration=9,254, mIoU=55.12%, best_valid_mIoU=55.12%, time=38sec
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+ [i] iteration=9,306, learning_rate=0.0006, loss=0.2850, time=56sec
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+ [i] iteration=9,372, learning_rate=0.0005, loss=0.2861, time=17sec
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+ [i] iteration=9,438, learning_rate=0.0005, loss=0.2701, time=17sec
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+ [i] iteration=9,504, learning_rate=0.0004, loss=0.2785, time=17sec
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+ [i] iteration=9,570, learning_rate=0.0003, loss=0.2818, time=17sec
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+ [i] iteration=9,636, learning_rate=0.0003, loss=0.2744, time=17sec
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+ [i] iteration=9,702, learning_rate=0.0002, loss=0.2816, time=17sec
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+ [i] iteration=9,768, learning_rate=0.0002, loss=0.2616, time=17sec
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+ [i] iteration=9,834, learning_rate=0.0001, loss=0.2540, time=17sec
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+ [i] iteration=9,900, learning_rate=0.0000, loss=0.2749, time=17sec
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+ [i] iteration=9,915, mIoU=55.09%, best_valid_mIoU=55.12%, time=38sec
experiments/logs/ResNet50@[email protected] ADDED
@@ -0,0 +1,200 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ [i] ResNet50@Puzzle@optimal
2
+
3
+ [i] mean values is [0.485, 0.456, 0.406]
4
+ [i] std values is [0.229, 0.224, 0.225]
5
+ [i] The number of class is 20
6
+ [i] train_transform is Compose(
7
+ <tools.ai.augment_utils.RandomResize object at 0x7b9d81d50520>
8
+ <tools.ai.augment_utils.RandomHorizontalFlip object at 0x7b9d81d50490>
9
+ <tools.ai.augment_utils.Normalize object at 0x7b9d81d50730>
10
+ <tools.ai.augment_utils.RandomCrop object at 0x7b9d81d503d0>
11
+ <tools.ai.augment_utils.Transpose object at 0x7b9d81d50430>
12
+ )
13
+ [i] test_transform is Compose(
14
+ <tools.ai.augment_utils.Normalize_For_Segmentation object at 0x7b9d81d502b0>
15
+ <tools.ai.augment_utils.Top_Left_Crop_For_Segmentation object at 0x7b9d81d500d0>
16
+ <tools.ai.augment_utils.Transpose_For_Segmentation object at 0x7b9d81d50100>
17
+ )
18
+
19
+ [i] log_iteration : 66
20
+ [i] val_iteration : 661
21
+ [i] max_iteration : 9,915
22
+ [i] Architecture is resnet50
23
+ [i] Total Params: 23.55M
24
+
25
+ [i] The number of pretrained weights : 106
26
+ [i] The number of pretrained bias : 53
27
+ [i] The number of scratched weights : 1
28
+ [i] The number of scratched bias : 0
29
+ [i] iteration=66, learning_rate=0.0994, alpha=0.03, loss=0.5450, class_loss=0.2720, p_class_loss=0.2716, re_loss=0.0728, conf_loss=0.0000, time=17sec
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+ [i] iteration=132, learning_rate=0.0988, alpha=0.08, loss=0.3240, class_loss=0.1571, p_class_loss=0.1604, re_loss=0.0800, conf_loss=0.0000, time=15sec
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+ [i] iteration=198, learning_rate=0.0982, alpha=0.13, loss=0.2655, class_loss=0.1247, p_class_loss=0.1286, re_loss=0.0920, conf_loss=0.0000, time=15sec
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+ [i] iteration=264, learning_rate=0.0976, alpha=0.19, loss=0.2734, class_loss=0.1248, p_class_loss=0.1296, re_loss=0.1020, conf_loss=0.0000, time=15sec
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+ [i] iteration=330, learning_rate=0.0970, alpha=0.24, loss=0.2663, class_loss=0.1184, p_class_loss=0.1237, re_loss=0.1012, conf_loss=0.0000, time=15sec
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+ [i] iteration=396, learning_rate=0.0964, alpha=0.29, loss=0.2637, class_loss=0.1147, p_class_loss=0.1193, re_loss=0.1016, conf_loss=0.0000, time=15sec
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+ [i] iteration=462, learning_rate=0.0958, alpha=0.35, loss=0.2497, class_loss=0.1052, p_class_loss=0.1096, re_loss=0.1011, conf_loss=0.0000, time=15sec
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+ [i] iteration=528, learning_rate=0.0952, alpha=0.40, loss=0.2480, class_loss=0.1024, p_class_loss=0.1073, re_loss=0.0960, conf_loss=0.0000, time=15sec
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+ [i] iteration=594, learning_rate=0.0946, alpha=0.45, loss=0.2408, class_loss=0.0982, p_class_loss=0.1023, re_loss=0.0892, conf_loss=0.0000, time=15sec
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+ [i] iteration=660, learning_rate=0.0940, alpha=0.51, loss=0.2340, class_loss=0.0930, p_class_loss=0.0967, re_loss=0.0879, conf_loss=0.0000, time=15sec
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+ [i] save model
40
+ [i] iteration=661, threshold=0.10, train_mIoU=40.43%, best_train_mIoU=40.43%, time=18sec
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+ [i] iteration=726, learning_rate=0.0934, alpha=0.56, loss=0.2258, class_loss=0.0862, p_class_loss=0.0908, re_loss=0.0873, conf_loss=0.0000, time=34sec
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+ [i] iteration=792, learning_rate=0.0928, alpha=0.61, loss=0.2239, class_loss=0.0842, p_class_loss=0.0881, re_loss=0.0844, conf_loss=0.0000, time=15sec
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+ [i] iteration=858, learning_rate=0.0922, alpha=0.67, loss=0.2513, class_loss=0.0957, p_class_loss=0.0998, re_loss=0.0839, conf_loss=0.0000, time=15sec
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+ [i] iteration=924, learning_rate=0.0916, alpha=0.72, loss=0.2326, class_loss=0.0871, p_class_loss=0.0914, re_loss=0.0753, conf_loss=0.0000, time=15sec
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+ [i] iteration=990, learning_rate=0.0910, alpha=0.77, loss=0.2415, class_loss=0.0887, p_class_loss=0.0934, re_loss=0.0771, conf_loss=0.0000, time=15sec
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+ [i] iteration=1,056, learning_rate=0.0904, alpha=0.83, loss=0.2436, class_loss=0.0887, p_class_loss=0.0931, re_loss=0.0749, conf_loss=0.0000, time=15sec
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+ [i] iteration=1,122, learning_rate=0.0898, alpha=0.88, loss=0.2431, class_loss=0.0885, p_class_loss=0.0923, re_loss=0.0709, conf_loss=0.0000, time=15sec
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+ [i] iteration=1,188, learning_rate=0.0892, alpha=0.93, loss=0.2383, class_loss=0.0846, p_class_loss=0.0887, re_loss=0.0698, conf_loss=0.0000, time=15sec
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+ [i] iteration=1,254, learning_rate=0.0886, alpha=0.98, loss=0.2512, class_loss=0.0905, p_class_loss=0.0949, re_loss=0.0668, conf_loss=0.0000, time=15sec
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+ [i] iteration=1,320, learning_rate=0.0879, alpha=1.04, loss=0.2436, class_loss=0.0860, p_class_loss=0.0902, re_loss=0.0649, conf_loss=0.0000, time=15sec
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+ [i] save model
52
+ [i] iteration=1,322, threshold=0.10, train_mIoU=43.46%, best_train_mIoU=43.46%, time=18sec
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+ [i] iteration=1,386, learning_rate=0.0873, alpha=1.09, loss=0.2324, class_loss=0.0796, p_class_loss=0.0843, re_loss=0.0628, conf_loss=0.0000, time=34sec
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+ [i] iteration=1,452, learning_rate=0.0867, alpha=1.14, loss=0.2405, class_loss=0.0829, p_class_loss=0.0869, re_loss=0.0618, conf_loss=0.0000, time=15sec
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+ [i] iteration=1,518, learning_rate=0.0861, alpha=1.20, loss=0.2409, class_loss=0.0820, p_class_loss=0.0863, re_loss=0.0606, conf_loss=0.0000, time=15sec
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+ [i] iteration=1,584, learning_rate=0.0855, alpha=1.25, loss=0.2384, class_loss=0.0817, p_class_loss=0.0860, re_loss=0.0564, conf_loss=0.0000, time=15sec
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+ [i] iteration=1,650, learning_rate=0.0849, alpha=1.30, loss=0.2454, class_loss=0.0832, p_class_loss=0.0869, re_loss=0.0578, conf_loss=0.0000, time=15sec
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+ [i] iteration=1,716, learning_rate=0.0843, alpha=1.36, loss=0.2452, class_loss=0.0819, p_class_loss=0.0863, re_loss=0.0568, conf_loss=0.0000, time=15sec
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+ [i] iteration=1,782, learning_rate=0.0837, alpha=1.41, loss=0.2589, class_loss=0.0876, p_class_loss=0.0915, re_loss=0.0565, conf_loss=0.0000, time=15sec
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+ [i] iteration=1,848, learning_rate=0.0831, alpha=1.46, loss=0.2510, class_loss=0.0851, p_class_loss=0.0891, re_loss=0.0525, conf_loss=0.0000, time=15sec
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+ [i] iteration=1,914, learning_rate=0.0825, alpha=1.52, loss=0.2478, class_loss=0.0831, p_class_loss=0.0872, re_loss=0.0511, conf_loss=0.0000, time=15sec
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+ [i] iteration=1,980, learning_rate=0.0818, alpha=1.57, loss=0.2568, class_loss=0.0858, p_class_loss=0.0899, re_loss=0.0516, conf_loss=0.0000, time=15sec
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+ [i] iteration=1,983, threshold=0.10, train_mIoU=43.18%, best_train_mIoU=43.46%, time=17sec
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+ [i] iteration=2,046, learning_rate=0.0812, alpha=1.62, loss=0.2431, class_loss=0.0796, p_class_loss=0.0835, re_loss=0.0493, conf_loss=0.0000, time=34sec
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+ [i] iteration=2,112, learning_rate=0.0806, alpha=1.68, loss=0.2583, class_loss=0.0855, p_class_loss=0.0897, re_loss=0.0496, conf_loss=0.0000, time=15sec
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+ [i] iteration=2,178, learning_rate=0.0800, alpha=1.73, loss=0.2435, class_loss=0.0797, p_class_loss=0.0837, re_loss=0.0463, conf_loss=0.0000, time=15sec
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+ [i] iteration=2,244, learning_rate=0.0794, alpha=1.78, loss=0.2551, class_loss=0.0834, p_class_loss=0.0877, re_loss=0.0471, conf_loss=0.0000, time=15sec
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+ [i] iteration=2,310, learning_rate=0.0788, alpha=1.84, loss=0.2540, class_loss=0.0833, p_class_loss=0.0879, re_loss=0.0450, conf_loss=0.0000, time=15sec
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+ [i] iteration=2,376, learning_rate=0.0782, alpha=1.89, loss=0.2583, class_loss=0.0841, p_class_loss=0.0890, re_loss=0.0451, conf_loss=0.0000, time=15sec
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+ [i] iteration=2,442, learning_rate=0.0775, alpha=1.94, loss=0.2598, class_loss=0.0858, p_class_loss=0.0898, re_loss=0.0433, conf_loss=0.0000, time=15sec
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+ [i] iteration=2,508, learning_rate=0.0769, alpha=2.00, loss=0.2635, class_loss=0.0871, p_class_loss=0.0911, re_loss=0.0427, conf_loss=0.0000, time=15sec
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+ [i] iteration=2,574, learning_rate=0.0763, alpha=2.05, loss=0.2546, class_loss=0.0815, p_class_loss=0.0855, re_loss=0.0427, conf_loss=0.0000, time=15sec
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+ [i] iteration=2,640, learning_rate=0.0757, alpha=2.10, loss=0.2583, class_loss=0.0855, p_class_loss=0.0894, re_loss=0.0396, conf_loss=0.0000, time=15sec
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+ [i] save model
75
+ [i] iteration=2,644, threshold=0.10, train_mIoU=44.51%, best_train_mIoU=44.51%, time=18sec
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+ [i] iteration=2,706, learning_rate=0.0751, alpha=2.16, loss=0.2611, class_loss=0.0843, p_class_loss=0.0881, re_loss=0.0411, conf_loss=0.0000, time=34sec
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+ [i] iteration=2,772, learning_rate=0.0745, alpha=2.21, loss=0.2525, class_loss=0.0810, p_class_loss=0.0847, re_loss=0.0393, conf_loss=0.0000, time=15sec
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+ [i] iteration=2,838, learning_rate=0.0738, alpha=2.26, loss=0.2540, class_loss=0.0816, p_class_loss=0.0857, re_loss=0.0383, conf_loss=0.0000, time=15sec
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+ [i] iteration=2,904, learning_rate=0.0732, alpha=2.32, loss=0.2652, class_loss=0.0844, p_class_loss=0.0884, re_loss=0.0399, conf_loss=0.0000, time=15sec
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+ [i] iteration=2,970, learning_rate=0.0726, alpha=2.37, loss=0.2607, class_loss=0.0836, p_class_loss=0.0875, re_loss=0.0378, conf_loss=0.0000, time=15sec
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+ [i] iteration=3,036, learning_rate=0.0720, alpha=2.42, loss=0.2755, class_loss=0.0893, p_class_loss=0.0935, re_loss=0.0383, conf_loss=0.0000, time=15sec
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+ [i] iteration=3,102, learning_rate=0.0714, alpha=2.48, loss=0.2690, class_loss=0.0872, p_class_loss=0.0913, re_loss=0.0366, conf_loss=0.0000, time=15sec
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+ [i] iteration=3,168, learning_rate=0.0707, alpha=2.53, loss=0.2728, class_loss=0.0901, p_class_loss=0.0939, re_loss=0.0351, conf_loss=0.0000, time=15sec
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+ [i] iteration=3,234, learning_rate=0.0701, alpha=2.58, loss=0.2591, class_loss=0.0822, p_class_loss=0.0865, re_loss=0.0350, conf_loss=0.0000, time=15sec
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+ [i] iteration=3,300, learning_rate=0.0695, alpha=2.64, loss=0.2650, class_loss=0.0854, p_class_loss=0.0892, re_loss=0.0343, conf_loss=0.0000, time=15sec
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+ [i] save model
87
+ [i] iteration=3,305, threshold=0.10, train_mIoU=45.10%, best_train_mIoU=45.10%, time=18sec
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+ [i] iteration=3,366, learning_rate=0.0689, alpha=2.69, loss=0.2514, class_loss=0.0783, p_class_loss=0.0823, re_loss=0.0338, conf_loss=0.0000, time=34sec
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+ [i] iteration=3,432, learning_rate=0.0682, alpha=2.74, loss=0.2762, class_loss=0.0893, p_class_loss=0.0933, re_loss=0.0341, conf_loss=0.0000, time=15sec
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+ [i] iteration=3,498, learning_rate=0.0676, alpha=2.80, loss=0.2591, class_loss=0.0813, p_class_loss=0.0847, re_loss=0.0333, conf_loss=0.0000, time=15sec
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+ [i] iteration=3,564, learning_rate=0.0670, alpha=2.85, loss=0.2858, class_loss=0.0919, p_class_loss=0.0962, re_loss=0.0343, conf_loss=0.0000, time=15sec
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+ [i] iteration=3,630, learning_rate=0.0664, alpha=2.90, loss=0.2693, class_loss=0.0856, p_class_loss=0.0902, re_loss=0.0322, conf_loss=0.0000, time=15sec
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+ [i] iteration=3,696, learning_rate=0.0657, alpha=2.96, loss=0.2648, class_loss=0.0845, p_class_loss=0.0881, re_loss=0.0312, conf_loss=0.0000, time=15sec
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+ [i] iteration=3,762, learning_rate=0.0651, alpha=3.01, loss=0.2704, class_loss=0.0865, p_class_loss=0.0907, re_loss=0.0310, conf_loss=0.0000, time=15sec
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+ [i] iteration=3,828, learning_rate=0.0645, alpha=3.06, loss=0.2752, class_loss=0.0883, p_class_loss=0.0921, re_loss=0.0310, conf_loss=0.0000, time=15sec
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+ [i] iteration=3,894, learning_rate=0.0638, alpha=3.11, loss=0.2768, class_loss=0.0883, p_class_loss=0.0921, re_loss=0.0309, conf_loss=0.0000, time=15sec
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+ [i] iteration=3,960, learning_rate=0.0632, alpha=3.17, loss=0.2772, class_loss=0.0894, p_class_loss=0.0932, re_loss=0.0299, conf_loss=0.0000, time=15sec
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+ [i] save model
99
+ [i] iteration=3,966, threshold=0.10, train_mIoU=46.22%, best_train_mIoU=46.22%, time=18sec
100
+ [i] iteration=4,026, learning_rate=0.0626, alpha=3.22, loss=0.2772, class_loss=0.0880, p_class_loss=0.0916, re_loss=0.0303, conf_loss=0.0000, time=34sec
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+ [i] iteration=4,092, learning_rate=0.0619, alpha=3.27, loss=0.2782, class_loss=0.0884, p_class_loss=0.0925, re_loss=0.0297, conf_loss=0.0000, time=15sec
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+ [i] iteration=4,158, learning_rate=0.0613, alpha=3.33, loss=0.2727, class_loss=0.0879, p_class_loss=0.0918, re_loss=0.0280, conf_loss=0.0000, time=15sec
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+ [i] iteration=4,224, learning_rate=0.0607, alpha=3.38, loss=0.2739, class_loss=0.0872, p_class_loss=0.0913, re_loss=0.0282, conf_loss=0.0000, time=15sec
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+ [i] iteration=4,290, learning_rate=0.0601, alpha=3.43, loss=0.2813, class_loss=0.0892, p_class_loss=0.0932, re_loss=0.0288, conf_loss=0.0000, time=15sec
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+ [i] iteration=4,356, learning_rate=0.0594, alpha=3.49, loss=0.2800, class_loss=0.0912, p_class_loss=0.0951, re_loss=0.0269, conf_loss=0.0000, time=15sec
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+ [i] iteration=4,422, learning_rate=0.0588, alpha=3.54, loss=0.2824, class_loss=0.0909, p_class_loss=0.0948, re_loss=0.0273, conf_loss=0.0000, time=15sec
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+ [i] iteration=4,488, learning_rate=0.0581, alpha=3.59, loss=0.2726, class_loss=0.0877, p_class_loss=0.0913, re_loss=0.0261, conf_loss=0.0000, time=15sec
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+ [i] iteration=4,554, learning_rate=0.0575, alpha=3.65, loss=0.2724, class_loss=0.0873, p_class_loss=0.0913, re_loss=0.0257, conf_loss=0.0000, time=15sec
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+ [i] iteration=4,620, learning_rate=0.0569, alpha=3.70, loss=0.2819, class_loss=0.0911, p_class_loss=0.0948, re_loss=0.0259, conf_loss=0.0000, time=15sec
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+ [i] iteration=4,627, threshold=0.10, train_mIoU=45.99%, best_train_mIoU=46.22%, time=17sec
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