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checkpoints/diffusion_specialized_302_dataset_base_from_dino_780_new_dataset_config_11H47/wandb/run-20260520_100113-rnm5uhtp/files/output.log

@@ -762,4 +762,235 @@ Training:  59%|████████████████████
 Training:  59%|█████████████████████▎              | 14780/25000 [1:09:15<35:28,  4.80step/s]INFO 2026-05-20 11:10:33 ot_train.py:488 step:15K smpl:2M ep:3K epch:10.43 loss:0.008 grdn:0.142 lr:1.8e-06 updt_s:0.110 data_s:0.161
 Training:  59%|████████████████████▏             | 14800/25000 [1:09:22<1:12:31,  2.34step/s]INFO 2026-05-20 11:10:39 ot_train.py:488 step:15K smpl:2M ep:3K epch:10.45 loss:0.007 grdn:0.128 lr:1.8e-06 updt_s:0.106 data_s:0.216
 Training:  59%|█████████████████████▎              | 14820/25000 [1:09:27<31:18,  5.42step/s]INFO 2026-05-20 11:10:44 ot_train.py:488 step:15K smpl:2M ep:3K epch:10.46 loss:0.009 grdn:0.157 lr:1.8e-06 updt_s:0.109 data_s:0.144
-Training:  59%|█████████████████████▎              | 14830/25000 [1:09:29<30:06,  5.63step/s]
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+Training:  63%|██████████████████████▋             | 15760/25000 [1:13:43<27:26,  5.61step/s]INFO 2026-05-20 11:15:01 ot_train.py:488 step:16K smpl:2M ep:3K epch:11.13 loss:0.008 grdn:0.143 lr:1.5e-06 updt_s:0.117 data_s:0.124
+Training:  63%|██████████████████████▋             | 15780/25000 [1:13:49<45:46,  3.36step/s]INFO 2026-05-20 11:15:07 ot_train.py:488 step:16K smpl:2M ep:3K epch:11.14 loss:0.008 grdn:0.144 lr:1.5e-06 updt_s:0.112 data_s:0.181
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+Training:  63%|██████████████████████▊             | 15840/25000 [1:14:05<25:58,  5.88step/s]INFO 2026-05-20 11:15:23 ot_train.py:488 step:16K smpl:2M ep:3K epch:11.18 loss:0.009 grdn:0.148 lr:1.5e-06 updt_s:0.108 data_s:0.126
+Training:  63%|██████████████████████▊             | 15860/25000 [1:14:11<45:58,  3.31step/s]INFO 2026-05-20 11:15:29 ot_train.py:488 step:16K smpl:2M ep:3K epch:11.20 loss:0.008 grdn:0.140 lr:1.5e-06 updt_s:0.111 data_s:0.191
+Training:  64%|██████████████████████▊             | 15880/25000 [1:14:16<25:54,  5.87step/s]INFO 2026-05-20 11:15:34 ot_train.py:488 step:16K smpl:2M ep:3K epch:11.21 loss:0.008 grdn:0.149 lr:1.5e-06 updt_s:0.110 data_s:0.136
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+Training:  64%|██████████████████████▉             | 15940/25000 [1:14:33<42:15,  3.57step/s]INFO 2026-05-20 11:15:50 ot_train.py:488 step:16K smpl:2M ep:3K epch:11.25 loss:0.007 grdn:0.136 lr:1.5e-06 updt_s:0.110 data_s:0.180
+Training:  64%|██████████████████████▉             | 15960/25000 [1:14:38<25:27,  5.92step/s]INFO 2026-05-20 11:15:55 ot_train.py:488 step:16K smpl:2M ep:3K epch:11.27 loss:0.008 grdn:0.145 lr:1.5e-06 updt_s:0.115 data_s:0.132
+Training:  64%|███████████████████████             | 15980/25000 [1:14:44<49:28,  3.04step/s]INFO 2026-05-20 11:16:01 ot_train.py:488 step:16K smpl:2M ep:3K epch:11.28 loss:0.007 grdn:0.135 lr:1.5e-06 updt_s:0.116 data_s:0.187
+Training:  64%|███████████████████████             | 16000/25000 [1:14:49<26:16,  5.71step/s]INFO 2026-05-20 11:16:07 ot_train.py:488 step:16K smpl:2M ep:3K epch:11.30 loss:0.008 grdn:0.144 lr:1.4e-06 updt_s:0.112 data_s:0.142
+INFO 2026-05-20 11:16:07 ot_train.py:502 Checkpoint policy after step 16000
+Training:  64%|███████████████████████             | 16020/25000 [1:15:02<18:15,  8.20step/s]INFO 2026-05-20 11:16:20 ot_train.py:488 step:16K smpl:2M ep:3K epch:11.31 loss:0.008 grdn:0.126 lr:1.4e-06 updt_s:0.104 data_s:0.010
+Training:  64%|███████████████████████             | 16040/25000 [1:15:05<17:09,  8.70step/s]INFO 2026-05-20 11:16:22 ot_train.py:488 step:16K smpl:2M ep:3K epch:11.32 loss:0.008 grdn:0.147 lr:1.4e-06 updt_s:0.110 data_s:0.010
+Training:  64%|███████████████████████▏            | 16060/25000 [1:15:10<50:35,  2.95step/s]INFO 2026-05-20 11:16:28 ot_train.py:488 step:16K smpl:2M ep:3K epch:11.34 loss:0.008 grdn:0.139 lr:1.4e-06 updt_s:0.109 data_s:0.149
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+Training:  64%|███████████████████████▏            | 16100/25000 [1:15:21<53:38,  2.77step/s]INFO 2026-05-20 11:16:38 ot_train.py:488 step:16K smpl:2M ep:3K epch:11.37 loss:0.008 grdn:0.138 lr:1.4e-06 updt_s:0.106 data_s:0.206
+Training:  64%|███████████████████████▏            | 16120/25000 [1:15:26<25:14,  5.86step/s]INFO 2026-05-20 11:16:43 ot_train.py:488 step:16K smpl:2M ep:3K epch:11.38 loss:0.008 grdn:0.136 lr:1.4e-06 updt_s:0.102 data_s:0.139
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+Training:  65%|███████████████████████▎            | 16220/25000 [1:15:54<49:21,  2.96step/s]INFO 2026-05-20 11:17:12 ot_train.py:488 step:16K smpl:2M ep:3K epch:11.45 loss:0.008 grdn:0.151 lr:1.4e-06 updt_s:0.107 data_s:0.203
+Training:  65%|███████████████████████▍            | 16240/25000 [1:15:59<26:22,  5.54step/s]INFO 2026-05-20 11:17:17 ot_train.py:488 step:16K smpl:2M ep:3K epch:11.47 loss:0.008 grdn:0.152 lr:1.4e-06 updt_s:0.113 data_s:0.134
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+Training:  65%|███████████████████████▍            | 16280/25000 [1:16:10<24:41,  5.89step/s]INFO 2026-05-20 11:17:28 ot_train.py:488 step:16K smpl:2M ep:3K epch:11.49 loss:0.008 grdn:0.138 lr:1.4e-06 updt_s:0.115 data_s:0.130
+Training:  65%|███████████████████████▍            | 16300/25000 [1:16:17<50:31,  2.87step/s]INFO 2026-05-20 11:17:34 ot_train.py:488 step:16K smpl:2M ep:3K epch:11.51 loss:0.008 grdn:0.136 lr:1.4e-06 updt_s:0.112 data_s:0.200
+Training:  65%|███████████████████████▌            | 16320/25000 [1:16:21<23:13,  6.23step/s]INFO 2026-05-20 11:17:39 ot_train.py:488 step:16K smpl:2M ep:3K epch:11.52 loss:0.009 grdn:0.141 lr:1.4e-06 updt_s:0.110 data_s:0.131
+Training:  65%|███████████████████████▌            | 16340/25000 [1:16:28<45:17,  3.19step/s]INFO 2026-05-20 11:17:45 ot_train.py:488 step:16K smpl:2M ep:3K epch:11.54 loss:0.007 grdn:0.134 lr:1.4e-06 updt_s:0.112 data_s:0.201
+Training:  65%|███████████████████████▌            | 16360/25000 [1:16:33<23:50,  6.04step/s]INFO 2026-05-20 11:17:50 ot_train.py:488 step:16K smpl:2M ep:3K epch:11.55 loss:0.008 grdn:0.137 lr:1.3e-06 updt_s:0.108 data_s:0.135
+Training:  66%|███████████████████████▌            | 16380/25000 [1:16:39<50:56,  2.82step/s]INFO 2026-05-20 11:17:56 ot_train.py:488 step:16K smpl:2M ep:3K epch:11.56 loss:0.008 grdn:0.129 lr:1.3e-06 updt_s:0.111 data_s:0.207
+Training:  66%|███████████████████████▌            | 16400/25000 [1:16:44<23:41,  6.05step/s]INFO 2026-05-20 11:18:01 ot_train.py:488 step:16K smpl:2M ep:3K epch:11.58 loss:0.008 grdn:0.142 lr:1.3e-06 updt_s:0.109 data_s:0.139
+Training:  66%|███████████████████████▋            | 16420/25000 [1:16:50<44:06,  3.24step/s]INFO 2026-05-20 11:18:07 ot_train.py:488 step:16K smpl:2M ep:4K epch:11.59 loss:0.008 grdn:0.130 lr:1.3e-06 updt_s:0.114 data_s:0.189
+Training:  66%|███████████████████████▋            | 16440/25000 [1:16:55<23:47,  6.00step/s]INFO 2026-05-20 11:18:12 ot_train.py:488 step:16K smpl:2M ep:4K epch:11.61 loss:0.008 grdn:0.138 lr:1.3e-06 updt_s:0.116 data_s:0.125
+Training:  66%|███████████████████████▋            | 16460/25000 [1:17:01<43:34,  3.27step/s]INFO 2026-05-20 11:18:18 ot_train.py:488 step:16K smpl:2M ep:4K epch:11.62 loss:0.008 grdn:0.129 lr:1.3e-06 updt_s:0.112 data_s:0.194
+Training:  66%|███████████████████████▋            | 16480/25000 [1:17:06<24:55,  5.70step/s]INFO 2026-05-20 11:18:23 ot_train.py:488 step:16K smpl:2M ep:4K epch:11.63 loss:0.008 grdn:0.146 lr:1.3e-06 updt_s:0.116 data_s:0.131
+Training:  66%|███████████████████████▊            | 16500/25000 [1:17:12<48:18,  2.93step/s]INFO 2026-05-20 11:18:30 ot_train.py:488 step:16K smpl:2M ep:4K epch:11.65 loss:0.008 grdn:0.133 lr:1.3e-06 updt_s:0.112 data_s:0.210
+Training:  66%|███████████████████████▊            | 16520/25000 [1:17:17<25:22,  5.57step/s]INFO 2026-05-20 11:18:35 ot_train.py:488 step:17K smpl:2M ep:4K epch:11.66 loss:0.008 grdn:0.136 lr:1.3e-06 updt_s:0.109 data_s:0.142
+Training:  66%|███████████████████████▊            | 16540/25000 [1:17:23<46:03,  3.06step/s]INFO 2026-05-20 11:18:41 ot_train.py:488 step:17K smpl:2M ep:4K epch:11.68 loss:0.009 grdn:0.146 lr:1.3e-06 updt_s:0.111 data_s:0.189
+Training:  66%|███████████████████████▊            | 16560/25000 [1:17:28<23:26,  6.00step/s]INFO 2026-05-20 11:18:46 ot_train.py:488 step:17K smpl:2M ep:4K epch:11.69 loss:0.008 grdn:0.145 lr:1.3e-06 updt_s:0.110 data_s:0.136
+Training:  66%|███████████████████████▉            | 16580/25000 [1:17:35<47:38,  2.95step/s]INFO 2026-05-20 11:18:52 ot_train.py:488 step:17K smpl:2M ep:4K epch:11.71 loss:0.007 grdn:0.130 lr:1.3e-06 updt_s:0.116 data_s:0.192
+Training:  66%|███████████████████████▉            | 16600/25000 [1:17:39<23:05,  6.06step/s]INFO 2026-05-20 11:18:57 ot_train.py:488 step:17K smpl:2M ep:4K epch:11.72 loss:0.008 grdn:0.140 lr:1.3e-06 updt_s:0.109 data_s:0.129
+Training:  66%|███████████████████████▉            | 16620/25000 [1:17:46<45:07,  3.10step/s]INFO 2026-05-20 11:19:03 ot_train.py:488 step:17K smpl:2M ep:4K epch:11.73 loss:0.007 grdn:0.130 lr:1.3e-06 updt_s:0.112 data_s:0.191
+Training:  67%|███████████████████████▉            | 16640/25000 [1:17:51<23:08,  6.02step/s]INFO 2026-05-20 11:19:08 ot_train.py:488 step:17K smpl:2M ep:4K epch:11.75 loss:0.008 grdn:0.145 lr:1.3e-06 updt_s:0.110 data_s:0.144
+Training:  67%|███████████████████████▉            | 16660/25000 [1:17:57<43:26,  3.20step/s]INFO 2026-05-20 11:19:14 ot_train.py:488 step:17K smpl:2M ep:4K epch:11.76 loss:0.008 grdn:0.142 lr:1.3e-06 updt_s:0.114 data_s:0.179
+Training:  67%|████████████████████████            | 16680/25000 [1:18:01<23:14,  5.97step/s]INFO 2026-05-20 11:19:19 ot_train.py:488 step:17K smpl:2M ep:4K epch:11.78 loss:0.008 grdn:0.143 lr:1.3e-06 updt_s:0.112 data_s:0.132
+Training:  67%|████████████████████████            | 16700/25000 [1:18:08<43:44,  3.16step/s]INFO 2026-05-20 11:19:25 ot_train.py:488 step:17K smpl:2M ep:4K epch:11.79 loss:0.008 grdn:0.154 lr:1.3e-06 updt_s:0.112 data_s:0.192
+Training:  67%|████████████████████████            | 16720/25000 [1:18:12<23:09,  5.96step/s]INFO 2026-05-20 11:19:30 ot_train.py:488 step:17K smpl:2M ep:4K epch:11.80 loss:0.008 grdn:0.127 lr:1.2e-06 updt_s:0.113 data_s:0.126
+Training:  67%|████████████████████████            | 16740/25000 [1:18:18<41:48,  3.29step/s]INFO 2026-05-20 11:19:36 ot_train.py:488 step:17K smpl:2M ep:4K epch:11.82 loss:0.007 grdn:0.139 lr:1.2e-06 updt_s:0.113 data_s:0.186
+Training:  67%|████████████████████████▏           | 16760/25000 [1:18:23<22:13,  6.18step/s]INFO 2026-05-20 11:19:41 ot_train.py:488 step:17K smpl:2M ep:4K epch:11.83 loss:0.008 grdn:0.144 lr:1.2e-06 updt_s:0.110 data_s:0.135
+Training:  67%|████████████████████████▏           | 16780/25000 [1:18:29<38:27,  3.56step/s]INFO 2026-05-20 11:19:47 ot_train.py:488 step:17K smpl:2M ep:4K epch:11.85 loss:0.008 grdn:0.147 lr:1.2e-06 updt_s:0.111 data_s:0.187
+Training:  67%|████████████████████████▏           | 16800/25000 [1:18:34<20:19,  6.72step/s]INFO 2026-05-20 11:19:51 ot_train.py:488 step:17K smpl:2M ep:4K epch:11.86 loss:0.008 grdn:0.148 lr:1.2e-06 updt_s:0.102 data_s:0.119
+Training:  67%|████████████████████████▏           | 16820/25000 [1:18:42<30:56,  4.41step/s]INFO 2026-05-20 11:19:59 ot_train.py:488 step:17K smpl:2M ep:4K epch:11.87 loss:0.008 grdn:0.145 lr:1.2e-06 updt_s:0.107 data_s:0.282
+Training:  67%|████████████████████████▏           | 16840/25000 [1:18:47<49:56,  2.72step/s]INFO 2026-05-20 11:20:05 ot_train.py:488 step:17K smpl:2M ep:4K epch:11.89 loss:0.008 grdn:0.137 lr:1.2e-06 updt_s:0.110 data_s:0.177
+Training:  67%|████████████████████████▎           | 16860/25000 [1:18:53<34:51,  3.89step/s]INFO 2026-05-20 11:20:10 ot_train.py:488 step:17K smpl:2M ep:4K epch:11.90 loss:0.008 grdn:0.153 lr:1.2e-06 updt_s:0.106 data_s:0.170
+Training:  68%|████████████████████████▎           | 16880/25000 [1:18:58<45:44,  2.96step/s]INFO 2026-05-20 11:20:16 ot_train.py:488 step:17K smpl:2M ep:4K epch:11.92 loss:0.007 grdn:0.134 lr:1.2e-06 updt_s:0.106 data_s:0.166
+Training:  68%|████████████████████████▎           | 16900/25000 [1:19:04<39:19,  3.43step/s]INFO 2026-05-20 11:20:21 ot_train.py:488 step:17K smpl:2M ep:4K epch:11.93 loss:0.008 grdn:0.145 lr:1.2e-06 updt_s:0.106 data_s:0.176
+Training:  68%|████████████████████████▎           | 16920/25000 [1:19:09<26:44,  5.03step/s]INFO 2026-05-20 11:20:26 ot_train.py:488 step:17K smpl:2M ep:4K epch:11.95 loss:0.007 grdn:0.127 lr:1.2e-06 updt_s:0.112 data_s:0.138
+Training:  68%|████████████████████████▍           | 16940/25000 [1:19:15<39:32,  3.40step/s]INFO 2026-05-20 11:20:32 ot_train.py:488 step:17K smpl:2M ep:4K epch:11.96 loss:0.008 grdn:0.138 lr:1.2e-06 updt_s:0.111 data_s:0.183
+Training:  68%|████████████████████████▍           | 16960/25000 [1:19:20<33:25,  4.01step/s]INFO 2026-05-20 11:20:37 ot_train.py:488 step:17K smpl:2M ep:4K epch:11.97 loss:0.008 grdn:0.140 lr:1.2e-06 updt_s:0.105 data_s:0.147
+Training:  68%|████████████████████████▍           | 16980/25000 [1:19:26<39:01,  3.42step/s]INFO 2026-05-20 11:20:43 ot_train.py:488 step:17K smpl:2M ep:4K epch:11.99 loss:0.008 grdn:0.142 lr:1.2e-06 updt_s:0.107 data_s:0.183
+Training:  68%|████████████████████████▍           | 17000/25000 [1:19:31<22:57,  5.81step/s]INFO 2026-05-20 11:20:48 ot_train.py:488 step:17K smpl:2M ep:4K epch:12.00 loss:0.008 grdn:0.140 lr:1.2e-06 updt_s:0.111 data_s:0.134
+Training:  68%|████████████████████████▌           | 17020/25000 [1:19:37<40:37,  3.27step/s]INFO 2026-05-20 11:20:54 ot_train.py:488 step:17K smpl:2M ep:4K epch:12.02 loss:0.008 grdn:0.142 lr:1.2e-06 updt_s:0.107 data_s:0.202
+Training:  68%|████████████████████████▌           | 17040/25000 [1:19:42<22:08,  5.99step/s]INFO 2026-05-20 11:20:59 ot_train.py:488 step:17K smpl:2M ep:4K epch:12.03 loss:0.008 grdn:0.133 lr:1.2e-06 updt_s:0.117 data_s:0.130
+Training:  68%|████████████████████████▌           | 17060/25000 [1:19:48<41:40,  3.18step/s]INFO 2026-05-20 11:21:06 ot_train.py:488 step:17K smpl:2M ep:4K epch:12.04 loss:0.008 grdn:0.141 lr:1.2e-06 updt_s:0.112 data_s:0.201
+Training:  68%|████████████████████████▌           | 17080/25000 [1:19:53<20:40,  6.38step/s]INFO 2026-05-20 11:21:10 ot_train.py:488 step:17K smpl:2M ep:4K epch:12.06 loss:0.008 grdn:0.139 lr:1.2e-06 updt_s:0.112 data_s:0.126
+Training:  68%|████████████████████████▌           | 17100/25000 [1:19:59<40:33,  3.25step/s]INFO 2026-05-20 11:21:16 ot_train.py:488 step:17K smpl:2M ep:4K epch:12.07 loss:0.008 grdn:0.140 lr:1.1e-06 updt_s:0.113 data_s:0.185
+Training:  68%|████████████████████████▋           | 17120/25000 [1:20:04<21:21,  6.15step/s]INFO 2026-05-20 11:21:21 ot_train.py:488 step:17K smpl:2M ep:4K epch:12.09 loss:0.008 grdn:0.142 lr:1.1e-06 updt_s:0.114 data_s:0.129
+Training:  69%|████████████████████████▋           | 17140/25000 [1:20:10<42:04,  3.11step/s]INFO 2026-05-20 11:21:27 ot_train.py:488 step:17K smpl:2M ep:4K epch:12.10 loss:0.008 grdn:0.137 lr:1.1e-06 updt_s:0.118 data_s:0.181
+Training:  69%|████████████████████████▋           | 17160/25000 [1:20:15<21:25,  6.10step/s]INFO 2026-05-20 11:21:32 ot_train.py:488 step:17K smpl:2M ep:4K epch:12.11 loss:0.007 grdn:0.131 lr:1.1e-06 updt_s:0.117 data_s:0.123
+Training:  69%|████████████████████████▋           | 17180/25000 [1:20:21<40:43,  3.20step/s]INFO 2026-05-20 11:21:38 ot_train.py:488 step:17K smpl:2M ep:4K epch:12.13 loss:0.008 grdn:0.133 lr:1.1e-06 updt_s:0.113 data_s:0.193
+Training:  69%|████████████████████████▊           | 17200/25000 [1:20:26<22:16,  5.84step/s]INFO 2026-05-20 11:21:43 ot_train.py:488 step:17K smpl:2M ep:4K epch:12.14 loss:0.007 grdn:0.129 lr:1.1e-06 updt_s:0.106 data_s:0.151
+Training:  69%|████████████████████████▊           | 17220/25000 [1:20:32<42:06,  3.08step/s]INFO 2026-05-20 11:21:50 ot_train.py:488 step:17K smpl:2M ep:4K epch:12.16 loss:0.008 grdn:0.141 lr:1.1e-06 updt_s:0.106 data_s:0.200
+Training:  69%|████████████████████████▊           | 17240/25000 [1:20:37<20:34,  6.29step/s]INFO 2026-05-20 11:21:54 ot_train.py:488 step:17K smpl:2M ep:4K epch:12.17 loss:0.008 grdn:0.147 lr:1.1e-06 updt_s:0.104 data_s:0.133
+Training:  69%|████████████████████████▊           | 17260/25000 [1:20:43<40:13,  3.21step/s]INFO 2026-05-20 11:22:00 ot_train.py:488 step:17K smpl:2M ep:4K epch:12.19 loss:0.008 grdn:0.136 lr:1.1e-06 updt_s:0.102 data_s:0.203
+Training:  69%|████████████████████████▉           | 17280/25000 [1:20:48<20:57,  6.14step/s]INFO 2026-05-20 11:22:05 ot_train.py:488 step:17K smpl:2M ep:4K epch:12.20 loss:0.008 grdn:0.135 lr:1.1e-06 updt_s:0.102 data_s:0.143
+Training:  69%|████████████████████████▉           | 17300/25000 [1:20:54<38:49,  3.31step/s]INFO 2026-05-20 11:22:12 ot_train.py:488 step:17K smpl:2M ep:4K epch:12.21 loss:0.008 grdn:0.135 lr:1.1e-06 updt_s:0.104 data_s:0.204
+Training:  69%|████████████████████████▉           | 17320/25000 [1:20:59<22:38,  5.65step/s]INFO 2026-05-20 11:22:16 ot_train.py:488 step:17K smpl:2M ep:4K epch:12.23 loss:0.008 grdn:0.127 lr:1.1e-06 updt_s:0.107 data_s:0.138
+Training:  69%|████████████████████████▉           | 17340/25000 [1:21:05<39:26,  3.24step/s]INFO 2026-05-20 11:22:23 ot_train.py:488 step:17K smpl:2M ep:4K epch:12.24 loss:0.009 grdn:0.141 lr:1.1e-06 updt_s:0.106 data_s:0.200
+Training:  69%|██████████████████████��█▉           | 17360/25000 [1:21:10<23:03,  5.52step/s]INFO 2026-05-20 11:22:28 ot_train.py:488 step:17K smpl:2M ep:4K epch:12.26 loss:0.008 grdn:0.137 lr:1.1e-06 updt_s:0.111 data_s:0.134
+Training:  70%|█████████████████████████           | 17380/25000 [1:21:16<38:51,  3.27step/s]INFO 2026-05-20 11:22:34 ot_train.py:488 step:17K smpl:2M ep:4K epch:12.27 loss:0.008 grdn:0.140 lr:1.1e-06 updt_s:0.111 data_s:0.197
+Training:  70%|█████████████████████████           | 17400/25000 [1:21:21<21:54,  5.78step/s]INFO 2026-05-20 11:22:39 ot_train.py:488 step:17K smpl:2M ep:4K epch:12.28 loss:0.008 grdn:0.135 lr:1.1e-06 updt_s:0.111 data_s:0.139
+Training:  70%|█████████████████████████           | 17420/25000 [1:21:28<38:15,  3.30step/s]INFO 2026-05-20 11:22:45 ot_train.py:488 step:17K smpl:2M ep:4K epch:12.30 loss:0.008 grdn:0.143 lr:1.1e-06 updt_s:0.120 data_s:0.191
+Training:  70%|█████████████████████████           | 17440/25000 [1:21:33<23:20,  5.40step/s]INFO 2026-05-20 11:22:50 ot_train.py:488 step:17K smpl:2M ep:4K epch:12.31 loss:0.008 grdn:0.147 lr:1.1e-06 updt_s:0.122 data_s:0.132
+Training:  70%|█████████████████████████▏          | 17460/25000 [1:21:39<42:22,  2.97step/s]INFO 2026-05-20 11:22:56 ot_train.py:488 step:17K smpl:2M ep:4K epch:12.33 loss:0.008 grdn:0.141 lr:1.1e-06 updt_s:0.115 data_s:0.186
+Training:  70%|█████████████████████████▏          | 17480/25000 [1:21:44<22:38,  5.53step/s]INFO 2026-05-20 11:23:01 ot_train.py:488 step:17K smpl:2M ep:4K epch:12.34 loss:0.008 grdn:0.133 lr:1.0e-06 updt_s:0.115 data_s:0.129
+Training:  70%|█████████████████████████▏          | 17500/25000 [1:21:50<38:35,  3.24step/s]INFO 2026-05-20 11:23:07 ot_train.py:488 step:18K smpl:2M ep:4K epch:12.35 loss:0.008 grdn:0.143 lr:1.0e-06 updt_s:0.118 data_s:0.184
+Training:  70%|█████████████████████████▏          | 17520/25000 [1:21:55<21:52,  5.70step/s]INFO 2026-05-20 11:23:12 ot_train.py:488 step:18K smpl:2M ep:4K epch:12.37 loss:0.008 grdn:0.132 lr:1.0e-06 updt_s:0.117 data_s:0.126
+Training:  70%|█████████████████████████▎          | 17540/25000 [1:22:01<37:50,  3.28step/s]INFO 2026-05-20 11:23:18 ot_train.py:488 step:18K smpl:2M ep:4K epch:12.38 loss:0.008 grdn:0.151 lr:1.0e-06 updt_s:0.118 data_s:0.193
+Training:  70%|█████████████████████████▎          | 17560/25000 [1:22:06<21:59,  5.64step/s]INFO 2026-05-20 11:23:23 ot_train.py:488 step:18K smpl:2M ep:4K epch:12.40 loss:0.008 grdn:0.145 lr:1.0e-06 updt_s:0.115 data_s:0.136
+Training:  70%|█████████████████████████▎          | 17580/25000 [1:22:12<38:36,  3.20step/s]INFO 2026-05-20 11:23:29 ot_train.py:488 step:18K smpl:2M ep:4K epch:12.41 loss:0.008 grdn:0.139 lr:1.0e-06 updt_s:0.107 data_s:0.190
+Training:  70%|█████████████████████████▎          | 17600/25000 [1:22:17<20:52,  5.91step/s]INFO 2026-05-20 11:23:34 ot_train.py:488 step:18K smpl:2M ep:4K epch:12.43 loss:0.008 grdn:0.142 lr:1.0e-06 updt_s:0.113 data_s:0.129
+Training:  70%|█████████████████████████▎          | 17620/25000 [1:22:23<38:33,  3.19step/s]INFO 2026-05-20 11:23:40 ot_train.py:488 step:18K smpl:2M ep:4K epch:12.44 loss:0.008 grdn:0.137 lr:1.0e-06 updt_s:0.109 data_s:0.201
+Training:  71%|█████████████████████████▍          | 17640/25000 [1:22:28<19:30,  6.29step/s]INFO 2026-05-20 11:23:45 ot_train.py:488 step:18K smpl:2M ep:4K epch:12.45 loss:0.007 grdn:0.124 lr:1.0e-06 updt_s:0.108 data_s:0.129
+Training:  71%|█████████████████████████▍          | 17660/25000 [1:22:34<39:26,  3.10step/s]INFO 2026-05-20 11:23:51 ot_train.py:488 step:18K smpl:2M ep:4K epch:12.47 loss:0.008 grdn:0.138 lr:1.0e-06 updt_s:0.105 data_s:0.208
+Training:  71%|█████████████████████████▍          | 17680/25000 [1:22:39<20:57,  5.82step/s]INFO 2026-05-20 11:23:56 ot_train.py:488 step:18K smpl:2M ep:4K epch:12.48 loss:0.008 grdn:0.140 lr:9.9e-07 updt_s:0.110 data_s:0.135
+Training:  71%|█████████████████████████▍          | 17700/25000 [1:22:45<38:17,  3.18step/s]INFO 2026-05-20 11:24:02 ot_train.py:488 step:18K smpl:2M ep:4K epch:12.50 loss:0.008 grdn:0.137 lr:9.9e-07 updt_s:0.111 data_s:0.190
+Training:  71%|█████████████████████████▌          | 17720/25000 [1:22:50<21:26,  5.66step/s]INFO 2026-05-20 11:24:07 ot_train.py:488 step:18K smpl:2M ep:4K epch:12.51 loss:0.008 grdn:0.142 lr:9.8e-07 updt_s:0.112 data_s:0.142
+Training:  71%|█████████████████████████▌          | 17740/25000 [1:22:56<37:48,  3.20step/s]INFO 2026-05-20 11:24:13 ot_train.py:488 step:18K smpl:2M ep:4K epch:12.52 loss:0.008 grdn:0.134 lr:9.8e-07 updt_s:0.110 data_s:0.185
+Training:  71%|█████████████████████████▌          | 17760/25000 [1:23:01<21:11,  5.69step/s]INFO 2026-05-20 11:24:18 ot_train.py:488 step:18K smpl:2M ep:4K epch:12.54 loss:0.008 grdn:0.137 lr:9.7e-07 updt_s:0.116 data_s:0.126
+Training:  71%|█████████████████████████▌          | 17780/25000 [1:23:07<36:49,  3.27step/s]INFO 2026-05-20 11:24:24 ot_train.py:488 step:18K smpl:2M ep:4K epch:12.55 loss:0.008 grdn:0.153 lr:9.7e-07 updt_s:0.114 data_s:0.188
+Training:  71%|█████████████████████████▋          | 17800/25000 [1:23:12<22:22,  5.36step/s]INFO 2026-05-20 11:24:29 ot_train.py:488 step:18K smpl:2M ep:4K epch:12.57 loss:0.008 grdn:0.149 lr:9.6e-07 updt_s:0.117 data_s:0.129
+Training:  71%|█████████████████████████▋          | 17820/25000 [1:23:18<39:52,  3.00step/s]INFO 2026-05-20 11:24:35 ot_train.py:488 step:18K smpl:2M ep:4K epch:12.58 loss:0.008 grdn:0.149 lr:9.6e-07 updt_s:0.122 data_s:0.183
+Training:  71%|█████████████████████████▋          | 17840/25000 [1:23:23<21:41,  5.50step/s]INFO 2026-05-20 11:24:40 ot_train.py:488 step:18K smpl:2M ep:4K epch:12.59 loss:0.007 grdn:0.129 lr:9.5e-07 updt_s:0.117 data_s:0.126
+Training:  71%|█████████████████████████▋          | 17860/25000 [1:23:29<36:06,  3.30step/s]INFO 2026-05-20 11:24:46 ot_train.py:488 step:18K smpl:2M ep:4K epch:12.61 loss:0.008 grdn:0.143 lr:9.5e-07 updt_s:0.122 data_s:0.178
+Training:  72%|█████████████████████████▋          | 17880/25000 [1:23:34<19:55,  5.96step/s]INFO 2026-05-20 11:24:51 ot_train.py:488 step:18K smpl:2M ep:4K epch:12.62 loss:0.008 grdn:0.132 lr:9.5e-07 updt_s:0.120 data_s:0.119
+Training:  72%|█████████████████████████▊          | 17900/25000 [1:23:40<36:11,  3.27step/s]INFO 2026-05-20 11:24:57 ot_train.py:488 step:18K smpl:2M ep:4K epch:12.64 loss:0.008 grdn:0.153 lr:9.4e-07 updt_s:0.122 data_s:0.186
+Training:  72%|█████████████████████████▊          | 17920/25000 [1:23:45<22:21,  5.28step/s]INFO 2026-05-20 11:25:02 ot_train.py:488 step:18K smpl:2M ep:4K epch:12.65 loss:0.008 grdn:0.137 lr:9.4e-07 updt_s:0.124 data_s:0.129
+Training:  72%|█████████████████████████▊          | 17940/25000 [1:23:51<35:15,  3.34step/s]INFO 2026-05-20 11:25:08 ot_train.py:488 step:18K smpl:2M ep:4K epch:12.67 loss:0.008 grdn:0.140 lr:9.3e-07 updt_s:0.116 data_s:0.177
+Training:  72%|█████████████████████████▊          | 17960/25000 [1:23:56<21:43,  5.40step/s]INFO 2026-05-20 11:25:13 ot_train.py:488 step:18K smpl:2M ep:4K epch:12.68 loss:0.008 grdn:0.135 lr:9.3e-07 updt_s:0.122 data_s:0.129
+Training:  72%|█████████████████████████▉          | 17980/25000 [1:24:02<36:36,  3.20step/s]INFO 2026-05-20 11:25:19 ot_train.py:488 step:18K smpl:2M ep:4K epch:12.69 loss:0.009 grdn:0.148 lr:9.2e-07 updt_s:0.119 data_s:0.184
+Training:  72%|█████████████████████████▉          | 18000/25000 [1:24:07<20:12,  5.77step/s]INFO 2026-05-20 11:25:24 ot_train.py:488 step:18K smpl:2M ep:4K epch:12.71 loss:0.008 grdn:0.139 lr:9.2e-07 updt_s:0.117 data_s:0.128
+INFO 2026-05-20 11:25:24 ot_train.py:502 Checkpoint policy after step 18000
+Training:  72%|█████████████████████████▉          | 18020/25000 [1:24:19<14:15,  8.16step/s]INFO 2026-05-20 11:25:36 ot_train.py:488 step:18K smpl:2M ep:4K epch:12.72 loss:0.007 grdn:0.139 lr:9.1e-07 updt_s:0.104 data_s:0.010
+Training:  72%|█████████████████████████▉          | 18040/25000 [1:24:22<15:15,  7.60step/s]INFO 2026-05-20 11:25:39 ot_train.py:488 step:18K smpl:2M ep:4K epch:12.74 loss:0.008 grdn:0.142 lr:9.1e-07 updt_s:0.115 data_s:0.012
+Training:  72%|██████████████████████████          | 18060/25000 [1:24:26<33:26,  3.46step/s]INFO 2026-05-20 11:25:44 ot_train.py:488 step:18K smpl:2M ep:4K epch:12.75 loss:0.008 grdn:0.136 lr:9.0e-07 updt_s:0.110 data_s:0.124
+Training:  72%|██████████████████████████          | 18080/25000 [1:24:31<18:37,  6.19step/s]INFO 2026-05-20 11:25:49 ot_train.py:488 step:18K smpl:2M ep:4K epch:12.76 loss:0.007 grdn:0.130 lr:9.0e-07 updt_s:0.106 data_s:0.141
+Training:  72%|██████████████████████████          | 18100/25000 [1:24:37<32:46,  3.51step/s]INFO 2026-05-20 11:25:55 ot_train.py:488 step:18K smpl:2M ep:4K epch:12.78 loss:0.009 grdn:0.157 lr:8.9e-07 updt_s:0.110 data_s:0.186
+Training:  72%|██████████████████████████          | 18120/25000 [1:24:42<18:37,  6.16step/s]INFO 2026-05-20 11:25:59 ot_train.py:488 step:18K smpl:2M ep:4K epch:12.79 loss:0.008 grdn:0.146 lr:8.9e-07 updt_s:0.110 data_s:0.130
+Training:  73%|██████████████████████████          | 18140/25000 [1:24:48<35:15,  3.24step/s]INFO 2026-05-20 11:26:06 ot_train.py:488 step:18K smpl:2M ep:4K epch:12.81 loss:0.008 grdn:0.145 lr:8.8e-07 updt_s:0.113 data_s:0.203
+Training:  73%|██████████████████████████▏         | 18160/25000 [1:24:53<18:29,  6.17step/s]INFO 2026-05-20 11:26:11 ot_train.py:488 step:18K smpl:2M ep:4K epch:12.82 loss:0.008 grdn:0.138 lr:8.8e-07 updt_s:0.106 data_s:0.135
+Training:  73%|██████████████████████████▏         | 18180/25000 [1:24:59<32:08,  3.54step/s]INFO 2026-05-20 11:26:17 ot_train.py:488 step:18K smpl:2M ep:4K epch:12.84 loss:0.008 grdn:0.137 lr:8.7e-07 updt_s:0.107 data_s:0.187
+Training:  73%|██████████████████████████▏         | 18200/25000 [1:25:04<16:50,  6.73step/s]INFO 2026-05-20 11:26:21 ot_train.py:488 step:18K smpl:2M ep:4K epch:12.85 loss:0.008 grdn:0.144 lr:8.7e-07 updt_s:0.104 data_s:0.132
+Training:  73%|██████████████████████████▏         | 18220/25000 [1:25:12<25:01,  4.52step/s]INFO 2026-05-20 11:26:29 ot_train.py:488 step:18K smpl:2M ep:4K epch:12.86 loss:0.008 grdn:0.144 lr:8.6e-07 updt_s:0.106 data_s:0.281
+Training:  73%|██████████████████████████▎         | 18240/25000 [1:25:17<52:37,  2.14step/s]INFO 2026-05-20 11:26:35 ot_train.py:488 step:18K smpl:2M ep:4K epch:12.88 loss:0.008 grdn:0.137 lr:8.6e-07 updt_s:0.105 data_s:0.180
+Training:  73%|██████████████████████████▎         | 18260/25000 [1:25:22<25:42,  4.37step/s]INFO 2026-05-20 11:26:40 ot_train.py:488 step:18K smpl:2M ep:4K epch:12.89 loss:0.008 grdn:0.130 lr:8.5e-07 updt_s:0.109 data_s:0.139
+Training:  73%|██████████████████████████▎         | 18280/25000 [1:25:28<56:42,  1.98step/s]INFO 2026-05-20 11:26:46 ot_train.py:488 step:18K smpl:2M ep:4K epch:12.91 loss:0.009 grdn:0.144 lr:8.5e-07 updt_s:0.107 data_s:0.192
+Training:  73%|██████████████████████████▎         | 18300/25000 [1:25:33<25:32,  4.37step/s]INFO 2026-05-20 11:26:51 ot_train.py:488 step:18K smpl:2M ep:4K epch:12.92 loss:0.008 grdn:0.143 lr:8.4e-07 updt_s:0.105 data_s:0.141
+Training:  73%|██████████████████████████▍         | 18320/25000 [1:25:39<46:23,  2.40step/s]INFO 2026-05-20 11:26:56 ot_train.py:488 step:18K smpl:2M ep:4K epch:12.93 loss:0.008 grdn:0.144 lr:8.4e-07 updt_s:0.104 data_s:0.183
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checkpoints/diffusion_specialized_302_dataset_base_from_dino_780_new_dataset_config_11H47/wandb/run-20260520_100113-rnm5uhtp/logs/debug-internal.log

@@ -550,3 +550,149 @@
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 {"time":"2026-05-20T11:10:40.457400142Z","level":"INFO","msg":"filestream: request sent","status":"200 OK"}
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+{"time":"2026-05-20T11:10:54.789143518Z","level":"INFO","msg":"filestream: request sent","status":"200 OK"}
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+{"time":"2026-05-20T11:11:09.258084902Z","level":"INFO","msg":"filestream: request sent","status":"200 OK"}
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+{"time":"2026-05-20T11:12:09.476064389Z","level":"INFO","msg":"filestream: request sent","status":"200 OK"}
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+{"time":"2026-05-20T11:12:39.565948389Z","level":"INFO","msg":"filestream: request sent","status":"200 OK"}
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+{"time":"2026-05-20T11:12:56.448290363Z","level":"INFO","msg":"filestream: request sent","status":"200 OK"}
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