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2024-07-01 15:02:44,339 - train_voc.py - INFO: Pytorch version: 1.13.1
2024-07-01 15:02:44,373 - train_voc.py - INFO: GPU type: NVIDIA GeForce RTX 3090
2024-07-01 15:02:44,373 - train_voc.py - INFO:
args: Namespace(aux_layer=-3, backbone='matvit_base_patch16_224', backend='nccl', betas=(0.9, 0.999), bkg_thre=0.5, cam2mask=10000, cam_scales=(1.0, 0.5, 0.75, 1.5), ckpt_dir='w_outputs/2024-07/voc_sota_ce_mct_01-15-02-44/checkpoints', crop_size=448, data_folder='/data/Datasets/VOC/VOC2012/', eval_iters=2000, finetune='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_p16_224-80ecf9dd.pth', global_crops_number=2, high_thre=0.7, ignore_index=255, list_folder='datasets/voc', local_crop_size=64, local_crops_number=0, local_rank=0, log_iters=200, log_tag='sota_ce_mct', low_thre=0.25, lr=6e-05, max_iters=20000, model='MATformer', num_attri=24, num_classes=21, num_workers=10, optimizer='PolyWarmupAdamW', pooling='gmp', power=0.9, pred_dir='w_outputs/2024-07/voc_sota_ce_mct_01-15-02-44/predictions', pretrained=True, save_ckpt=True, seed=0, sim=1.0, spg=4, tag_threshold=0.2, tb_dir='w_outputs/2024-07/voc_sota_ce_mct_01-15-02-44/tensorboards', tensorboard=True, text_attri_pt_path='./attributes_text/attribute_embedding/pascal_voc_desc_bge-base-en-v1.5_gpt4.0_cluster_32_embedding_bank.pth', train_set='train_aug', update_prototype=0, use_aa=False, use_gauss=False, use_solar=False, val_set='val', w_cle=0.2, w_ptc=0.3, w_reg=0.05, w_seg=0.12, w_une=0.1, w_var=0.5, warmup_iters=1500, warmup_lr=1e-06, work_dir='w_outputs/2024-07/voc_sota_ce_mct_01-15-02-44', wt_decay=0.01)
2024-07-01 15:02:44,373 - distributed_c10d.py - INFO: Added key: store_based_barrier_key:1 to store for rank: 0
2024-07-01 15:02:44,373 - distributed_c10d.py - INFO: Rank 0: Completed store-based barrier for key:store_based_barrier_key:1 with 1 nodes.
2024-07-01 15:02:44,374 - train_voc.py - INFO: Total gpus: 1, samples per gpu: 4...
2024-07-01 15:02:45,828 - train_voc.py - INFO:
Optimizer:
PolyWarmupAdamW (
Parameter Group 0
amsgrad: False
betas: (0.9, 0.999)
capturable: False
eps: 1e-08
foreach: None
lr: 6e-05
maximize: False
weight_decay: 0.01
Parameter Group 1
amsgrad: False
betas: (0.9, 0.999)
capturable: False
eps: 1e-08
foreach: None
lr: 6e-05
maximize: False
weight_decay: 0.01
Parameter Group 2
amsgrad: False
betas: (0.9, 0.999)
capturable: False
eps: 1e-08
foreach: None
lr: 0.0006000000000000001
maximize: False
weight_decay: 0.01
Parameter Group 3
amsgrad: False
betas: (0.9, 0.999)
capturable: False
eps: 1e-08
foreach: None
lr: 0.0006000000000000001
maximize: False
weight_decay: 0.01
)
2024-07-01 15:05:54,859 - train_voc.py - INFO: Iter: 200; Elasped: 0:03:10; ETA: 5:13:30; LR: 7.960e-06; cls_loss: 0.4121, cls_loss_aux: 10.8427, cls_loss_mct: 2.9957, ptc_loss: 0.4374, cle_loss: 24.8204, une_loss: 0.6275, seg_loss: 3.0513...
2024-07-01 15:09:01,339 - train_voc.py - INFO: Iter: 400; Elasped: 0:06:17; ETA: 5:07:53; LR: 1.596e-05; cls_loss: 0.2457, cls_loss_aux: 0.5004, cls_loss_mct: 2.9957, ptc_loss: 0.4352, cle_loss: 22.9752, une_loss: 0.6232, seg_loss: 3.0595...
2024-07-01 15:12:06,142 - train_voc.py - INFO: Iter: 600; Elasped: 0:09:22; ETA: 5:02:51; LR: 2.396e-05; cls_loss: 0.2423, cls_loss_aux: 0.3197, cls_loss_mct: 2.9957, ptc_loss: 0.4483, cle_loss: 14.8339, une_loss: 0.5900, seg_loss: 3.0569...
2024-07-01 15:15:13,551 - train_voc.py - INFO: Iter: 800; Elasped: 0:12:29; ETA: 4:59:36; LR: 3.196e-05; cls_loss: 0.2397, cls_loss_aux: 0.2822, cls_loss_mct: 2.9957, ptc_loss: 0.4392, cle_loss: 11.1754, une_loss: 0.5639, seg_loss: 3.0611...
2024-07-01 15:18:23,942 - train_voc.py - INFO: Iter: 1000; Elasped: 0:15:39; ETA: 4:57:21; LR: 3.996e-05; cls_loss: 0.2250, cls_loss_aux: 0.2916, cls_loss_mct: 2.9957, ptc_loss: 0.4203, cle_loss: 10.4116, une_loss: 0.5504, seg_loss: 3.0676...
2024-07-01 15:21:36,351 - train_voc.py - INFO: Iter: 1200; Elasped: 0:18:52; ETA: 4:55:34; LR: 4.796e-05; cls_loss: 0.1938, cls_loss_aux: 0.2708, cls_loss_mct: 2.9957, ptc_loss: 0.4040, cle_loss: 10.1553, une_loss: 0.5441, seg_loss: 3.0484...
2024-07-01 15:24:49,486 - train_voc.py - INFO: Iter: 1400; Elasped: 0:22:05; ETA: 4:53:23; LR: 5.596e-05; cls_loss: 0.1643, cls_loss_aux: 0.2607, cls_loss_mct: 2.9956, ptc_loss: 0.3724, cle_loss: 10.8159, une_loss: 0.5386, seg_loss: 2.9667...
2024-07-01 15:28:05,977 - train_voc.py - INFO: Iter: 1600; Elasped: 0:25:21; ETA: 4:51:31; LR: 5.566e-05; cls_loss: 0.1360, cls_loss_aux: 0.2055, cls_loss_mct: 2.9954, ptc_loss: 0.3307, cle_loss: 10.3683, une_loss: 0.5332, seg_loss: 2.8859...
2024-07-01 15:31:26,080 - train_voc.py - INFO: Iter: 1800; Elasped: 0:28:42; ETA: 4:50:11; LR: 5.512e-05; cls_loss: 0.1305, cls_loss_aux: 0.2060, cls_loss_mct: 2.9939, ptc_loss: 0.2964, cle_loss: 11.2300, une_loss: 0.5272, seg_loss: 2.8465...
2024-07-01 15:34:44,945 - train_voc.py - INFO: Iter: 2000; Elasped: 0:32:00; ETA: 4:48:00; LR: 5.457e-05; cls_loss: 0.1125, cls_loss_aux: 0.1842, cls_loss_mct: 2.3390, ptc_loss: 0.2664, cle_loss: 10.3550, une_loss: 0.5132, seg_loss: 2.9630...
2024-07-01 15:34:50,805 - train_voc.py - INFO: Validating...
2024-07-01 15:41:15,712 - train_voc.py - INFO: val cls score: 0.673031
2024-07-01 15:41:15,712 - train_voc.py - INFO:
+-----------------+--------+---------+-----------+--------+----------+---------+
| Class | CAM | aux_CAM | Score Map | Fused | Seg_Pred | Seg_CAM |
+=================+========+=========+===========+========+==========+=========+
| _background_ | 29.289 | 65.630 | 37.787 | 62.473 | 0.500 | 41.190 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| aeroplane | 15.280 | 0.298 | 0.687 | 4.820 | 1.316 | 14.503 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| bicycle | 20.082 | 26.628 | 7.982 | 26.594 | 0.097 | 18.241 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| bird | 14.086 | 22.203 | 22.585 | 26.279 | 0.010 | 13.932 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| boat | 13.855 | 19.057 | 8.662 | 11.546 | 0.391 | 11.471 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| bottle | 37.461 | 31.725 | 26.244 | 37.740 | 0.005 | 20.905 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| bus | 44.080 | 66.522 | 29.287 | 49.390 | 4.460 | 41.577 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| car | 39.606 | 58.014 | 29.570 | 56.616 | 0.001 | 38.952 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| cat | 47.897 | 25.680 | 21.029 | 20.905 | 0.362 | 44.091 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| chair | 13.820 | 3.350 | 7.007 | 10.062 | 0.177 | 7.069 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| cow | 24.945 | 38.358 | 26.281 | 28.360 | 0.008 | 0 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| diningtable | 41.304 | 37.510 | 10.470 | 19.544 | 1.295 | 21.489 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| dog | 33.321 | 42.350 | 26.532 | 35.879 | 0.006 | 27.202 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| horse | 22.255 | 1.556 | 1.729 | 6.597 | 0.225 | 0 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| motorbike | 35.776 | 49.812 | 26.104 | 48.800 | 0.000 | 28.518 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| person | 46.637 | 49.351 | 20.644 | 49.504 | 0.146 | 40.022 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| pottedplant | 20.472 | 44.860 | 24.171 | 38.680 | 0.019 | 20.417 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| sheep | 20.894 | 0.694 | 7.346 | 12.777 | 0.010 | 17.271 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| sofa | 28.888 | 4.237 | 9.546 | 13.530 | 1.245 | 14.320 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| train | 30.156 | 1.502 | 4.469 | 7.861 | 0.438 | 29.162 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| tvmonitor | 25.096 | 31.127 | 24.006 | 28.001 | 0.939 | 26.018 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| m-Precision | 35.000 | 36.386 | 25.519 | 41.370 | 5.046 | nan |
+-----------------+--------+---------+-----------+--------+----------+---------+
| m-Recall | 86.268 | 54.877 | 42.249 | 49.140 | 5.871 | 60.107 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| m-ConfutionRati | 2.809 | 13.426 | 10.125 | 2.845 | 8347.488 | nan |
| o | | | | | | |
+-----------------+--------+---------+-----------+--------+----------+---------+
| m-IoU | 28.819 | 29.546 | 17.721 | 28.379 | 0.555 | 22.683 |
+-----------------+--------+---------+-----------+--------+----------+---------+
2024-07-01 15:44:31,550 - train_voc.py - INFO: Iter: 2200; Elasped: 0:41:47; ETA: 5:38:03; LR: 5.403e-05; cls_loss: 0.1152, cls_loss_aux: 0.1784, cls_loss_mct: 0.3121, ptc_loss: 0.2717, cle_loss: 10.2386, une_loss: 0.4975, seg_loss: 1.6905...
2024-07-01 15:47:52,251 - train_voc.py - INFO: Iter: 2400; Elasped: 0:45:08; ETA: 5:30:58; LR: 5.348e-05; cls_loss: 0.0961, cls_loss_aux: 0.1596, cls_loss_mct: 0.0440, ptc_loss: 0.2426, cle_loss: 9.7664, une_loss: 0.4993, seg_loss: 0.7636...
2024-07-01 15:51:09,752 - train_voc.py - INFO: Iter: 2600; Elasped: 0:48:25; ETA: 5:24:01; LR: 5.293e-05; cls_loss: 0.0960, cls_loss_aux: 0.1809, cls_loss_mct: 0.0284, ptc_loss: 0.2352, cle_loss: 9.6152, une_loss: 0.4992, seg_loss: 0.5650...
2024-07-01 15:54:28,433 - train_voc.py - INFO: Iter: 2800; Elasped: 0:51:44; ETA: 5:17:47; LR: 5.239e-05; cls_loss: 0.0862, cls_loss_aux: 0.1411, cls_loss_mct: 0.0224, ptc_loss: 0.2007, cle_loss: 9.7463, une_loss: 0.4980, seg_loss: 0.3607...
2024-07-01 15:57:45,343 - train_voc.py - INFO: Iter: 3000; Elasped: 0:55:01; ETA: 5:11:45; LR: 5.184e-05; cls_loss: 0.0819, cls_loss_aux: 0.1391, cls_loss_mct: 0.0188, ptc_loss: 0.1904, cle_loss: 9.4141, une_loss: 0.4975, seg_loss: 0.3298...
2024-07-01 16:01:01,800 - train_voc.py - INFO: Iter: 3200; Elasped: 0:58:17; ETA: 5:05:59; LR: 5.129e-05; cls_loss: 0.0941, cls_loss_aux: 0.1542, cls_loss_mct: 0.0122, ptc_loss: 0.1796, cle_loss: 9.4727, une_loss: 0.4963, seg_loss: 0.2806...
2024-07-01 16:04:17,509 - train_voc.py - INFO: Iter: 3400; Elasped: 1:01:33; ETA: 5:00:30; LR: 5.074e-05; cls_loss: 0.0859, cls_loss_aux: 0.1473, cls_loss_mct: 0.0095, ptc_loss: 0.1479, cle_loss: 9.1408, une_loss: 0.4943, seg_loss: 0.2716...
2024-07-01 16:07:33,757 - train_voc.py - INFO: Iter: 3600; Elasped: 1:04:49; ETA: 4:55:16; LR: 5.019e-05; cls_loss: 0.0718, cls_loss_aux: 0.1277, cls_loss_mct: 0.0083, ptc_loss: 0.1378, cle_loss: 9.0570, une_loss: 0.4934, seg_loss: 0.1928...
2024-07-01 16:10:49,205 - train_voc.py - INFO: Iter: 3800; Elasped: 1:08:05; ETA: 4:50:15; LR: 4.964e-05; cls_loss: 0.0764, cls_loss_aux: 0.1238, cls_loss_mct: 0.0108, ptc_loss: 0.1343, cle_loss: 9.0064, une_loss: 0.4940, seg_loss: 0.1801...
2024-07-01 16:14:05,172 - train_voc.py - INFO: Iter: 4000; Elasped: 1:11:21; ETA: 4:45:24; LR: 4.909e-05; cls_loss: 0.0747, cls_loss_aux: 0.1199, cls_loss_mct: 0.0096, ptc_loss: 0.1320, cle_loss: 9.0148, une_loss: 0.4930, seg_loss: 0.1682...
2024-07-01 16:14:11,216 - train_voc.py - INFO: Validating...
2024-07-01 16:20:29,583 - train_voc.py - INFO: val cls score: 0.878286
2024-07-01 16:20:29,583 - train_voc.py - INFO:
+-----------------+--------+---------+-----------+--------+----------+---------+
| Class | CAM | aux_CAM | Score Map | Fused | Seg_Pred | Seg_CAM |
+=================+========+=========+===========+========+==========+=========+
| _background_ | 80.513 | 84.296 | 76.777 | 87.056 | 78.737 | 79.262 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| aeroplane | 14.261 | 44.776 | 41.546 | 56.950 | 11.877 | 13.400 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| bicycle | 43.213 | 40.598 | 39.412 | 45.746 | 37.547 | 42.138 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| bird | 60.575 | 73.561 | 56.786 | 76.098 | 71.752 | 60.605 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| boat | 24.007 | 34.965 | 29.066 | 31.255 | 21.351 | 22.478 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| bottle | 60.961 | 51.054 | 42.588 | 52.968 | 56.534 | 56.838 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| bus | 80.653 | 82.551 | 78.357 | 83.620 | 79.273 | 78.705 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| car | 80.301 | 71.348 | 62.151 | 76.489 | 76.691 | 78.723 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| cat | 86.696 | 59.733 | 58.013 | 71.653 | 84.512 | 83.956 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| chair | 38.711 | 27.308 | 23.798 | 30.837 | 25.116 | 30.200 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| cow | 84.422 | 80.896 | 79.525 | 79.110 | 78.647 | 83.930 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| diningtable | 30.584 | 51.003 | 35.483 | 24.048 | 15.425 | 23.962 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| dog | 83.504 | 80.047 | 80.775 | 84.104 | 81.252 | 78.083 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| horse | 82.274 | 72.231 | 69.796 | 79.176 | 78.615 | 79.492 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| motorbike | 70.952 | 66.061 | 63.218 | 68.693 | 65.163 | 66.786 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| person | 78.534 | 73.735 | 66.445 | 76.600 | 74.807 | 78.455 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| pottedplant | 57.922 | 59.926 | 34.719 | 42.534 | 44.794 | 52.502 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| sheep | 81.049 | 76.071 | 76.164 | 83.581 | 82.407 | 79.281 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| sofa | 52.982 | 6.133 | 11.471 | 29.366 | 18.873 | 19.984 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| train | 58.446 | 59.677 | 57.228 | 59.305 | 55.788 | 56.587 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| tvmonitor | 40.655 | 58.383 | 34.147 | 49.393 | 36.370 | 35.541 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| m-Precision | 70.175 | 69.534 | 59.572 | 78.639 | 71.238 | 68.810 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| m-Recall | 85.400 | 79.370 | 80.876 | 73.590 | 75.116 | 80.424 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| m-ConfutionRati | 0.795 | 0.660 | 1.083 | 0.363 | 0.866 | 0.865 |
| o | | | | | | |
+-----------------+--------+---------+-----------+--------+----------+---------+
| m-IoU | 61.486 | 59.731 | 53.213 | 61.361 | 55.978 | 57.186 |
+-----------------+--------+---------+-----------+--------+----------+---------+
2024-07-01 16:23:40,268 - train_voc.py - INFO: Iter: 4200; Elasped: 1:20:56; ETA: 5:04:27; LR: 4.853e-05; cls_loss: 0.0747, cls_loss_aux: 0.1165, cls_loss_mct: 0.0072, ptc_loss: 0.1102, cle_loss: 8.8381, une_loss: 0.4914, seg_loss: 0.1844...
2024-07-01 16:26:55,489 - train_voc.py - INFO: Iter: 4400; Elasped: 1:24:11; ETA: 4:58:28; LR: 4.798e-05; cls_loss: 0.0714, cls_loss_aux: 0.1174, cls_loss_mct: 0.0078, ptc_loss: 0.1037, cle_loss: 8.9126, une_loss: 0.4927, seg_loss: 0.1527...
2024-07-01 16:30:11,331 - train_voc.py - INFO: Iter: 4600; Elasped: 1:27:27; ETA: 4:52:46; LR: 4.743e-05; cls_loss: 0.0767, cls_loss_aux: 0.1243, cls_loss_mct: 0.0062, ptc_loss: 0.0998, cle_loss: 9.4577, une_loss: 0.4900, seg_loss: 0.1443...
2024-07-01 16:33:25,899 - train_voc.py - INFO: Iter: 4800; Elasped: 1:30:41; ETA: 4:47:09; LR: 4.687e-05; cls_loss: 0.0699, cls_loss_aux: 0.1198, cls_loss_mct: 0.0070, ptc_loss: 0.0929, cle_loss: 8.8295, une_loss: 0.4901, seg_loss: 0.1285...
2024-07-01 16:36:40,906 - train_voc.py - INFO: Iter: 5000; Elasped: 1:33:56; ETA: 4:41:48; LR: 4.632e-05; cls_loss: 0.0753, cls_loss_aux: 0.1271, cls_loss_mct: 0.0066, ptc_loss: 0.0868, cle_loss: 8.8645, une_loss: 0.4895, seg_loss: 0.1887...
2024-07-01 16:39:55,971 - train_voc.py - INFO: Iter: 5200; Elasped: 1:37:11; ETA: 4:36:35; LR: 4.576e-05; cls_loss: 0.0722, cls_loss_aux: 0.1145, cls_loss_mct: 0.0055, ptc_loss: 0.0858, cle_loss: 8.7374, une_loss: 0.4892, seg_loss: 0.1409...
2024-07-01 16:43:12,026 - train_voc.py - INFO: Iter: 5400; Elasped: 1:40:28; ETA: 4:31:37; LR: 4.520e-05; cls_loss: 0.0766, cls_loss_aux: 0.1265, cls_loss_mct: 0.0065, ptc_loss: 0.0875, cle_loss: 9.0454, une_loss: 0.4903, seg_loss: 0.1612...
2024-07-01 16:46:28,765 - train_voc.py - INFO: Iter: 5600; Elasped: 1:43:44; ETA: 4:26:44; LR: 4.465e-05; cls_loss: 0.0702, cls_loss_aux: 0.1118, cls_loss_mct: 0.0044, ptc_loss: 0.0819, cle_loss: 8.9063, une_loss: 0.4892, seg_loss: 0.1541...
2024-07-01 16:49:45,556 - train_voc.py - INFO: Iter: 5800; Elasped: 1:47:01; ETA: 4:22:00; LR: 4.409e-05; cls_loss: 0.0674, cls_loss_aux: 0.1064, cls_loss_mct: 0.0049, ptc_loss: 0.0853, cle_loss: 8.7338, une_loss: 0.4891, seg_loss: 0.1544...
2024-07-01 16:53:03,712 - train_voc.py - INFO: Iter: 6000; Elasped: 1:50:19; ETA: 4:17:24; LR: 4.353e-05; cls_loss: 0.0743, cls_loss_aux: 0.1140, cls_loss_mct: 0.0050, ptc_loss: 0.0827, cle_loss: 9.2751, une_loss: 0.4893, seg_loss: 0.1602...
2024-07-01 16:53:09,633 - train_voc.py - INFO: Validating...
2024-07-01 16:59:34,690 - train_voc.py - INFO: val cls score: 0.902362
2024-07-01 16:59:34,690 - train_voc.py - INFO:
+-----------------+--------+---------+-----------+--------+----------+---------+
| Class | CAM | aux_CAM | Score Map | Fused | Seg_Pred | Seg_CAM |
+=================+========+=========+===========+========+==========+=========+
| _background_ | 84.048 | 87.325 | 81.949 | 89.807 | 83.023 | 83.241 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| aeroplane | 14.554 | 71.880 | 65.149 | 66.066 | 13.019 | 13.874 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| bicycle | 42.966 | 42.542 | 40.846 | 47.040 | 39.190 | 41.851 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| bird | 81.377 | 75.547 | 76.284 | 86.057 | 84.521 | 81.257 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| boat | 43.214 | 50.272 | 40.136 | 50.068 | 39.690 | 39.768 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| bottle | 73.163 | 52.164 | 39.416 | 55.045 | 68.566 | 70.319 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| bus | 84.404 | 86.273 | 77.165 | 84.753 | 84.073 | 85.139 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| car | 82.251 | 64.407 | 55.611 | 82.121 | 79.506 | 80.612 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| cat | 85.697 | 77.332 | 75.244 | 74.334 | 83.169 | 83.292 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| chair | 44.162 | 27.368 | 20.702 | 41.322 | 30.900 | 30.708 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| cow | 87.570 | 74.944 | 83.267 | 80.975 | 75.578 | 81.372 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| diningtable | 48.530 | 50.955 | 49.925 | 40.717 | 42.032 | 42.638 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| dog | 85.306 | 83.875 | 84.257 | 80.049 | 78.221 | 81.744 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| horse | 86.308 | 77.564 | 79.169 | 84.047 | 82.039 | 81.534 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| motorbike | 79.587 | 78.554 | 76.473 | 79.552 | 75.194 | 77.135 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| person | 82.138 | 77.241 | 73.738 | 80.600 | 79.978 | 81.426 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| pottedplant | 64.376 | 64.924 | 34.178 | 49.146 | 54.177 | 62.533 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| sheep | 86.271 | 84.542 | 84.668 | 86.500 | 81.594 | 83.074 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| sofa | 51.310 | 53.793 | 37.206 | 51.642 | 36.674 | 35.838 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| train | 60.289 | 65.340 | 59.702 | 62.034 | 59.030 | 58.958 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| tvmonitor | 51.819 | 67.704 | 49.850 | 62.035 | 46.613 | 49.447 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| m-Precision | 74.955 | 76.572 | 67.177 | 84.162 | 73.884 | 72.748 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| m-Recall | 88.309 | 85.347 | 86.940 | 78.711 | 81.078 | 85.011 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| m-ConfutionRati | 0.627 | 0.421 | 0.726 | 0.235 | 0.708 | 0.716 |
| o | | | | | | |
+-----------------+--------+---------+-----------+--------+----------+---------+
| m-IoU | 67.588 | 67.359 | 61.187 | 68.281 | 62.704 | 64.084 |
+-----------------+--------+---------+-----------+--------+----------+---------+
2024-07-01 17:02:45,418 - train_voc.py - INFO: Iter: 6200; Elasped: 2:00:01; ETA: 4:27:08; LR: 4.297e-05; cls_loss: 0.0710, cls_loss_aux: 0.1036, cls_loss_mct: 0.0048, ptc_loss: 0.0765, cle_loss: 9.1253, une_loss: 0.4881, seg_loss: 0.1279...
2024-07-01 17:06:02,641 - train_voc.py - INFO: Iter: 6400; Elasped: 2:03:18; ETA: 4:22:00; LR: 4.241e-05; cls_loss: 0.0662, cls_loss_aux: 0.0990, cls_loss_mct: 0.0050, ptc_loss: 0.0731, cle_loss: 8.9892, une_loss: 0.4874, seg_loss: 0.1724...
2024-07-01 17:09:19,154 - train_voc.py - INFO: Iter: 6600; Elasped: 2:06:35; ETA: 4:17:00; LR: 4.185e-05; cls_loss: 0.0647, cls_loss_aux: 0.0911, cls_loss_mct: 0.0037, ptc_loss: 0.0700, cle_loss: 8.7862, une_loss: 0.4874, seg_loss: 0.1207...
2024-07-01 17:12:33,348 - train_voc.py - INFO: Iter: 6800; Elasped: 2:09:49; ETA: 4:11:59; LR: 4.128e-05; cls_loss: 0.0563, cls_loss_aux: 0.0853, cls_loss_mct: 0.0034, ptc_loss: 0.0694, cle_loss: 8.6952, une_loss: 0.4873, seg_loss: 0.1326...
2024-07-01 17:15:46,966 - train_voc.py - INFO: Iter: 7000; Elasped: 2:13:02; ETA: 4:07:03; LR: 4.072e-05; cls_loss: 0.0654, cls_loss_aux: 0.1047, cls_loss_mct: 0.0035, ptc_loss: 0.0719, cle_loss: 8.8023, une_loss: 0.4871, seg_loss: 0.1490...
2024-07-01 17:18:59,577 - train_voc.py - INFO: Iter: 7200; Elasped: 2:16:15; ETA: 4:02:13; LR: 4.016e-05; cls_loss: 0.0623, cls_loss_aux: 0.0906, cls_loss_mct: 0.0034, ptc_loss: 0.0637, cle_loss: 8.8114, une_loss: 0.4858, seg_loss: 0.1103...
2024-07-01 17:22:11,271 - train_voc.py - INFO: Iter: 7400; Elasped: 2:19:27; ETA: 3:57:26; LR: 3.959e-05; cls_loss: 0.0581, cls_loss_aux: 0.0831, cls_loss_mct: 0.0033, ptc_loss: 0.0656, cle_loss: 8.6354, une_loss: 0.4861, seg_loss: 0.1190...
2024-07-01 17:25:23,847 - train_voc.py - INFO: Iter: 7600; Elasped: 2:22:39; ETA: 3:52:44; LR: 3.902e-05; cls_loss: 0.0627, cls_loss_aux: 0.0919, cls_loss_mct: 0.0034, ptc_loss: 0.0607, cle_loss: 8.6052, une_loss: 0.4866, seg_loss: 0.1037...
2024-07-01 17:28:35,720 - train_voc.py - INFO: Iter: 7800; Elasped: 2:25:51; ETA: 3:48:07; LR: 3.846e-05; cls_loss: 0.0592, cls_loss_aux: 0.0833, cls_loss_mct: 0.0023, ptc_loss: 0.0555, cle_loss: 8.3581, une_loss: 0.4852, seg_loss: 0.1091...
2024-07-01 17:31:48,694 - train_voc.py - INFO: Iter: 8000; Elasped: 2:29:04; ETA: 3:43:36; LR: 3.789e-05; cls_loss: 0.0617, cls_loss_aux: 0.0895, cls_loss_mct: 0.0024, ptc_loss: 0.0604, cle_loss: 8.7888, une_loss: 0.4855, seg_loss: 0.1308...
2024-07-01 17:31:54,505 - train_voc.py - INFO: Validating...
2024-07-01 17:38:07,235 - train_voc.py - INFO: val cls score: 0.898923
2024-07-01 17:38:07,235 - train_voc.py - INFO:
+-----------------+--------+---------+-----------+--------+----------+---------+
| Class | CAM | aux_CAM | Score Map | Fused | Seg_Pred | Seg_CAM |
+=================+========+=========+===========+========+==========+=========+
| _background_ | 86.057 | 88.683 | 86.130 | 90.234 | 84.945 | 85.569 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| aeroplane | 15.552 | 77.622 | 71.055 | 71.803 | 13.866 | 15.235 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| bicycle | 45.146 | 43.227 | 34.556 | 47.054 | 41.373 | 44.628 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| bird | 86.620 | 78.824 | 72.104 | 84.429 | 86.034 | 86.216 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| boat | 65.499 | 58.236 | 62.270 | 66.933 | 59.534 | 60.285 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| bottle | 77.213 | 53.340 | 34.171 | 56.944 | 71.687 | 76.819 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| bus | 85.289 | 83.181 | 79.436 | 82.015 | 82.384 | 83.742 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| car | 79.460 | 74.554 | 74.518 | 82.706 | 73.642 | 76.400 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| cat | 83.247 | 73.694 | 78.550 | 74.066 | 84.277 | 80.632 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| chair | 45.413 | 29.971 | 33.295 | 45.403 | 30.855 | 33.430 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| cow | 87.967 | 76.495 | 85.764 | 79.798 | 80.717 | 77.900 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| diningtable | 49.315 | 56.586 | 44.889 | 41.786 | 38.598 | 47.348 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| dog | 85.389 | 82.932 | 81.504 | 79.945 | 79.814 | 80.098 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| horse | 87.229 | 77.351 | 79.707 | 83.049 | 79.339 | 81.360 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| motorbike | 76.516 | 74.953 | 74.003 | 74.951 | 73.460 | 74.809 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| person | 80.781 | 74.809 | 70.111 | 77.317 | 78.210 | 80.439 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| pottedplant | 51.521 | 58.299 | 34.582 | 41.864 | 47.080 | 51.085 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| sheep | 87.169 | 84.295 | 84.970 | 86.358 | 83.307 | 85.002 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| sofa | 56.684 | 67.504 | 61.857 | 56.157 | 43.001 | 44.144 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| train | 59.522 | 60.380 | 57.170 | 59.693 | 56.520 | 57.718 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| tvmonitor | 66.244 | 67.239 | 49.820 | 62.661 | 60.616 | 63.573 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| m-Precision | 79.418 | 80.082 | 71.480 | 87.421 | 77.701 | 77.406 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| m-Recall | 85.534 | 83.313 | 85.186 | 76.481 | 79.508 | 82.474 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| m-ConfutionRati | 0.500 | 0.339 | 0.543 | 0.170 | 0.587 | 0.551 |
| o | | | | | | |
+-----------------+--------+---------+-----------+--------+----------+---------+
| m-IoU | 69.421 | 68.675 | 64.308 | 68.817 | 64.250 | 66.021 |
+-----------------+--------+---------+-----------+--------+----------+---------+
2024-07-01 17:41:13,602 - train_voc.py - INFO: Iter: 8200; Elasped: 2:38:29; ETA: 3:48:03; LR: 3.732e-05; cls_loss: 0.0539, cls_loss_aux: 0.0737, cls_loss_mct: 0.0024, ptc_loss: 0.0542, cle_loss: 8.5886, une_loss: 0.4840, seg_loss: 0.0917...
2024-07-01 17:44:26,553 - train_voc.py - INFO: Iter: 8400; Elasped: 2:41:42; ETA: 3:43:18; LR: 3.675e-05; cls_loss: 0.0582, cls_loss_aux: 0.0761, cls_loss_mct: 0.0029, ptc_loss: 0.0596, cle_loss: 8.6735, une_loss: 0.4849, seg_loss: 0.1277...
2024-07-01 17:47:39,347 - train_voc.py - INFO: Iter: 8600; Elasped: 2:44:55; ETA: 3:38:36; LR: 3.618e-05; cls_loss: 0.0609, cls_loss_aux: 0.0810, cls_loss_mct: 0.0024, ptc_loss: 0.0596, cle_loss: 8.6950, une_loss: 0.4862, seg_loss: 0.1029...
2024-07-01 17:50:52,301 - train_voc.py - INFO: Iter: 8800; Elasped: 2:48:08; ETA: 3:33:59; LR: 3.561e-05; cls_loss: 0.0596, cls_loss_aux: 0.0845, cls_loss_mct: 0.0028, ptc_loss: 0.0619, cle_loss: 8.7440, une_loss: 0.4855, seg_loss: 0.1200...
2024-07-01 17:54:05,042 - train_voc.py - INFO: Iter: 9000; Elasped: 2:51:21; ETA: 3:29:25; LR: 3.504e-05; cls_loss: 0.0579, cls_loss_aux: 0.0744, cls_loss_mct: 0.0023, ptc_loss: 0.0596, cle_loss: 8.6235, une_loss: 0.4854, seg_loss: 0.1143...
2024-07-01 17:57:18,236 - train_voc.py - INFO: Iter: 9200; Elasped: 2:54:34; ETA: 3:24:55; LR: 3.446e-05; cls_loss: 0.0604, cls_loss_aux: 0.0846, cls_loss_mct: 0.0030, ptc_loss: 0.0629, cle_loss: 8.7198, une_loss: 0.4867, seg_loss: 0.1276...
2024-07-01 18:00:30,305 - train_voc.py - INFO: Iter: 9400; Elasped: 2:57:46; ETA: 3:20:27; LR: 3.389e-05; cls_loss: 0.0516, cls_loss_aux: 0.0655, cls_loss_mct: 0.0020, ptc_loss: 0.0534, cle_loss: 8.6016, une_loss: 0.4844, seg_loss: 0.0957...
2024-07-01 18:03:42,133 - train_voc.py - INFO: Iter: 9600; Elasped: 3:00:58; ETA: 3:16:02; LR: 3.331e-05; cls_loss: 0.0609, cls_loss_aux: 0.0764, cls_loss_mct: 0.0025, ptc_loss: 0.0582, cle_loss: 8.6879, une_loss: 0.4855, seg_loss: 0.0930...
2024-07-01 18:06:54,666 - train_voc.py - INFO: Iter: 9800; Elasped: 3:04:10; ETA: 3:11:41; LR: 3.273e-05; cls_loss: 0.0556, cls_loss_aux: 0.0694, cls_loss_mct: 0.0024, ptc_loss: 0.0567, cle_loss: 8.7715, une_loss: 0.4845, seg_loss: 0.0940...
2024-07-01 18:10:07,524 - train_voc.py - INFO: Iter: 10000; Elasped: 3:07:23; ETA: 3:07:23; LR: 3.216e-05; cls_loss: 0.0580, cls_loss_aux: 0.0746, cls_loss_mct: 0.0022, ptc_loss: 0.0536, cle_loss: 8.7659, une_loss: 0.4839, seg_loss: 0.0950...
2024-07-01 18:10:13,764 - train_voc.py - INFO: Validating...
2024-07-01 18:16:26,445 - train_voc.py - INFO: val cls score: 0.895218
2024-07-01 18:16:26,446 - train_voc.py - INFO:
+-----------------+--------+---------+-----------+--------+----------+---------+
| Class | CAM | aux_CAM | Score Map | Fused | Seg_Pred | Seg_CAM |
+=================+========+=========+===========+========+==========+=========+
| _background_ | 86.362 | 88.513 | 83.166 | 89.598 | 84.933 | 86.078 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| aeroplane | 15.852 | 78.567 | 70.368 | 71.718 | 13.652 | 15.654 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| bicycle | 44.596 | 43.161 | 18.352 | 40.458 | 38.582 | 43.815 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| bird | 87.450 | 70.969 | 72.314 | 82.608 | 87.218 | 87.004 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| boat | 65.669 | 60.242 | 58.141 | 65.908 | 64.189 | 59.689 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| bottle | 73.341 | 53.627 | 39.986 | 64.533 | 69.326 | 70.283 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| bus | 85.819 | 83.924 | 72.166 | 75.822 | 84.119 | 85.535 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| car | 78.908 | 65.865 | 56.838 | 75.302 | 77.441 | 78.730 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| cat | 86.894 | 81.446 | 82.191 | 78.479 | 84.292 | 85.036 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| chair | 46.330 | 32.406 | 27.063 | 41.950 | 35.533 | 34.092 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| cow | 87.755 | 78.277 | 74.449 | 78.053 | 80.966 | 82.302 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| diningtable | 38.018 | 46.039 | 39.879 | 30.609 | 38.269 | 31.297 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| dog | 83.641 | 81.422 | 81.692 | 77.308 | 75.184 | 78.790 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| horse | 86.007 | 74.722 | 83.969 | 80.866 | 81.438 | 82.426 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| motorbike | 75.918 | 75.867 | 67.480 | 69.429 | 72.056 | 72.544 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| person | 80.355 | 76.417 | 68.708 | 76.088 | 78.087 | 79.921 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| pottedplant | 52.175 | 60.298 | 20.130 | 37.659 | 45.387 | 50.933 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| sheep | 89.296 | 82.967 | 85.383 | 81.988 | 84.737 | 86.485 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| sofa | 53.956 | 64.224 | 59.633 | 52.605 | 44.458 | 45.825 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| train | 61.014 | 61.852 | 59.887 | 60.646 | 57.921 | 60.836 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| tvmonitor | 59.689 | 61.927 | 32.018 | 51.318 | 53.946 | 58.158 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| m-Precision | 81.427 | 77.822 | 67.758 | 88.196 | 80.080 | 79.525 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| m-Recall | 82.213 | 83.712 | 81.545 | 71.952 | 77.271 | 79.544 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| m-ConfutionRati | 0.458 | 0.367 | 0.831 | 0.157 | 0.530 | 0.500 |
| o | | | | | | |
+-----------------+--------+---------+-----------+--------+----------+---------+
| m-IoU | 68.526 | 67.749 | 59.706 | 65.855 | 64.368 | 65.497 |
+-----------------+--------+---------+-----------+--------+----------+---------+
2024-07-01 18:19:24,531 - train_voc.py - INFO: Iter: 10200; Elasped: 3:16:40; ETA: 3:08:57; LR: 3.158e-05; cls_loss: 0.0539, cls_loss_aux: 0.0696, cls_loss_mct: 0.0031, ptc_loss: 0.0571, cle_loss: 8.0832, une_loss: 0.4850, seg_loss: 0.1083...
2024-07-01 18:22:28,726 - train_voc.py - INFO: Iter: 10400; Elasped: 3:19:44; ETA: 3:04:22; LR: 3.100e-05; cls_loss: 0.0549, cls_loss_aux: 0.0685, cls_loss_mct: 0.0043, ptc_loss: 0.0550, cle_loss: 8.1653, une_loss: 0.4852, seg_loss: 0.0961...
2024-07-01 18:25:33,652 - train_voc.py - INFO: Iter: 10600; Elasped: 3:22:49; ETA: 2:59:51; LR: 3.041e-05; cls_loss: 0.0563, cls_loss_aux: 0.0712, cls_loss_mct: 0.0026, ptc_loss: 0.0542, cle_loss: 7.9929, une_loss: 0.4856, seg_loss: 0.0983...
2024-07-01 18:28:38,209 - train_voc.py - INFO: Iter: 10800; Elasped: 3:25:54; ETA: 2:55:23; LR: 2.983e-05; cls_loss: 0.0494, cls_loss_aux: 0.0604, cls_loss_mct: 0.0050, ptc_loss: 0.0506, cle_loss: 8.2303, une_loss: 0.4853, seg_loss: 0.0872...
2024-07-01 18:31:41,747 - train_voc.py - INFO: Iter: 11000; Elasped: 3:28:57; ETA: 2:50:57; LR: 2.925e-05; cls_loss: 0.0487, cls_loss_aux: 0.0529, cls_loss_mct: 0.0133, ptc_loss: 0.0504, cle_loss: 7.9995, une_loss: 0.4849, seg_loss: 0.0918...
2024-07-01 18:34:45,859 - train_voc.py - INFO: Iter: 11200; Elasped: 3:32:01; ETA: 2:46:35; LR: 2.866e-05; cls_loss: 0.0880, cls_loss_aux: 0.1148, cls_loss_mct: 0.0372, ptc_loss: 0.0825, cle_loss: 7.9241, une_loss: 0.4921, seg_loss: 0.2376...
2024-07-01 18:37:49,597 - train_voc.py - INFO: Iter: 11400; Elasped: 3:35:05; ETA: 2:42:15; LR: 2.807e-05; cls_loss: 0.0600, cls_loss_aux: 0.0724, cls_loss_mct: 0.0025, ptc_loss: 0.0627, cle_loss: 7.8950, une_loss: 0.4873, seg_loss: 0.1316...
2024-07-01 18:40:54,741 - train_voc.py - INFO: Iter: 11600; Elasped: 3:38:10; ETA: 2:37:58; LR: 2.749e-05; cls_loss: 0.0560, cls_loss_aux: 0.0688, cls_loss_mct: 0.0021, ptc_loss: 0.0560, cle_loss: 7.9979, une_loss: 0.4866, seg_loss: 0.1271...
2024-07-01 18:44:03,203 - train_voc.py - INFO: Iter: 11800; Elasped: 3:41:19; ETA: 2:33:47; LR: 2.690e-05; cls_loss: 0.0526, cls_loss_aux: 0.0594, cls_loss_mct: 0.0019, ptc_loss: 0.0538, cle_loss: 7.9220, une_loss: 0.4852, seg_loss: 0.0993...
2024-07-01 18:47:12,009 - train_voc.py - INFO: Iter: 12000; Elasped: 3:44:28; ETA: 2:29:38; LR: 2.631e-05; cls_loss: 0.0468, cls_loss_aux: 0.0548, cls_loss_mct: 0.0021, ptc_loss: 0.0498, cle_loss: 8.1005, une_loss: 0.4842, seg_loss: 0.0893...
2024-07-01 18:47:17,479 - train_voc.py - INFO: Validating...
2024-07-01 18:53:32,600 - train_voc.py - INFO: val cls score: 0.910749
2024-07-01 18:53:32,600 - train_voc.py - INFO:
+-----------------+--------+---------+-----------+--------+----------+---------+
| Class | CAM | aux_CAM | Score Map | Fused | Seg_Pred | Seg_CAM |
+=================+========+=========+===========+========+==========+=========+
| _background_ | 91.408 | 89.400 | 90.944 | 90.993 | 91.005 | 91.045 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| aeroplane | 73.012 | 79.811 | 74.553 | 79.625 | 75.347 | 71.533 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| bicycle | 43.274 | 41.218 | 42.912 | 45.643 | 37.404 | 41.703 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| bird | 83.425 | 82.148 | 82.988 | 86.871 | 84.559 | 83.259 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| boat | 64.803 | 60.531 | 66.397 | 70.004 | 63.637 | 60.486 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| bottle | 77.126 | 61.679 | 71.635 | 74.502 | 75.168 | 75.732 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| bus | 86.737 | 83.719 | 85.587 | 86.808 | 82.842 | 85.111 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| car | 84.016 | 69.990 | 81.014 | 83.466 | 82.444 | 82.945 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| cat | 85.344 | 79.129 | 85.900 | 79.233 | 86.484 | 83.774 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| chair | 49.067 | 35.032 | 49.581 | 46.341 | 35.182 | 36.274 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| cow | 86.283 | 77.149 | 83.167 | 79.052 | 82.764 | 82.067 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| diningtable | 45.874 | 55.877 | 46.088 | 36.572 | 36.791 | 41.181 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| dog | 87.138 | 83.893 | 86.245 | 83.079 | 80.963 | 82.961 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| horse | 86.973 | 74.789 | 85.974 | 84.141 | 83.735 | 84.765 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| motorbike | 78.855 | 77.730 | 78.212 | 77.995 | 76.919 | 76.893 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| person | 81.618 | 77.376 | 80.027 | 79.000 | 79.778 | 80.679 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| pottedplant | 63.260 | 63.331 | 59.338 | 58.813 | 56.883 | 59.944 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| sheep | 89.366 | 82.456 | 86.940 | 84.715 | 85.762 | 87.125 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| sofa | 55.453 | 61.791 | 55.459 | 52.031 | 43.764 | 45.838 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| train | 61.791 | 62.286 | 61.765 | 62.663 | 61.710 | 61.404 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| tvmonitor | 64.411 | 69.953 | 56.931 | 65.277 | 63.553 | 60.727 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| m-Precision | 82.927 | 80.237 | 81.590 | 87.726 | 81.917 | 80.671 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| m-Recall | 86.425 | 84.514 | 85.864 | 79.794 | 81.717 | 84.151 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| m-ConfutionRati | 0.246 | 0.318 | 0.267 | 0.166 | 0.264 | 0.294 |
| o | | | | | | |
+-----------------+--------+---------+-----------+--------+----------+---------+
| m-IoU | 73.297 | 69.966 | 71.984 | 71.754 | 69.843 | 70.259 |
+-----------------+--------+---------+-----------+--------+----------+---------+
2024-07-01 18:56:30,587 - train_voc.py - INFO: Iter: 12200; Elasped: 3:53:46; ETA: 2:29:27; LR: 2.571e-05; cls_loss: 0.0498, cls_loss_aux: 0.0571, cls_loss_mct: 0.0016, ptc_loss: 0.0459, cle_loss: 7.7650, une_loss: 0.4843, seg_loss: 0.0831...
2024-07-01 18:59:33,896 - train_voc.py - INFO: Iter: 12400; Elasped: 3:56:49; ETA: 2:25:08; LR: 2.512e-05; cls_loss: 0.0488, cls_loss_aux: 0.0540, cls_loss_mct: 0.0018, ptc_loss: 0.0467, cle_loss: 7.9380, une_loss: 0.4848, seg_loss: 0.0818...
2024-07-01 19:02:37,070 - train_voc.py - INFO: Iter: 12600; Elasped: 3:59:53; ETA: 2:20:53; LR: 2.452e-05; cls_loss: 0.0422, cls_loss_aux: 0.0441, cls_loss_mct: 0.0027, ptc_loss: 0.0419, cle_loss: 7.6862, une_loss: 0.4837, seg_loss: 0.0887...
2024-07-01 19:05:40,545 - train_voc.py - INFO: Iter: 12800; Elasped: 4:02:56; ETA: 2:16:39; LR: 2.393e-05; cls_loss: 0.0472, cls_loss_aux: 0.0511, cls_loss_mct: 0.0018, ptc_loss: 0.0483, cle_loss: 7.6829, une_loss: 0.4832, seg_loss: 0.0847...
2024-07-01 19:08:44,262 - train_voc.py - INFO: Iter: 13000; Elasped: 4:06:00; ETA: 2:12:27; LR: 2.333e-05; cls_loss: 0.0462, cls_loss_aux: 0.0516, cls_loss_mct: 0.0017, ptc_loss: 0.0444, cle_loss: 7.8048, une_loss: 0.4835, seg_loss: 0.0802...
2024-07-01 19:11:47,333 - train_voc.py - INFO: Iter: 13200; Elasped: 4:09:03; ETA: 2:08:17; LR: 2.273e-05; cls_loss: 0.0465, cls_loss_aux: 0.0507, cls_loss_mct: 0.0017, ptc_loss: 0.0435, cle_loss: 7.8494, une_loss: 0.4836, seg_loss: 0.0875...
2024-07-01 19:14:50,468 - train_voc.py - INFO: Iter: 13400; Elasped: 4:12:06; ETA: 2:04:10; LR: 2.212e-05; cls_loss: 0.0403, cls_loss_aux: 0.0420, cls_loss_mct: 0.0012, ptc_loss: 0.0419, cle_loss: 7.5842, une_loss: 0.4824, seg_loss: 0.0686...
2024-07-01 19:17:53,910 - train_voc.py - INFO: Iter: 13600; Elasped: 4:15:09; ETA: 2:00:04; LR: 2.152e-05; cls_loss: 0.0434, cls_loss_aux: 0.0428, cls_loss_mct: 0.0015, ptc_loss: 0.0382, cle_loss: 7.8777, une_loss: 0.4831, seg_loss: 0.0860...
2024-07-01 19:20:57,684 - train_voc.py - INFO: Iter: 13800; Elasped: 4:18:13; ETA: 1:56:00; LR: 2.091e-05; cls_loss: 0.0406, cls_loss_aux: 0.0395, cls_loss_mct: 0.0015, ptc_loss: 0.0399, cle_loss: 8.0264, une_loss: 0.4831, seg_loss: 0.0742...
2024-07-01 19:24:00,054 - train_voc.py - INFO: Iter: 14000; Elasped: 4:21:16; ETA: 1:51:58; LR: 2.031e-05; cls_loss: 0.0459, cls_loss_aux: 0.0463, cls_loss_mct: 0.0018, ptc_loss: 0.0419, cle_loss: 7.7088, une_loss: 0.4838, seg_loss: 0.0891...
2024-07-01 19:24:05,903 - train_voc.py - INFO: Validating...
2024-07-01 19:30:18,744 - train_voc.py - INFO: val cls score: 0.910558
2024-07-01 19:30:18,744 - train_voc.py - INFO:
+-----------------+--------+---------+-----------+--------+----------+---------+
| Class | CAM | aux_CAM | Score Map | Fused | Seg_Pred | Seg_CAM |
+=================+========+=========+===========+========+==========+=========+
| _background_ | 91.523 | 89.783 | 90.799 | 91.224 | 91.064 | 90.987 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| aeroplane | 72.009 | 78.980 | 74.109 | 79.277 | 70.518 | 69.354 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| bicycle | 43.719 | 43.343 | 42.758 | 46.522 | 37.698 | 42.673 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| bird | 87.630 | 79.633 | 84.834 | 87.322 | 86.100 | 87.753 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| boat | 67.578 | 59.990 | 68.306 | 70.351 | 64.595 | 62.361 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| bottle | 77.490 | 66.047 | 68.563 | 77.038 | 75.405 | 77.479 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| bus | 87.266 | 83.683 | 85.546 | 83.061 | 84.676 | 86.132 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| car | 83.166 | 76.156 | 81.259 | 82.491 | 80.304 | 80.464 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| cat | 86.033 | 78.161 | 86.768 | 80.556 | 87.679 | 84.462 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| chair | 45.271 | 38.905 | 44.937 | 47.970 | 33.143 | 33.139 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| cow | 87.070 | 80.592 | 85.128 | 80.151 | 82.495 | 82.702 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| diningtable | 59.993 | 56.082 | 57.656 | 54.100 | 57.885 | 55.950 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| dog | 87.051 | 84.580 | 85.763 | 84.125 | 81.717 | 82.413 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| horse | 87.283 | 73.882 | 86.531 | 84.439 | 83.194 | 84.854 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| motorbike | 76.808 | 75.775 | 76.853 | 76.743 | 74.557 | 75.316 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| person | 81.859 | 78.384 | 79.199 | 78.803 | 80.235 | 81.637 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| pottedplant | 59.004 | 61.779 | 53.863 | 53.148 | 50.462 | 55.239 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| sheep | 89.286 | 83.914 | 87.218 | 84.412 | 88.258 | 88.856 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| sofa | 59.455 | 65.873 | 58.452 | 60.052 | 44.762 | 47.399 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| train | 61.726 | 60.846 | 61.218 | 61.408 | 59.936 | 61.310 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| tvmonitor | 66.582 | 71.373 | 57.991 | 65.548 | 64.986 | 63.666 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| m-Precision | 82.568 | 80.847 | 80.439 | 87.102 | 80.602 | 80.573 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| m-Recall | 87.740 | 84.885 | 87.219 | 81.453 | 83.808 | 85.305 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| m-ConfutionRati | 0.260 | 0.294 | 0.295 | 0.179 | 0.315 | 0.314 |
| o | | | | | | |
+-----------------+--------+---------+-----------+--------+----------+---------+
| m-IoU | 74.181 | 70.846 | 72.274 | 72.797 | 70.461 | 71.150 |
+-----------------+--------+---------+-----------+--------+----------+---------+
2024-07-01 19:33:16,398 - train_voc.py - INFO: Iter: 14200; Elasped: 4:30:32; ETA: 1:50:29; LR: 1.970e-05; cls_loss: 0.0408, cls_loss_aux: 0.0398, cls_loss_mct: 0.0014, ptc_loss: 0.0446, cle_loss: 7.7298, une_loss: 0.4828, seg_loss: 0.0774...
2024-07-01 19:36:19,498 - train_voc.py - INFO: Iter: 14400; Elasped: 4:33:35; ETA: 1:46:23; LR: 1.908e-05; cls_loss: 0.0442, cls_loss_aux: 0.0416, cls_loss_mct: 0.0013, ptc_loss: 0.0416, cle_loss: 7.8975, une_loss: 0.4821, seg_loss: 0.0709...
2024-07-01 19:39:23,109 - train_voc.py - INFO: Iter: 14600; Elasped: 4:36:39; ETA: 1:42:19; LR: 1.847e-05; cls_loss: 0.0423, cls_loss_aux: 0.0404, cls_loss_mct: 0.0012, ptc_loss: 0.0425, cle_loss: 7.7903, une_loss: 0.4818, seg_loss: 0.0686...
2024-07-01 19:42:26,497 - train_voc.py - INFO: Iter: 14800; Elasped: 4:39:42; ETA: 1:38:16; LR: 1.785e-05; cls_loss: 0.0372, cls_loss_aux: 0.0367, cls_loss_mct: 0.0013, ptc_loss: 0.0398, cle_loss: 7.7438, une_loss: 0.4820, seg_loss: 0.0660...
2024-07-01 19:45:29,474 - train_voc.py - INFO: Iter: 15000; Elasped: 4:42:45; ETA: 1:34:15; LR: 1.723e-05; cls_loss: 0.0375, cls_loss_aux: 0.0385, cls_loss_mct: 0.0012, ptc_loss: 0.0388, cle_loss: 7.8099, une_loss: 0.4826, seg_loss: 0.0734...
2024-07-01 19:48:33,120 - train_voc.py - INFO: Iter: 15200; Elasped: 4:45:49; ETA: 1:30:15; LR: 1.661e-05; cls_loss: 0.0414, cls_loss_aux: 0.0402, cls_loss_mct: 0.0016, ptc_loss: 0.0393, cle_loss: 7.8445, une_loss: 0.4826, seg_loss: 0.0783...
2024-07-01 19:51:36,159 - train_voc.py - INFO: Iter: 15400; Elasped: 4:48:52; ETA: 1:26:17; LR: 1.599e-05; cls_loss: 0.0426, cls_loss_aux: 0.0414, cls_loss_mct: 0.0015, ptc_loss: 0.0411, cle_loss: 7.9573, une_loss: 0.4833, seg_loss: 0.0721...
2024-07-01 19:54:39,545 - train_voc.py - INFO: Iter: 15600; Elasped: 4:51:55; ETA: 1:22:20; LR: 1.536e-05; cls_loss: 0.0407, cls_loss_aux: 0.0402, cls_loss_mct: 0.0014, ptc_loss: 0.0424, cle_loss: 7.8636, une_loss: 0.4832, seg_loss: 0.0794...
2024-07-01 19:57:42,870 - train_voc.py - INFO: Iter: 15800; Elasped: 4:54:58; ETA: 1:18:24; LR: 1.473e-05; cls_loss: 0.0383, cls_loss_aux: 0.0397, cls_loss_mct: 0.0010, ptc_loss: 0.0380, cle_loss: 7.7133, une_loss: 0.4832, seg_loss: 0.0722...
2024-07-01 20:00:47,005 - train_voc.py - INFO: Iter: 16000; Elasped: 4:58:03; ETA: 1:14:30; LR: 1.410e-05; cls_loss: 0.0374, cls_loss_aux: 0.0362, cls_loss_mct: 0.0010, ptc_loss: 0.0360, cle_loss: 7.6678, une_loss: 0.4823, seg_loss: 0.0652...
2024-07-01 20:00:52,079 - train_voc.py - INFO: Validating...
2024-07-01 20:07:05,100 - train_voc.py - INFO: val cls score: 0.926694
2024-07-01 20:07:05,100 - train_voc.py - INFO:
+-----------------+--------+---------+-----------+--------+----------+---------+
| Class | CAM | aux_CAM | Score Map | Fused | Seg_Pred | Seg_CAM |
+=================+========+=========+===========+========+==========+=========+
| _background_ | 91.927 | 89.944 | 91.451 | 91.235 | 91.766 | 91.651 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| aeroplane | 76.015 | 76.365 | 77.635 | 81.518 | 80.534 | 75.352 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| bicycle | 44.543 | 43.782 | 44.013 | 47.182 | 40.714 | 43.923 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| bird | 87.948 | 82.640 | 85.793 | 88.330 | 87.272 | 87.927 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| boat | 71.822 | 63.122 | 72.045 | 74.695 | 70.510 | 68.554 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| bottle | 80.054 | 64.929 | 75.639 | 77.543 | 77.478 | 79.778 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| bus | 88.956 | 85.665 | 87.128 | 87.879 | 87.894 | 88.382 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| car | 83.850 | 72.509 | 81.656 | 81.427 | 82.526 | 81.934 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| cat | 81.444 | 75.718 | 83.276 | 74.144 | 84.685 | 80.709 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| chair | 55.043 | 40.906 | 53.682 | 52.510 | 39.295 | 41.278 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| cow | 86.949 | 79.339 | 84.805 | 80.232 | 83.772 | 82.313 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| diningtable | 37.221 | 54.104 | 38.467 | 29.425 | 30.066 | 34.786 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| dog | 88.063 | 83.498 | 86.931 | 84.421 | 82.525 | 84.140 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| horse | 86.124 | 70.551 | 84.553 | 81.309 | 84.234 | 83.786 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| motorbike | 77.821 | 77.701 | 78.245 | 75.993 | 73.931 | 74.920 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| person | 82.204 | 79.112 | 79.819 | 79.001 | 81.047 | 81.817 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| pottedplant | 63.792 | 62.055 | 56.806 | 57.454 | 55.431 | 62.139 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| sheep | 89.118 | 84.064 | 87.188 | 83.504 | 87.575 | 88.672 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| sofa | 64.342 | 68.039 | 63.195 | 63.314 | 52.583 | 52.962 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| train | 63.087 | 62.943 | 62.764 | 64.435 | 62.165 | 62.726 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| tvmonitor | 68.754 | 70.542 | 57.406 | 66.599 | 66.045 | 66.518 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| m-Precision | 84.976 | 80.915 | 83.021 | 88.982 | 83.674 | 83.140 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| m-Recall | 86.168 | 84.965 | 85.688 | 79.753 | 82.103 | 84.138 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| m-ConfutionRati | 0.212 | 0.289 | 0.244 | 0.147 | 0.233 | 0.246 |
| o | | | | | | |
+-----------------+--------+---------+-----------+--------+----------+---------+
| m-IoU | 74.718 | 70.835 | 72.976 | 72.483 | 71.526 | 72.108 |
+-----------------+--------+---------+-----------+--------+----------+---------+
2024-07-01 20:10:02,958 - train_voc.py - INFO: Iter: 16200; Elasped: 5:07:18; ETA: 1:12:04; LR: 1.346e-05; cls_loss: 0.0362, cls_loss_aux: 0.0314, cls_loss_mct: 0.0011, ptc_loss: 0.0378, cle_loss: 7.8616, une_loss: 0.4824, seg_loss: 0.0706...
2024-07-01 20:13:05,707 - train_voc.py - INFO: Iter: 16400; Elasped: 5:10:21; ETA: 1:08:07; LR: 1.282e-05; cls_loss: 0.0355, cls_loss_aux: 0.0333, cls_loss_mct: 0.0011, ptc_loss: 0.0391, cle_loss: 7.6889, une_loss: 0.4834, seg_loss: 0.0690...
2024-07-01 20:16:09,074 - train_voc.py - INFO: Iter: 16600; Elasped: 5:13:25; ETA: 1:04:11; LR: 1.218e-05; cls_loss: 0.0359, cls_loss_aux: 0.0323, cls_loss_mct: 0.0011, ptc_loss: 0.0406, cle_loss: 7.7342, une_loss: 0.4828, seg_loss: 0.0664...
2024-07-01 20:19:11,953 - train_voc.py - INFO: Iter: 16800; Elasped: 5:16:27; ETA: 1:00:16; LR: 1.153e-05; cls_loss: 0.0339, cls_loss_aux: 0.0320, cls_loss_mct: 0.0013, ptc_loss: 0.0360, cle_loss: 7.4465, une_loss: 0.4830, seg_loss: 0.0652...
2024-07-01 20:22:15,197 - train_voc.py - INFO: Iter: 17000; Elasped: 5:19:31; ETA: 0:56:23; LR: 1.088e-05; cls_loss: 0.0359, cls_loss_aux: 0.0312, cls_loss_mct: 0.0011, ptc_loss: 0.0347, cle_loss: 7.7487, une_loss: 0.4826, seg_loss: 0.0689...
2024-07-01 20:25:18,447 - train_voc.py - INFO: Iter: 17200; Elasped: 5:22:34; ETA: 0:52:30; LR: 1.023e-05; cls_loss: 0.0316, cls_loss_aux: 0.0282, cls_loss_mct: 0.0010, ptc_loss: 0.0374, cle_loss: 7.8018, une_loss: 0.4823, seg_loss: 0.0670...
2024-07-01 20:28:21,905 - train_voc.py - INFO: Iter: 17400; Elasped: 5:25:37; ETA: 0:48:39; LR: 9.569e-06; cls_loss: 0.0403, cls_loss_aux: 0.0367, cls_loss_mct: 0.0010, ptc_loss: 0.0381, cle_loss: 7.8838, une_loss: 0.4827, seg_loss: 0.0611...
2024-07-01 20:31:25,546 - train_voc.py - INFO: Iter: 17600; Elasped: 5:28:41; ETA: 0:44:49; LR: 8.904e-06; cls_loss: 0.0325, cls_loss_aux: 0.0298, cls_loss_mct: 0.0010, ptc_loss: 0.0378, cle_loss: 7.7264, une_loss: 0.4820, seg_loss: 0.0681...
2024-07-01 20:34:28,918 - train_voc.py - INFO: Iter: 17800; Elasped: 5:31:44; ETA: 0:41:00; LR: 8.233e-06; cls_loss: 0.0381, cls_loss_aux: 0.0350, cls_loss_mct: 0.0009, ptc_loss: 0.0378, cle_loss: 7.8155, une_loss: 0.4819, seg_loss: 0.0660...
2024-07-01 20:37:32,661 - train_voc.py - INFO: Iter: 18000; Elasped: 5:34:48; ETA: 0:37:12; LR: 7.557e-06; cls_loss: 0.0365, cls_loss_aux: 0.0337, cls_loss_mct: 0.0009, ptc_loss: 0.0388, cle_loss: 8.0857, une_loss: 0.4821, seg_loss: 0.0741...
2024-07-01 20:37:38,453 - train_voc.py - INFO: Validating...
2024-07-01 20:43:51,347 - train_voc.py - INFO: val cls score: 0.928591
2024-07-01 20:43:51,347 - train_voc.py - INFO:
+-----------------+--------+---------+-----------+--------+----------+---------+
| Class | CAM | aux_CAM | Score Map | Fused | Seg_Pred | Seg_CAM |
+=================+========+=========+===========+========+==========+=========+
| _background_ | 92.323 | 89.752 | 91.704 | 91.672 | 92.156 | 92.066 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| aeroplane | 79.307 | 78.347 | 79.635 | 81.965 | 83.249 | 78.986 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| bicycle | 44.372 | 44.355 | 44.101 | 47.585 | 39.439 | 43.451 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| bird | 87.500 | 80.493 | 86.509 | 88.668 | 87.994 | 87.576 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| boat | 72.133 | 63.581 | 72.577 | 73.407 | 70.567 | 68.871 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| bottle | 79.706 | 62.290 | 75.413 | 77.036 | 78.661 | 79.510 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| bus | 89.904 | 85.681 | 87.849 | 87.636 | 87.191 | 89.142 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| car | 83.270 | 70.919 | 81.552 | 81.443 | 82.692 | 82.543 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| cat | 81.609 | 72.788 | 82.735 | 74.483 | 85.908 | 80.950 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| chair | 54.288 | 35.992 | 51.594 | 51.240 | 39.872 | 40.204 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| cow | 86.421 | 78.175 | 84.389 | 80.218 | 87.285 | 85.827 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| diningtable | 57.599 | 56.834 | 59.089 | 50.908 | 55.338 | 55.229 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| dog | 88.166 | 81.542 | 87.079 | 84.316 | 85.537 | 85.869 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| horse | 86.000 | 71.835 | 84.117 | 80.865 | 83.959 | 83.520 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| motorbike | 79.572 | 77.681 | 79.784 | 77.814 | 77.688 | 78.436 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| person | 84.280 | 81.144 | 81.970 | 82.452 | 82.462 | 83.518 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| pottedplant | 67.011 | 65.960 | 59.910 | 62.440 | 59.323 | 63.604 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| sheep | 87.952 | 80.675 | 85.986 | 81.591 | 86.453 | 87.086 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| sofa | 63.387 | 67.829 | 61.621 | 61.644 | 49.465 | 52.901 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| train | 65.430 | 64.375 | 64.553 | 65.133 | 64.765 | 65.233 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| tvmonitor | 66.744 | 70.852 | 58.354 | 66.495 | 64.953 | 63.278 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| m-Precision | 85.005 | 81.280 | 83.207 | 88.952 | 83.990 | 83.121 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| m-Recall | 87.708 | 84.210 | 87.151 | 81.075 | 84.194 | 86.000 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| m-ConfutionRati | 0.212 | 0.290 | 0.242 | 0.148 | 0.232 | 0.249 |
| o | | | | | | |
+-----------------+--------+---------+-----------+--------+----------+---------+
| m-IoU | 76.046 | 70.529 | 74.310 | 73.762 | 73.569 | 73.705 |
+-----------------+--------+---------+-----------+--------+----------+---------+
2024-07-01 20:46:49,398 - train_voc.py - INFO: Iter: 18200; Elasped: 5:44:05; ETA: 0:34:01; LR: 6.874e-06; cls_loss: 0.0339, cls_loss_aux: 0.0298, cls_loss_mct: 0.0008, ptc_loss: 0.0364, cle_loss: 7.7300, une_loss: 0.4824, seg_loss: 0.0657...
2024-07-01 20:49:51,366 - train_voc.py - INFO: Iter: 18400; Elasped: 5:47:07; ETA: 0:30:11; LR: 6.183e-06; cls_loss: 0.0370, cls_loss_aux: 0.0330, cls_loss_mct: 0.0010, ptc_loss: 0.0360, cle_loss: 7.6140, une_loss: 0.4823, seg_loss: 0.0684...
2024-07-01 20:52:56,168 - train_voc.py - INFO: Iter: 18600; Elasped: 5:50:12; ETA: 0:26:21; LR: 5.483e-06; cls_loss: 0.0343, cls_loss_aux: 0.0294, cls_loss_mct: 0.0009, ptc_loss: 0.0402, cle_loss: 7.7911, une_loss: 0.4823, seg_loss: 0.0676...
2024-07-01 20:55:59,782 - train_voc.py - INFO: Iter: 18800; Elasped: 5:53:15; ETA: 0:22:32; LR: 4.773e-06; cls_loss: 0.0339, cls_loss_aux: 0.0276, cls_loss_mct: 0.0008, ptc_loss: 0.0364, cle_loss: 7.9039, une_loss: 0.4817, seg_loss: 0.0645...
2024-07-01 20:59:03,519 - train_voc.py - INFO: Iter: 19000; Elasped: 5:56:19; ETA: 0:18:45; LR: 4.051e-06; cls_loss: 0.0293, cls_loss_aux: 0.0251, cls_loss_mct: 0.0008, ptc_loss: 0.0364, cle_loss: 7.6872, une_loss: 0.4818, seg_loss: 0.0604...
2024-07-01 21:02:06,620 - train_voc.py - INFO: Iter: 19200; Elasped: 5:59:22; ETA: 0:14:58; LR: 3.315e-06; cls_loss: 0.0322, cls_loss_aux: 0.0262, cls_loss_mct: 0.0008, ptc_loss: 0.0403, cle_loss: 7.8247, une_loss: 0.4825, seg_loss: 0.0632...
2024-07-01 21:05:09,374 - train_voc.py - INFO: Iter: 19400; Elasped: 6:02:25; ETA: 0:11:12; LR: 2.560e-06; cls_loss: 0.0273, cls_loss_aux: 0.0234, cls_loss_mct: 0.0007, ptc_loss: 0.0340, cle_loss: 7.6039, une_loss: 0.4813, seg_loss: 0.0601...
2024-07-01 21:08:12,652 - train_voc.py - INFO: Iter: 19600; Elasped: 6:05:28; ETA: 0:07:27; LR: 1.779e-06; cls_loss: 0.0293, cls_loss_aux: 0.0243, cls_loss_mct: 0.0007, ptc_loss: 0.0333, cle_loss: 7.7823, une_loss: 0.4812, seg_loss: 0.0600...
2024-07-01 21:11:16,199 - train_voc.py - INFO: Iter: 19800; Elasped: 6:08:32; ETA: 0:03:43; LR: 9.552e-07; cls_loss: 0.0338, cls_loss_aux: 0.0280, cls_loss_mct: 0.0007, ptc_loss: 0.0382, cle_loss: 7.9162, une_loss: 0.4815, seg_loss: 0.0650...
2024-07-01 21:14:20,215 - train_voc.py - INFO: Iter: 20000; Elasped: 6:11:36; ETA: 0:00:00; LR: 8.077e-09; cls_loss: 0.0333, cls_loss_aux: 0.0279, cls_loss_mct: 0.0007, ptc_loss: 0.0389, cle_loss: 7.8708, une_loss: 0.4809, seg_loss: 0.0727...
2024-07-01 21:14:25,434 - train_voc.py - INFO: Validating...
2024-07-01 21:20:38,260 - train_voc.py - INFO: val cls score: 0.927642
2024-07-01 21:20:38,261 - train_voc.py - INFO:
+-----------------+--------+---------+-----------+--------+----------+---------+
| Class | CAM | aux_CAM | Score Map | Fused | Seg_Pre d | Seg_CAM |
+=================+========+=========+===========+========+==========+=========+
| _background_ | 91.878 | 89.633 | 91.267 | 91.185 | 91.669 | 91.573 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| aeroplane | 78.418 | 77.248 | 78.879 | 81.956 | 79.316 | 76.992 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| bicycle | 43.462 | 43.507 | 43.675 | 47.080 | 39.761 | 42.723 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| bird | 87.437 | 81.795 | 85.841 | 88.025 | 88.062 | 87.593 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| boat | 69.395 | 62.658 | 70.220 | 72.292 | 67.874 | 65.076 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| bottle | 79.779 | 64.248 | 74.619 | 76.258 | 78.577 | 79.563 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| bus | 89.372 | 85.217 | 87.524 | 86.901 | 87.513 | 88.301 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| car | 83.085 | 72.606 | 80.831 | 81.809 | 81.720 | 81.671 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| cat | 75.127 | 67.229 | 77.268 | 67.107 | 78.470 | 74.306 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| chair | 53.714 | 37.585 | 51.494 | 52.404 | 40.103 | 39.606 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| cow | 84.530 | 73.755 | 83.175 | 75.579 | 83.460 | 80.166 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| diningtable | 50.946 | 54.138 | 50.758 | 43.505 | 46.795 | 46.908 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| dog | 86.813 | 81.311 | 85.882 | 82.265 | 81.232 | 83.240 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| horse | 86.578 | 75.672 | 84.793 | 82.120 | 83.680 | 83.471 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| motorbike | 79.381 | 77.319 | 79.368 | 77.966 | 77.531 | 77.680 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| person | 83.617 | 80.700 | 81.241 | 81.669 | 81.486 | 82.959 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| pottedplant | 67.315 | 67.291 | 61.844 | 61.987 | 58.990 | 64.507 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| sheep | 87.999 | 81.206 | 85.545 | 81.007 | 86.219 | 86.944 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| sofa | 64.391 | 67.869 | 63.881 | 63.030 | 52.997 | 54.656 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| train | 64.337 | 64.330 | 63.973 | 64.752 | 63.418 | 64.113 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| tvmonitor | 67.564 | 70.415 | 58.491 | 66.085 | 65.681 | 65.594 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| m-Precision | 84.738 | 81.420 | 82.838 | 88.593 | 83.354 | 82.749 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| m-Recall | 86.951 | 83.990 | 86.608 | 80.360 | 83.451 | 84.941 |
+-----------------+--------+---------+-----------+--------+----------+---------+
| m-ConfutionRati | 0.219 | 0.288 | 0.250 | 0.154 | 0.244 | 0.259 |
| o | | | | | | |
+-----------------+--------+---------+-----------+--------+----------+---------+
| m-IoU | 75.007 | 70.273 | 73.360 | 72.618 | 72.122 | 72.269 |
+-----------------+--------+---------+-----------+--------+----------+---------+