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Thank you for your excellent work. On my own dataset, PQ calculation of stuff class is wrong. Can you tell me what else I should pay attention to?
2020-06-19 19:17:10,088 | upsnet_end2end_test.py | line 158: test config:{'dataset': {'dataset': 'coco', 'dataset_path': './data/coco/', 'image_set': 'train2017', 'num_classes': 4, 'num_seg_classes': 6, 'root_path': './data/', 'test_image_set': 'val2017'}, 'debug_mode': False, 'gpus': '1', 'model_prefix': 'upsnet_resnet_50_coco_', 'network': {'anchor_ratios': [0.5, 1, 2], 'anchor_scales': [8], 'backbone_fix_bn': True, 'backbone_freeze_at': 2, 'backbone_with_dconv': 100, 'backbone_with_dilation': False, 'backbone_with_dpyramid': False, 'bbox_reg_weights': [10.0, 10.0, 5.0, 5.0], 'binary_thresh': 0.5, 'cls_agnostic_bbox_reg': False, 'fcn_head': 'FCNHead', 'fcn_num_layers': 3, 'fcn_with_norm': 'none', 'fpn_feature_dim': 256, 'fpn_upsample_method': 'nearest', 'fpn_with_gap': True, 'fpn_with_norm': 'none', 'has_fcn_head': True, 'has_fpn': True, 'has_mask_head': True, 'has_mask_rcnn': True, 'has_panoptic_head': True, 'has_rcnn': True, 'has_rpn': True, 'mask_size': 28, 'num_anchors': 3, 'pixel_means': array([102.9801, 115.9465, 122.7717]), 'pretrained': './model/pretrained_model/resnet-50-caffe.pth', 'rcnn_feat_stride': 32, 'rcnn_with_norm': 'none', 'rpn_feat_stride': [4, 8, 16, 32, 64], 'rpn_with_norm': 'none', 'use_caffe_model': True, 'use_syncbn': False}, 'output_path': './output/upsnet/coco', 'symbol': 'resnet_50_upsnet', 'test': {'batch_size': 1, 'max_det': 100, 'max_size': 2000, 'nms_thresh': 0.5, 'panoptic_score_thresh': 0.6, 'panoptic_stuff_area_limit': 500000, 'rpn_min_size': 0, 'rpn_nms_thresh': 0.7, 'rpn_post_nms_top_n': 1000, 'rpn_pre_nms_top_n': 1000, 'scales': [1200], 'score_thresh': 0.05, 'test_iteration': 90000, 'vis_mask': False}, 'train': {'batch_rois': 512, 'batch_size': 1, 'bbox_loss_weight': 1.0, 'bbox_means': [0.0, 0.0, 0.0, 0.0], 'bbox_normalization_precomputed': True, 'bbox_regression_thresh': 0.5, 'bbox_stds': [0.1, 0.1, 0.2, 0.2], 'bbox_thresh': 0.5, 'bbox_weights': array([1., 1., 1., 1.]), 'begin_iteration': 0, 'bg_thresh_hi': 0.5, 'bg_thresh_lo': 0.0, 'crowd_filter_thresh': 0.7, 'decay_iteration': [60000, 80000], 'display_iter': 20, 'eval_data': True, 'fcn_focal_loss_gamma': 1.0, 'fcn_loss_weight': 0.2, 'fcn_use_focal_loss': False, 'fcn_with_negative_loss': False, 'fcn_with_roi_loss': True, 'fg_fraction': 0.25, 'fg_thresh': 0.5, 'flip': True, 'gt_min_area': -1, 'lr': 0.00125, 'lr_schedule': 'step', 'max_iteration': 90000, 'max_size': 2000, 'momentum': 0.9, 'panoptic_box_keep_fraction': 0.7, 'panoptic_focal_loss_gamma': 1.0, 'panoptic_loss_weight': 0.1, 'panoptic_use_focal_loss': False, 'panoptic_with_roi_loss': False, 'resume': False, 'rpn_batch_size': 256, 'rpn_bbox_weights': [1.0, 1.0, 1.0, 1.0], 'rpn_clobber_positive': False, 'rpn_fg_fraction': 0.5, 'rpn_individual_proposals': True, 'rpn_min_size': 0, 'rpn_negative_overlap': 0.3, 'rpn_nms_thresh': 0.7, 'rpn_positive_overlap': 0.7, 'rpn_positive_weight': -1.0, 'rpn_post_nms_top_n': 2000, 'rpn_pre_nms_top_n': 2000, 'rpn_straddle_thresh': 0, 'scales': [1200], 'shuffle': True, 'snapshot_step': 2000, 'use_horovod': False, 'warmup_iteration': 1500, 'wd': 0.0001}}
2020-06-19 19:17:17,126 | json_dataset.py | line 63 : Creating: coco_val2017 2020-06-19 19:17:17,789 | upsnet_end2end_test.py | line 274: Batch 1/85, data_time:0.182, net_time:0.000, post_time:0.021 2020-06-19 19:17:18,166 | upsnet_end2end_test.py | line 274: Batch 2/85, data_time:0.157, net_time:0.000, post_time:0.019 2020-06-19 19:17:18,539 | upsnet_end2end_test.py | line 274: Batch 3/85, data_time:0.151, net_time:0.000, post_time:0.018 2020-06-19 19:17:18,911 | upsnet_end2end_test.py | line 274: Batch 4/85, data_time:0.149, net_time:0.000, post_time:0.017 2020-06-19 19:17:19,321 | upsnet_end2end_test.py | line 274: Batch 5/85, data_time:0.147, net_time:0.000, post_time:0.025 2020-06-19 19:17:19,744 | upsnet_end2end_test.py | line 274: Batch 6/85, data_time:0.145, net_time:0.000, post_time:0.030 2020-06-19 19:17:20,141 | upsnet_end2end_test.py | line 274: Batch 7/85, data_time:0.143, net_time:0.000, post_time:0.032 2020-06-19 19:17:20,546 | upsnet_end2end_test.py | line 274: Batch 8/85, data_time:0.141, net_time:0.000, post_time:0.034 2020-06-19 19:17:20,958 | upsnet_end2end_test.py | line 274: Batch 9/85, data_time:0.142, net_time:0.000, post_time:0.034 2020-06-19 19:17:21,357 | upsnet_end2end_test.py | line 274: Batch 10/85, data_time:0.143, net_time:0.000, post_time:0.034 2020-06-19 19:17:21,749 | upsnet_end2end_test.py | line 274: Batch 11/85, data_time:0.141, net_time:0.214, post_time:0.035 2020-06-19 19:17:22,140 | upsnet_end2end_test.py | line 274: Batch 12/85, data_time:0.141, net_time:0.214, post_time:0.034 2020-06-19 19:17:22,561 | upsnet_end2end_test.py | line 274: Batch 13/85, data_time:0.141, net_time:0.213, post_time:0.036 2020-06-19 19:17:22,970 | upsnet_end2end_test.py | line 274: Batch 14/85, data_time:0.140, net_time:0.214, post_time:0.037 2020-06-19 19:17:23,388 | upsnet_end2end_test.py | line 274: Batch 15/85, data_time:0.140, net_time:0.214, post_time:0.038 2020-06-19 19:17:23,785 | upsnet_end2end_test.py | line 274: Batch 16/85, data_time:0.139, net_time:0.215, post_time:0.039 2020-06-19 19:17:24,204 | upsnet_end2end_test.py | line 274: Batch 17/85, data_time:0.139, net_time:0.215, post_time:0.039 2020-06-19 19:17:24,633 | upsnet_end2end_test.py | line 274: Batch 18/85, data_time:0.139, net_time:0.215, post_time:0.041 2020-06-19 19:17:25,016 | upsnet_end2end_test.py | line 274: Batch 19/85, data_time:0.138, net_time:0.215, post_time:0.040 2020-06-19 19:17:25,393 | upsnet_end2end_test.py | line 274: Batch 20/85, data_time:0.138, net_time:0.215, post_time:0.039 2020-06-19 19:17:25,780 | upsnet_end2end_test.py | line 274: Batch 21/85, data_time:0.137, net_time:0.215, post_time:0.039 2020-06-19 19:17:26,151 | upsnet_end2end_test.py | line 274: Batch 22/85, data_time:0.137, net_time:0.215, post_time:0.039 2020-06-19 19:17:26,536 | upsnet_end2end_test.py | line 274: Batch 23/85, data_time:0.136, net_time:0.215, post_time:0.039 2020-06-19 19:17:26,913 | upsnet_end2end_test.py | line 274: Batch 24/85, data_time:0.135, 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19:17:37,092 | upsnet_end2end_test.py | line 274: Batch 49/85, data_time:0.135, net_time:0.215, post_time:0.043 2020-06-19 19:17:37,502 | upsnet_end2end_test.py | line 274: Batch 50/85, data_time:0.135, net_time:0.216, post_time:0.043 2020-06-19 19:17:37,882 | upsnet_end2end_test.py | line 274: Batch 51/85, data_time:0.135, net_time:0.216, post_time:0.043 2020-06-19 19:17:38,294 | upsnet_end2end_test.py | line 274: Batch 52/85, data_time:0.134, net_time:0.216, post_time:0.043 2020-06-19 19:17:38,692 | upsnet_end2end_test.py | line 274: Batch 53/85, data_time:0.134, net_time:0.216, post_time:0.043 2020-06-19 19:17:39,063 | upsnet_end2end_test.py | line 274: Batch 54/85, data_time:0.134, net_time:0.216, post_time:0.043 2020-06-19 19:17:39,447 | upsnet_end2end_test.py | line 274: Batch 55/85, data_time:0.134, net_time:0.216, post_time:0.043 2020-06-19 19:17:39,816 | upsnet_end2end_test.py | line 274: Batch 56/85, data_time:0.133, net_time:0.216, post_time:0.043 2020-06-19 19:17:40,184 | 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upsnet_end2end_test.py | line 274: Batch 81/85, data_time:0.132, net_time:0.216, post_time:0.042 2020-06-19 19:17:50,072 | upsnet_end2end_test.py | line 274: Batch 82/85, data_time:0.132, net_time:0.216, post_time:0.042 2020-06-19 19:17:50,446 | upsnet_end2end_test.py | line 274: Batch 83/85, data_time:0.132, net_time:0.216, post_time:0.042 2020-06-19 19:17:50,817 | upsnet_end2end_test.py | line 274: Batch 84/85, data_time:0.131, net_time:0.216, post_time:0.042 2020-06-19 19:17:51,193 | upsnet_end2end_test.py | line 274: Batch 85/85, data_time:0.131, net_time:0.216, post_time:0.042 2020-06-19 19:17:55,412 | base_dataset.py | line 194: Writing bbox results json to: /media/mubai/backupdata/UPSNet/output/upsnet/coco/upsnet_resnet50_coco_4gpu/val2017/results/bbox_coco_val2017_results.json 2020-06-19 19:17:55,687 | base_dataset.py | line 706: ~~~~ Mean and per-category AP @ IoU=[0.50,0.95] ~~~~ 2020-06-19 19:17:55,687 | base_dataset.py | line 714: 0.815 2020-06-19 19:17:55,688 | base_dataset.py | line 714: 0.693 2020-06-19 19:17:55,688 | base_dataset.py | line 714: 0.252 2020-06-19 19:17:55,688 | base_dataset.py | line 716: 0.587 2020-06-19 19:17:55,688 | base_dataset.py | line 706: ~~~~ Mean and per-category AP @ IoU=[0.50,0.50] ~~~~ 2020-06-19 19:17:55,688 | base_dataset.py | line 714: 0.986 2020-06-19 19:17:55,688 | base_dataset.py | line 714: 0.945 2020-06-19 19:17:55,689 | base_dataset.py | line 714: 0.505 2020-06-19 19:17:55,689 | base_dataset.py | line 716: 0.812 2020-06-19 19:17:55,689 | base_dataset.py | line 718: ~~~~ Summary metrics ~~~~ 2020-06-19 19:17:55,692 | coco.py | line 214: Writing segmentation results json to: /media/mubai/backupdata/UPSNet/output/upsnet/coco/upsnet_resnet50_coco_4gpu/val2017/results/segmentations_coco_val2017_results.json 2020-06-19 19:17:56,087 | base_dataset.py | line 706: ~~~~ Mean and per-category AP @ IoU=[0.50,0.95] ~~~~ 2020-06-19 19:17:56,088 | base_dataset.py | line 714: 0.768 2020-06-19 19:17:56,088 | base_dataset.py | line 714: 0.626 2020-06-19 19:17:56,088 | base_dataset.py | line 714: 0.000 2020-06-19 19:17:56,088 | base_dataset.py | line 716: 0.465 2020-06-19 19:17:56,089 | base_dataset.py | line 706: ~~~~ Mean and per-category AP @ IoU=[0.50,0.50] ~~~~ 2020-06-19 19:17:56,089 | base_dataset.py | line 714: 0.986 2020-06-19 19:17:56,089 | base_dataset.py | line 714: 0.944 2020-06-19 19:17:56,089 | base_dataset.py | line 714: 0.000 2020-06-19 19:17:56,089 | base_dataset.py | line 716: 0.643 2020-06-19 19:17:56,089 | base_dataset.py | line 718: ~~~~ Summary metrics ~~~~ 2020-06-19 19:17:56,092 | upsnet_end2end_test.py | line 307: unified pano result: 2020-06-19 19:18:25,855 | base_dataset.py | line 301: | PQ SQ RQ N 2020-06-19 19:18:25,855 | base_dataset.py | line 302: -------------------------------------- 2020-06-19 19:18:25,855 | base_dataset.py | line 304: All | 2157.7 2206.5 63.1 6 2020-06-19 19:18:25,855 | base_dataset.py | line 304: Things | 52.1 56.1 61.8 3 2020-06-19 19:18:25,855 | base_dataset.py | line 304: Stuff | 4263.4 4356.8 64.4 3 2020-06-19 19:18:25,855 | base_dataset.py | line 306: IDX | PQ SQ RQ IoU TP FP FN 2020-06-19 19:18:25,856 | base_dataset.py | line 309: 0 | 10119.1 10241.0 98.8 8500.1 83 1 1 2020-06-19 19:18:25,856 | base_dataset.py | line 309: 1 | 0.0 0.0 0.0 0.0 0 0 30 2020-06-19 19:18:25,856 | base_dataset.py | line 309: 2 | 2671.2 2829.4 94.4 2150.3 76 1 8 2020-06-19 19:18:25,856 | base_dataset.py | line 309: 3 | 81.1 86.9 93.3 6.1 7 0 1 2020-06-19 19:18:25,856 | base_dataset.py | line 309: 4 | 75.0 81.5 92.1 502.6 617 43 63 2020-06-19 19:18:25,856 | base_dataset.py | line 309: 5 | 0.0 0.0 0.0 0.0 0 1 2 2020-06-19 19:18:30,706 | coco.py | line 260: evaluate segmentation: 2020-06-19 19:18:30,706 | coco.py | line 264: IU_array: 2020-06-19 19:18:30,706 | coco.py | line 266: 0.98239 2020-06-19 19:18:30,706 | coco.py | line 266: 0.87430 2020-06-19 19:18:30,707 | coco.py | line 266: 0.97881 2020-06-19 19:18:30,707 | coco.py | line 266: 0.75718 2020-06-19 19:18:30,707 | coco.py | line 266: 0.61269 2020-06-19 19:18:30,707 | coco.py | line 266: 0.08025 2020-06-19 19:18:30,707 | coco.py | line 267: meanIU:0.71427 2020-06-19 19:18:30,708 | coco.py | line 271: confusion_matrix: 2020-06-19 19:18:30,708 | coco.py | line 272: 0.996 0. 0.001 0. 0.002 0. 0.001 0.943 0.04 0. 0.015 0. 0. 0. 0.998 0. 0.001 0. 0.14 0. 0. 0.817 0.043 0. 0.164 0.011 0.184 0. 0.642 0. 0.276 0. 0. 0. 0.639 0.085
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Thank you for your excellent work. On my own dataset, PQ calculation of stuff class is wrong. Can you tell me what else I should pay attention to?
2020-06-19 19:17:10,088 | upsnet_end2end_test.py | line 158: test config:{'dataset': {'dataset': 'coco',
'dataset_path': './data/coco/',
'image_set': 'train2017',
'num_classes': 4,
'num_seg_classes': 6,
'root_path': './data/',
'test_image_set': 'val2017'},
'debug_mode': False,
'gpus': '1',
'model_prefix': 'upsnet_resnet_50_coco_',
'network': {'anchor_ratios': [0.5, 1, 2],
'anchor_scales': [8],
'backbone_fix_bn': True,
'backbone_freeze_at': 2,
'backbone_with_dconv': 100,
'backbone_with_dilation': False,
'backbone_with_dpyramid': False,
'bbox_reg_weights': [10.0, 10.0, 5.0, 5.0],
'binary_thresh': 0.5,
'cls_agnostic_bbox_reg': False,
'fcn_head': 'FCNHead',
'fcn_num_layers': 3,
'fcn_with_norm': 'none',
'fpn_feature_dim': 256,
'fpn_upsample_method': 'nearest',
'fpn_with_gap': True,
'fpn_with_norm': 'none',
'has_fcn_head': True,
'has_fpn': True,
'has_mask_head': True,
'has_mask_rcnn': True,
'has_panoptic_head': True,
'has_rcnn': True,
'has_rpn': True,
'mask_size': 28,
'num_anchors': 3,
'pixel_means': array([102.9801, 115.9465, 122.7717]),
'pretrained': './model/pretrained_model/resnet-50-caffe.pth',
'rcnn_feat_stride': 32,
'rcnn_with_norm': 'none',
'rpn_feat_stride': [4, 8, 16, 32, 64],
'rpn_with_norm': 'none',
'use_caffe_model': True,
'use_syncbn': False},
'output_path': './output/upsnet/coco',
'symbol': 'resnet_50_upsnet',
'test': {'batch_size': 1,
'max_det': 100,
'max_size': 2000,
'nms_thresh': 0.5,
'panoptic_score_thresh': 0.6,
'panoptic_stuff_area_limit': 500000,
'rpn_min_size': 0,
'rpn_nms_thresh': 0.7,
'rpn_post_nms_top_n': 1000,
'rpn_pre_nms_top_n': 1000,
'scales': [1200],
'score_thresh': 0.05,
'test_iteration': 90000,
'vis_mask': False},
'train': {'batch_rois': 512,
'batch_size': 1,
'bbox_loss_weight': 1.0,
'bbox_means': [0.0, 0.0, 0.0, 0.0],
'bbox_normalization_precomputed': True,
'bbox_regression_thresh': 0.5,
'bbox_stds': [0.1, 0.1, 0.2, 0.2],
'bbox_thresh': 0.5,
'bbox_weights': array([1., 1., 1., 1.]),
'begin_iteration': 0,
'bg_thresh_hi': 0.5,
'bg_thresh_lo': 0.0,
'crowd_filter_thresh': 0.7,
'decay_iteration': [60000, 80000],
'display_iter': 20,
'eval_data': True,
'fcn_focal_loss_gamma': 1.0,
'fcn_loss_weight': 0.2,
'fcn_use_focal_loss': False,
'fcn_with_negative_loss': False,
'fcn_with_roi_loss': True,
'fg_fraction': 0.25,
'fg_thresh': 0.5,
'flip': True,
'gt_min_area': -1,
'lr': 0.00125,
'lr_schedule': 'step',
'max_iteration': 90000,
'max_size': 2000,
'momentum': 0.9,
'panoptic_box_keep_fraction': 0.7,
'panoptic_focal_loss_gamma': 1.0,
'panoptic_loss_weight': 0.1,
'panoptic_use_focal_loss': False,
'panoptic_with_roi_loss': False,
'resume': False,
'rpn_batch_size': 256,
'rpn_bbox_weights': [1.0, 1.0, 1.0, 1.0],
'rpn_clobber_positive': False,
'rpn_fg_fraction': 0.5,
'rpn_individual_proposals': True,
'rpn_min_size': 0,
'rpn_negative_overlap': 0.3,
'rpn_nms_thresh': 0.7,
'rpn_positive_overlap': 0.7,
'rpn_positive_weight': -1.0,
'rpn_post_nms_top_n': 2000,
'rpn_pre_nms_top_n': 2000,
'rpn_straddle_thresh': 0,
'scales': [1200],
'shuffle': True,
'snapshot_step': 2000,
'use_horovod': False,
'warmup_iteration': 1500,
'wd': 0.0001}}
2020-06-19 19:17:17,126 | json_dataset.py | line 63 : Creating: coco_val2017
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2020-06-19 19:17:51,193 | upsnet_end2end_test.py | line 274: Batch 85/85, data_time:0.131, net_time:0.216, post_time:0.042
2020-06-19 19:17:55,412 | base_dataset.py | line 194: Writing bbox results json to: /media/mubai/backupdata/UPSNet/output/upsnet/coco/upsnet_resnet50_coco_4gpu/val2017/results/bbox_coco_val2017_results.json
2020-06-19 19:17:55,687 | base_dataset.py | line 706: ~~~~ Mean and per-category AP @ IoU=[0.50,0.95] ~~~~
2020-06-19 19:17:55,687 | base_dataset.py | line 714: 0.815
2020-06-19 19:17:55,688 | base_dataset.py | line 714: 0.693
2020-06-19 19:17:55,688 | base_dataset.py | line 714: 0.252
2020-06-19 19:17:55,688 | base_dataset.py | line 716: 0.587
2020-06-19 19:17:55,688 | base_dataset.py | line 706: ~~~~ Mean and per-category AP @ IoU=[0.50,0.50] ~~~~
2020-06-19 19:17:55,688 | base_dataset.py | line 714: 0.986
2020-06-19 19:17:55,688 | base_dataset.py | line 714: 0.945
2020-06-19 19:17:55,689 | base_dataset.py | line 714: 0.505
2020-06-19 19:17:55,689 | base_dataset.py | line 716: 0.812
2020-06-19 19:17:55,689 | base_dataset.py | line 718: ~~~~ Summary metrics ~~~~
2020-06-19 19:17:55,692 | coco.py | line 214: Writing segmentation results json to: /media/mubai/backupdata/UPSNet/output/upsnet/coco/upsnet_resnet50_coco_4gpu/val2017/results/segmentations_coco_val2017_results.json
2020-06-19 19:17:56,087 | base_dataset.py | line 706: ~~~~ Mean and per-category AP @ IoU=[0.50,0.95] ~~~~
2020-06-19 19:17:56,088 | base_dataset.py | line 714: 0.768
2020-06-19 19:17:56,088 | base_dataset.py | line 714: 0.626
2020-06-19 19:17:56,088 | base_dataset.py | line 714: 0.000
2020-06-19 19:17:56,088 | base_dataset.py | line 716: 0.465
2020-06-19 19:17:56,089 | base_dataset.py | line 706: ~~~~ Mean and per-category AP @ IoU=[0.50,0.50] ~~~~
2020-06-19 19:17:56,089 | base_dataset.py | line 714: 0.986
2020-06-19 19:17:56,089 | base_dataset.py | line 714: 0.944
2020-06-19 19:17:56,089 | base_dataset.py | line 714: 0.000
2020-06-19 19:17:56,089 | base_dataset.py | line 716: 0.643
2020-06-19 19:17:56,089 | base_dataset.py | line 718: ~~~~ Summary metrics ~~~~
2020-06-19 19:17:56,092 | upsnet_end2end_test.py | line 307: unified pano result:
2020-06-19 19:18:25,855 | base_dataset.py | line 301: | PQ SQ RQ N
2020-06-19 19:18:25,855 | base_dataset.py | line 302: --------------------------------------
2020-06-19 19:18:25,855 | base_dataset.py | line 304: All | 2157.7 2206.5 63.1 6
2020-06-19 19:18:25,855 | base_dataset.py | line 304: Things | 52.1 56.1 61.8 3
2020-06-19 19:18:25,855 | base_dataset.py | line 304: Stuff | 4263.4 4356.8 64.4 3
2020-06-19 19:18:25,855 | base_dataset.py | line 306: IDX | PQ SQ RQ IoU TP FP FN
2020-06-19 19:18:25,856 | base_dataset.py | line 309: 0 | 10119.1 10241.0 98.8 8500.1 83 1 1
2020-06-19 19:18:25,856 | base_dataset.py | line 309: 1 | 0.0 0.0 0.0 0.0 0 0 30
2020-06-19 19:18:25,856 | base_dataset.py | line 309: 2 | 2671.2 2829.4 94.4 2150.3 76 1 8
2020-06-19 19:18:25,856 | base_dataset.py | line 309: 3 | 81.1 86.9 93.3 6.1 7 0 1
2020-06-19 19:18:25,856 | base_dataset.py | line 309: 4 | 75.0 81.5 92.1 502.6 617 43 63
2020-06-19 19:18:25,856 | base_dataset.py | line 309: 5 | 0.0 0.0 0.0 0.0 0 1 2
2020-06-19 19:18:30,706 | coco.py | line 260: evaluate segmentation:
2020-06-19 19:18:30,706 | coco.py | line 264: IU_array:
2020-06-19 19:18:30,706 | coco.py | line 266: 0.98239
2020-06-19 19:18:30,706 | coco.py | line 266: 0.87430
2020-06-19 19:18:30,707 | coco.py | line 266: 0.97881
2020-06-19 19:18:30,707 | coco.py | line 266: 0.75718
2020-06-19 19:18:30,707 | coco.py | line 266: 0.61269
2020-06-19 19:18:30,707 | coco.py | line 266: 0.08025
2020-06-19 19:18:30,707 | coco.py | line 267: meanIU:0.71427
2020-06-19 19:18:30,708 | coco.py | line 271: confusion_matrix:
2020-06-19 19:18:30,708 | coco.py | line 272: 0.996 0. 0.001 0. 0.002 0.
0.001 0.943 0.04 0. 0.015 0.
0. 0. 0.998 0. 0.001 0.
0.14 0. 0. 0.817 0.043 0.
0.164 0.011 0.184 0. 0.642 0.
0.276 0. 0. 0. 0.639 0.085
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