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cout_x152_ship_test.txt
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============================ Messages from Goddess ============================
* Job starting from: Tue Jan 3 21:35:23 CST 2023
* Job ID : 36151
* Job name : detectron_ship_mrcnn_x152_tsswd_ship_test
* Job partition : v100-32g
* Nodes : 1
* Cores : 4
* Working directory: ~/fatcat/TSSWD_training
===============================================================================
Loading python/3.8.10-gpu
Loading requirement: cuda/11.2
==== Registering DataSet ====
==== Registering CWB TEST DataSet ====
==== Registering WMO TEST DataSet ====
==== Registering Wind TEST DataSet ====
==== Registering Size DataSet ====
==== Finish Registering ====
==== Modifying Config ====
==== Config been Modified ====
====Start Evaluation====
====Start Evaluation on Val Set====
[32m[01/03 21:35:45 d2.data.datasets.coco]: [0mLoaded 290 images in COCO format from /home/eorslab/fatcat/SAR_DATASET/TSSWD/annotation/val/coco/annotation/val.json
[32m[01/03 21:35:45 d2.data.build]: [0mDistribution of instances among all 1 categories:
[36m| category | #instances |
|:----------:|:-------------|
| ship | 743 |
| | |[0m
[32m[01/03 21:35:45 d2.data.dataset_mapper]: [0m[DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(800, 800), max_size=1333, sample_style='choice')]
[32m[01/03 21:35:45 d2.data.common]: [0mSerializing 290 elements to byte tensors and concatenating them all ...
[32m[01/03 21:35:45 d2.data.common]: [0mSerialized dataset takes 0.19 MiB
[32m[01/03 21:35:45 d2.evaluation.evaluator]: [0mStart inference on 290 batches
[32m[01/03 21:35:46 d2.evaluation.evaluator]: [0mInference done 11/290. Dataloading: 0.0007 s/iter. Inference: 0.0789 s/iter. Eval: 0.0066 s/iter. Total: 0.0862 s/iter. ETA=0:00:24
[32m[01/03 21:35:51 d2.evaluation.evaluator]: [0mInference done 72/290. Dataloading: 0.0010 s/iter. Inference: 0.0789 s/iter. Eval: 0.0031 s/iter. Total: 0.0831 s/iter. ETA=0:00:18
[32m[01/03 21:35:56 d2.evaluation.evaluator]: [0mInference done 132/290. Dataloading: 0.0010 s/iter. Inference: 0.0789 s/iter. Eval: 0.0033 s/iter. Total: 0.0833 s/iter. ETA=0:00:13
[32m[01/03 21:36:01 d2.evaluation.evaluator]: [0mInference done 189/290. Dataloading: 0.0011 s/iter. Inference: 0.0790 s/iter. Eval: 0.0048 s/iter. Total: 0.0849 s/iter. ETA=0:00:08
[32m[01/03 21:36:06 d2.evaluation.evaluator]: [0mInference done 248/290. Dataloading: 0.0011 s/iter. Inference: 0.0790 s/iter. Eval: 0.0051 s/iter. Total: 0.0852 s/iter. ETA=0:00:03
[32m[01/03 21:36:10 d2.evaluation.evaluator]: [0mTotal inference time: 0:00:24.456846 (0.085813 s / iter per device, on 1 devices)
[32m[01/03 21:36:10 d2.evaluation.evaluator]: [0mTotal inference pure compute time: 0:00:22 (0.079070 s / iter per device, on 1 devices)
[32m[01/03 21:36:10 d2.evaluation.coco_evaluation]: [0mPreparing results for COCO format ...
[32m[01/03 21:36:10 d2.evaluation.coco_evaluation]: [0mSaving results to ./evaluation_output_x152/coco_instances_results.json
[32m[01/03 21:36:10 d2.evaluation.coco_evaluation]: [0mEvaluating predictions with unofficial COCO API...
Loading and preparing results...
DONE (t=0.00s)
creating index...
index created!
[32m[01/03 21:36:10 d2.evaluation.fast_eval_api]: [0mEvaluate annotation type *bbox*
[32m[01/03 21:36:10 d2.evaluation.fast_eval_api]: [0mCOCOeval_opt.evaluate() finished in 0.02 seconds.
[32m[01/03 21:36:10 d2.evaluation.fast_eval_api]: [0mAccumulating evaluation results...
[32m[01/03 21:36:10 d2.evaluation.fast_eval_api]: [0mCOCOeval_opt.accumulate() finished in 0.00 seconds.
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.493
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.927
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.477
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.493
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.228
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.495
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.555
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.555
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000
[32m[01/03 21:36:10 d2.evaluation.coco_evaluation]: [0mEvaluation results for bbox:
| AP | AP50 | AP75 | APs | APm | APl |
|:------:|:------:|:------:|:------:|:-----:|:-----:|
| 49.254 | 92.692 | 47.659 | 49.254 | nan | nan |
[32m[01/03 21:36:10 d2.evaluation.coco_evaluation]: [0mSome metrics cannot be computed and is shown as NaN.
Loading and preparing results...
DONE (t=0.01s)
creating index...
index created!
[32m[01/03 21:36:10 d2.evaluation.fast_eval_api]: [0mEvaluate annotation type *segm*
[32m[01/03 21:36:10 d2.evaluation.fast_eval_api]: [0mCOCOeval_opt.evaluate() finished in 0.03 seconds.
[32m[01/03 21:36:10 d2.evaluation.fast_eval_api]: [0mAccumulating evaluation results...
[32m[01/03 21:36:10 d2.evaluation.fast_eval_api]: [0mCOCOeval_opt.accumulate() finished in 0.00 seconds.
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.448
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.928
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.371
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.448
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.206
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.455
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.509
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.509
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000
[32m[01/03 21:36:10 d2.evaluation.coco_evaluation]: [0mEvaluation results for segm:
| AP | AP50 | AP75 | APs | APm | APl |
|:------:|:------:|:------:|:------:|:-----:|:-----:|
| 44.796 | 92.784 | 37.122 | 44.796 | nan | nan |
[32m[01/03 21:36:10 d2.evaluation.coco_evaluation]: [0mSome metrics cannot be computed and is shown as NaN.
OrderedDict([('bbox', {'AP': 49.25351488754723, 'AP50': 92.6916495002149, 'AP75': 47.65878421600041, 'APs': 49.25351488754723, 'APm': nan, 'APl': nan}), ('segm', {'AP': 44.79597523034881, 'AP50': 92.78355497039679, 'AP75': 37.12154258067631, 'APs': 44.79597523034881, 'APm': nan, 'APl': nan})])
====Start Evaluation on Test Set====
[32m[01/03 21:36:11 d2.data.datasets.coco]: [0mLoaded 290 images in COCO format from /home/eorslab/fatcat/SAR_DATASET/TSSWD/annotation/test/coco/annotation/test.json
[32m[01/03 21:36:11 d2.data.build]: [0mDistribution of instances among all 1 categories:
[36m| category | #instances |
|:----------:|:-------------|
| ship | 752 |
| | |[0m
[32m[01/03 21:36:11 d2.data.dataset_mapper]: [0m[DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(800, 800), max_size=1333, sample_style='choice')]
[32m[01/03 21:36:11 d2.data.common]: [0mSerializing 290 elements to byte tensors and concatenating them all ...
[32m[01/03 21:36:11 d2.data.common]: [0mSerialized dataset takes 0.20 MiB
[32m[01/03 21:36:11 d2.evaluation.evaluator]: [0mStart inference on 290 batches
[32m[01/03 21:36:12 d2.evaluation.evaluator]: [0mInference done 11/290. Dataloading: 0.0011 s/iter. Inference: 0.0821 s/iter. Eval: 0.0043 s/iter. Total: 0.0875 s/iter. ETA=0:00:24
[32m[01/03 21:36:17 d2.evaluation.evaluator]: [0mInference done 68/290. Dataloading: 0.0013 s/iter. Inference: 0.0822 s/iter. Eval: 0.0044 s/iter. Total: 0.0880 s/iter. ETA=0:00:19
[32m[01/03 21:36:22 d2.evaluation.evaluator]: [0mInference done 126/290. Dataloading: 0.0012 s/iter. Inference: 0.0812 s/iter. Eval: 0.0052 s/iter. Total: 0.0877 s/iter. ETA=0:00:14
[32m[01/03 21:36:27 d2.evaluation.evaluator]: [0mInference done 185/290. Dataloading: 0.0012 s/iter. Inference: 0.0805 s/iter. Eval: 0.0051 s/iter. Total: 0.0868 s/iter. ETA=0:00:09
[32m[01/03 21:36:32 d2.evaluation.evaluator]: [0mInference done 242/290. Dataloading: 0.0011 s/iter. Inference: 0.0801 s/iter. Eval: 0.0060 s/iter. Total: 0.0873 s/iter. ETA=0:00:04
[32m[01/03 21:36:36 d2.evaluation.evaluator]: [0mTotal inference time: 0:00:24.776750 (0.086936 s / iter per device, on 1 devices)
[32m[01/03 21:36:36 d2.evaluation.evaluator]: [0mTotal inference pure compute time: 0:00:22 (0.079905 s / iter per device, on 1 devices)
[32m[01/03 21:36:36 d2.evaluation.coco_evaluation]: [0mPreparing results for COCO format ...
[32m[01/03 21:36:36 d2.evaluation.coco_evaluation]: [0mSaving results to ./evaluation_output_x152/coco_instances_results.json
[32m[01/03 21:36:36 d2.evaluation.coco_evaluation]: [0mEvaluating predictions with unofficial COCO API...
Loading and preparing results...
DONE (t=0.00s)
creating index...
index created!
[32m[01/03 21:36:36 d2.evaluation.fast_eval_api]: [0mEvaluate annotation type *bbox*
[32m[01/03 21:36:36 d2.evaluation.fast_eval_api]: [0mCOCOeval_opt.evaluate() finished in 0.02 seconds.
[32m[01/03 21:36:36 d2.evaluation.fast_eval_api]: [0mAccumulating evaluation results...
[32m[01/03 21:36:36 d2.evaluation.fast_eval_api]: [0mCOCOeval_opt.accumulate() finished in 0.00 seconds.
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.512
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.931
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.506
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.512
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.241
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.547
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.574
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.574
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000
[32m[01/03 21:36:36 d2.evaluation.coco_evaluation]: [0mEvaluation results for bbox:
| AP | AP50 | AP75 | APs | APm | APl |
|:------:|:------:|:------:|:------:|:-----:|:-----:|
| 51.211 | 93.061 | 50.602 | 51.211 | nan | nan |
[32m[01/03 21:36:36 d2.evaluation.coco_evaluation]: [0mSome metrics cannot be computed and is shown as NaN.
Loading and preparing results...
DONE (t=0.01s)
creating index...
index created!
[32m[01/03 21:36:36 d2.evaluation.fast_eval_api]: [0mEvaluate annotation type *segm*
[32m[01/03 21:36:36 d2.evaluation.fast_eval_api]: [0mCOCOeval_opt.evaluate() finished in 0.03 seconds.
[32m[01/03 21:36:36 d2.evaluation.fast_eval_api]: [0mAccumulating evaluation results...
[32m[01/03 21:36:36 d2.evaluation.fast_eval_api]: [0mCOCOeval_opt.accumulate() finished in 0.00 seconds.
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.465
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.930
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.400
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.465
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.220
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.502
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.529
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.529
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000
[32m[01/03 21:36:36 d2.evaluation.coco_evaluation]: [0mEvaluation results for segm:
| AP | AP50 | AP75 | APs | APm | APl |
|:------:|:------:|:------:|:------:|:-----:|:-----:|
| 46.512 | 93.040 | 39.962 | 46.512 | nan | nan |
[32m[01/03 21:36:36 d2.evaluation.coco_evaluation]: [0mSome metrics cannot be computed and is shown as NaN.
OrderedDict([('bbox', {'AP': 51.21091623513199, 'AP50': 93.06066125172809, 'AP75': 50.60221543054752, 'APs': 51.21091623513199, 'APm': nan, 'APl': nan}), ('segm', {'AP': 46.51176413464396, 'AP50': 93.0399394730976, 'AP75': 39.961971182164504, 'APs': 46.51176413464396, 'APm': nan, 'APl': nan})])
====Start Evaluation on CWB L Test Set====
[32m[01/03 21:36:36 d2.data.datasets.coco]: [0mLoaded 73 images in COCO format from /home/eorslab/fatcat/SAR_DATASET/TSSWD/annotation/test/coco/annotation/L_test.json
[32m[01/03 21:36:36 d2.data.build]: [0mDistribution of instances among all 1 categories:
[36m| category | #instances |
|:----------:|:-------------|
| ship | 203 |
| | |[0m
[32m[01/03 21:36:36 d2.data.dataset_mapper]: [0m[DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(800, 800), max_size=1333, sample_style='choice')]
[32m[01/03 21:36:36 d2.data.common]: [0mSerializing 73 elements to byte tensors and concatenating them all ...
[32m[01/03 21:36:36 d2.data.common]: [0mSerialized dataset takes 0.05 MiB
[32m[01/03 21:36:36 d2.evaluation.evaluator]: [0mStart inference on 73 batches
[32m[01/03 21:36:38 d2.evaluation.evaluator]: [0mInference done 11/73. Dataloading: 0.0010 s/iter. Inference: 0.0820 s/iter. Eval: 0.0027 s/iter. Total: 0.0856 s/iter. ETA=0:00:05
[32m[01/03 21:36:43 d2.evaluation.evaluator]: [0mInference done 66/73. Dataloading: 0.0013 s/iter. Inference: 0.0819 s/iter. Eval: 0.0082 s/iter. Total: 0.0915 s/iter. ETA=0:00:00
[32m[01/03 21:36:43 d2.evaluation.evaluator]: [0mTotal inference time: 0:00:06.221527 (0.091493 s / iter per device, on 1 devices)
[32m[01/03 21:36:43 d2.evaluation.evaluator]: [0mTotal inference pure compute time: 0:00:05 (0.081613 s / iter per device, on 1 devices)
[32m[01/03 21:36:43 d2.evaluation.coco_evaluation]: [0mPreparing results for COCO format ...
[32m[01/03 21:36:43 d2.evaluation.coco_evaluation]: [0mSaving results to ./weather_test_final/CWB/L/coco_instances_results.json
[32m[01/03 21:36:44 d2.evaluation.coco_evaluation]: [0mEvaluating predictions with unofficial COCO API...
Loading and preparing results...
DONE (t=0.00s)
creating index...
index created!
[32m[01/03 21:36:44 d2.evaluation.fast_eval_api]: [0mEvaluate annotation type *bbox*
[32m[01/03 21:36:44 d2.evaluation.fast_eval_api]: [0mCOCOeval_opt.evaluate() finished in 0.01 seconds.
[32m[01/03 21:36:44 d2.evaluation.fast_eval_api]: [0mAccumulating evaluation results...
[32m[01/03 21:36:44 d2.evaluation.fast_eval_api]: [0mCOCOeval_opt.accumulate() finished in 0.00 seconds.
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.590
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.982
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.684
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.590
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.240
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.640
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.650
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.650
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000
[32m[01/03 21:36:44 d2.evaluation.coco_evaluation]: [0mEvaluation results for bbox:
| AP | AP50 | AP75 | APs | APm | APl |
|:------:|:------:|:------:|:------:|:-----:|:-----:|
| 58.962 | 98.241 | 68.428 | 58.962 | nan | nan |
[32m[01/03 21:36:44 d2.evaluation.coco_evaluation]: [0mSome metrics cannot be computed and is shown as NaN.
Loading and preparing results...
DONE (t=0.00s)
creating index...
index created!
[32m[01/03 21:36:44 d2.evaluation.fast_eval_api]: [0mEvaluate annotation type *segm*
[32m[01/03 21:36:44 d2.evaluation.fast_eval_api]: [0mCOCOeval_opt.evaluate() finished in 0.01 seconds.
[32m[01/03 21:36:44 d2.evaluation.fast_eval_api]: [0mAccumulating evaluation results...
[32m[01/03 21:36:44 d2.evaluation.fast_eval_api]: [0mCOCOeval_opt.accumulate() finished in 0.00 seconds.
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.531
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.983
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.511
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.531
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.216
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.583
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.593
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.593
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000
[32m[01/03 21:36:44 d2.evaluation.coco_evaluation]: [0mEvaluation results for segm:
| AP | AP50 | AP75 | APs | APm | APl |
|:------:|:------:|:------:|:------:|:-----:|:-----:|
| 53.145 | 98.297 | 51.097 | 53.145 | nan | nan |
[32m[01/03 21:36:44 d2.evaluation.coco_evaluation]: [0mSome metrics cannot be computed and is shown as NaN.
OrderedDict([('bbox', {'AP': 58.961966035698055, 'AP50': 98.24132498145644, 'AP75': 68.42774855576495, 'APs': 58.961966035698055, 'APm': nan, 'APl': nan}), ('segm', {'AP': 53.14496918901534, 'AP50': 98.2974653956032, 'AP75': 51.09655339743401, 'APs': 53.14496918901534, 'APm': nan, 'APl': nan})])
====Start Evaluation on CWB M Test Set====
[32m[01/03 21:36:44 d2.data.datasets.coco]: [0mLoaded 73 images in COCO format from /home/eorslab/fatcat/SAR_DATASET/TSSWD/annotation/test/coco/annotation/M_test.json
[32m[01/03 21:36:44 d2.data.build]: [0mDistribution of instances among all 1 categories:
[36m| category | #instances |
|:----------:|:-------------|
| ship | 176 |
| | |[0m
[32m[01/03 21:36:44 d2.data.dataset_mapper]: [0m[DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(800, 800), max_size=1333, sample_style='choice')]
[32m[01/03 21:36:44 d2.data.common]: [0mSerializing 73 elements to byte tensors and concatenating them all ...
[32m[01/03 21:36:44 d2.data.common]: [0mSerialized dataset takes 0.05 MiB
[32m[01/03 21:36:44 d2.evaluation.evaluator]: [0mStart inference on 73 batches
[32m[01/03 21:36:48 d2.evaluation.evaluator]: [0mInference done 48/73. Dataloading: 0.0010 s/iter. Inference: 0.0790 s/iter. Eval: 0.0042 s/iter. Total: 0.0842 s/iter. ETA=0:00:02
[32m[01/03 21:36:50 d2.evaluation.evaluator]: [0mTotal inference time: 0:00:05.875954 (0.086411 s / iter per device, on 1 devices)
[32m[01/03 21:36:50 d2.evaluation.evaluator]: [0mTotal inference pure compute time: 0:00:05 (0.079008 s / iter per device, on 1 devices)
[32m[01/03 21:36:50 d2.evaluation.coco_evaluation]: [0mPreparing results for COCO format ...
[32m[01/03 21:36:50 d2.evaluation.coco_evaluation]: [0mSaving results to ./weather_test_final/CWB/M/coco_instances_results.json
[32m[01/03 21:36:50 d2.evaluation.coco_evaluation]: [0mEvaluating predictions with unofficial COCO API...
Loading and preparing results...
DONE (t=0.00s)
creating index...
index created!
[32m[01/03 21:36:50 d2.evaluation.fast_eval_api]: [0mEvaluate annotation type *bbox*
[32m[01/03 21:36:50 d2.evaluation.fast_eval_api]: [0mCOCOeval_opt.evaluate() finished in 0.01 seconds.
[32m[01/03 21:36:50 d2.evaluation.fast_eval_api]: [0mAccumulating evaluation results...
[32m[01/03 21:36:50 d2.evaluation.fast_eval_api]: [0mCOCOeval_opt.accumulate() finished in 0.00 seconds.
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.535
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.941
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.529
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.535
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.272
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.589
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.593
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.593
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000
[32m[01/03 21:36:50 d2.evaluation.coco_evaluation]: [0mEvaluation results for bbox:
| AP | AP50 | AP75 | APs | APm | APl |
|:------:|:------:|:------:|:------:|:-----:|:-----:|
| 53.459 | 94.110 | 52.918 | 53.459 | nan | nan |
[32m[01/03 21:36:50 d2.evaluation.coco_evaluation]: [0mSome metrics cannot be computed and is shown as NaN.
Loading and preparing results...
DONE (t=0.00s)
creating index...
index created!
[32m[01/03 21:36:50 d2.evaluation.fast_eval_api]: [0mEvaluate annotation type *segm*
[32m[01/03 21:36:50 d2.evaluation.fast_eval_api]: [0mCOCOeval_opt.evaluate() finished in 0.01 seconds.
[32m[01/03 21:36:50 d2.evaluation.fast_eval_api]: [0mAccumulating evaluation results...
[32m[01/03 21:36:50 d2.evaluation.fast_eval_api]: [0mCOCOeval_opt.accumulate() finished in 0.00 seconds.
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.488
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.941
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.406
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.488
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.241
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.541
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.546
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.546
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000
[32m[01/03 21:36:50 d2.evaluation.coco_evaluation]: [0mEvaluation results for segm:
| AP | AP50 | AP75 | APs | APm | APl |
|:------:|:------:|:------:|:------:|:-----:|:-----:|
| 48.820 | 94.110 | 40.568 | 48.820 | nan | nan |
[32m[01/03 21:36:50 d2.evaluation.coco_evaluation]: [0mSome metrics cannot be computed and is shown as NaN.
OrderedDict([('bbox', {'AP': 53.45861327516944, 'AP50': 94.10980928029886, 'AP75': 52.917802304450255, 'APs': 53.45861327516944, 'APm': nan, 'APl': nan}), ('segm', {'AP': 48.8198464018423, 'AP50': 94.10980928029886, 'AP75': 40.568480845822776, 'APs': 48.8198464018423, 'APm': nan, 'APl': nan})])
====Start Evaluation on CWB S Test Set====
[32m[01/03 21:36:50 d2.data.datasets.coco]: [0mLoaded 72 images in COCO format from /home/eorslab/fatcat/SAR_DATASET/TSSWD/annotation/test/coco/annotation/S_test.json
[32m[01/03 21:36:50 d2.data.build]: [0mDistribution of instances among all 1 categories:
[36m| category | #instances |
|:----------:|:-------------|
| ship | 183 |
| | |[0m
[32m[01/03 21:36:50 d2.data.dataset_mapper]: [0m[DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(800, 800), max_size=1333, sample_style='choice')]
[32m[01/03 21:36:50 d2.data.common]: [0mSerializing 72 elements to byte tensors and concatenating them all ...
[32m[01/03 21:36:50 d2.data.common]: [0mSerialized dataset takes 0.05 MiB
[32m[01/03 21:36:50 d2.evaluation.evaluator]: [0mStart inference on 72 batches
[32m[01/03 21:36:53 d2.evaluation.evaluator]: [0mInference done 29/72. Dataloading: 0.0010 s/iter. Inference: 0.0790 s/iter. Eval: 0.0042 s/iter. Total: 0.0842 s/iter. ETA=0:00:03
[32m[01/03 21:36:57 d2.evaluation.evaluator]: [0mTotal inference time: 0:00:05.800362 (0.086573 s / iter per device, on 1 devices)
[32m[01/03 21:36:57 d2.evaluation.evaluator]: [0mTotal inference pure compute time: 0:00:05 (0.078927 s / iter per device, on 1 devices)
[32m[01/03 21:36:57 d2.evaluation.coco_evaluation]: [0mPreparing results for COCO format ...
[32m[01/03 21:36:57 d2.evaluation.coco_evaluation]: [0mSaving results to ./weather_test_final/CWB/S/coco_instances_results.json
[32m[01/03 21:36:57 d2.evaluation.coco_evaluation]: [0mEvaluating predictions with unofficial COCO API...
Loading and preparing results...
DONE (t=0.00s)
creating index...
index created!
[32m[01/03 21:36:57 d2.evaluation.fast_eval_api]: [0mEvaluate annotation type *bbox*
[32m[01/03 21:36:57 d2.evaluation.fast_eval_api]: [0mCOCOeval_opt.evaluate() finished in 0.01 seconds.
[32m[01/03 21:36:57 d2.evaluation.fast_eval_api]: [0mAccumulating evaluation results...
[32m[01/03 21:36:57 d2.evaluation.fast_eval_api]: [0mCOCOeval_opt.accumulate() finished in 0.00 seconds.
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.465
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.916
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.395
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.465
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.237
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.484
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.522
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.522
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000
[32m[01/03 21:36:57 d2.evaluation.coco_evaluation]: [0mEvaluation results for bbox:
| AP | AP50 | AP75 | APs | APm | APl |
|:------:|:------:|:------:|:------:|:-----:|:-----:|
| 46.519 | 91.599 | 39.521 | 46.519 | nan | nan |
[32m[01/03 21:36:57 d2.evaluation.coco_evaluation]: [0mSome metrics cannot be computed and is shown as NaN.
Loading and preparing results...
DONE (t=0.00s)
creating index...
index created!
[32m[01/03 21:36:57 d2.evaluation.fast_eval_api]: [0mEvaluate annotation type *segm*
[32m[01/03 21:36:57 d2.evaluation.fast_eval_api]: [0mCOCOeval_opt.evaluate() finished in 0.01 seconds.
[32m[01/03 21:36:57 d2.evaluation.fast_eval_api]: [0mAccumulating evaluation results...
[32m[01/03 21:36:57 d2.evaluation.fast_eval_api]: [0mCOCOeval_opt.accumulate() finished in 0.00 seconds.
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.425
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.904
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.367
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.425
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.224
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.453
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.489
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.489
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000
[32m[01/03 21:36:57 d2.evaluation.coco_evaluation]: [0mEvaluation results for segm:
| AP | AP50 | AP75 | APs | APm | APl |
|:------:|:------:|:------:|:------:|:-----:|:-----:|
| 42.490 | 90.385 | 36.719 | 42.490 | nan | nan |
[32m[01/03 21:36:57 d2.evaluation.coco_evaluation]: [0mSome metrics cannot be computed and is shown as NaN.
OrderedDict([('bbox', {'AP': 46.51940321628753, 'AP50': 91.59893424430791, 'AP75': 39.52124061273067, 'APs': 46.51940321628753, 'APm': nan, 'APl': nan}), ('segm', {'AP': 42.490477077732294, 'AP50': 90.38450409490675, 'AP75': 36.71927951355919, 'APs': 42.490477077732294, 'APm': nan, 'APl': nan})])
====Start Evaluation on CWB sw Test Set====
[32m[01/03 21:36:57 d2.data.datasets.coco]: [0mLoaded 72 images in COCO format from /home/eorslab/fatcat/SAR_DATASET/TSSWD/annotation/test/coco/annotation/sw_test.json
[32m[01/03 21:36:57 d2.data.build]: [0mDistribution of instances among all 1 categories:
[36m| category | #instances |
|:----------:|:-------------|
| ship | 190 |
| | |[0m
[32m[01/03 21:36:57 d2.data.dataset_mapper]: [0m[DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(800, 800), max_size=1333, sample_style='choice')]
[32m[01/03 21:36:57 d2.data.common]: [0mSerializing 72 elements to byte tensors and concatenating them all ...
[32m[01/03 21:36:57 d2.data.common]: [0mSerialized dataset takes 0.05 MiB
[32m[01/03 21:36:57 d2.evaluation.evaluator]: [0mStart inference on 72 batches
[32m[01/03 21:36:58 d2.evaluation.evaluator]: [0mInference done 11/72. Dataloading: 0.0008 s/iter. Inference: 0.0788 s/iter. Eval: 0.0026 s/iter. Total: 0.0822 s/iter. ETA=0:00:05
[32m[01/03 21:37:03 d2.evaluation.evaluator]: [0mInference done 69/72. Dataloading: 0.0011 s/iter. Inference: 0.0790 s/iter. Eval: 0.0058 s/iter. Total: 0.0858 s/iter. ETA=0:00:00
[32m[01/03 21:37:03 d2.evaluation.evaluator]: [0mTotal inference time: 0:00:05.829260 (0.087004 s / iter per device, on 1 devices)
[32m[01/03 21:37:03 d2.evaluation.evaluator]: [0mTotal inference pure compute time: 0:00:05 (0.078946 s / iter per device, on 1 devices)
[32m[01/03 21:37:03 d2.evaluation.coco_evaluation]: [0mPreparing results for COCO format ...
[32m[01/03 21:37:03 d2.evaluation.coco_evaluation]: [0mSaving results to ./weather_test_final/CWB/sw/coco_instances_results.json
[32m[01/03 21:37:03 d2.evaluation.coco_evaluation]: [0mEvaluating predictions with unofficial COCO API...
Loading and preparing results...
DONE (t=0.00s)
creating index...
index created!
[32m[01/03 21:37:03 d2.evaluation.fast_eval_api]: [0mEvaluate annotation type *bbox*
[32m[01/03 21:37:03 d2.evaluation.fast_eval_api]: [0mCOCOeval_opt.evaluate() finished in 0.01 seconds.
[32m[01/03 21:37:03 d2.evaluation.fast_eval_api]: [0mAccumulating evaluation results...
[32m[01/03 21:37:03 d2.evaluation.fast_eval_api]: [0mCOCOeval_opt.accumulate() finished in 0.00 seconds.
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.463
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.896
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.425
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.463
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.216
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.468
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.526
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.526
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000
[32m[01/03 21:37:03 d2.evaluation.coco_evaluation]: [0mEvaluation results for bbox:
| AP | AP50 | AP75 | APs | APm | APl |
|:------:|:------:|:------:|:------:|:-----:|:-----:|
| 46.310 | 89.579 | 42.502 | 46.310 | nan | nan |
[32m[01/03 21:37:03 d2.evaluation.coco_evaluation]: [0mSome metrics cannot be computed and is shown as NaN.
Loading and preparing results...
DONE (t=0.00s)
creating index...
index created!
[32m[01/03 21:37:03 d2.evaluation.fast_eval_api]: [0mEvaluate annotation type *segm*
[32m[01/03 21:37:03 d2.evaluation.fast_eval_api]: [0mCOCOeval_opt.evaluate() finished in 0.01 seconds.
[32m[01/03 21:37:03 d2.evaluation.fast_eval_api]: [0mAccumulating evaluation results...
[32m[01/03 21:37:03 d2.evaluation.fast_eval_api]: [0mCOCOeval_opt.accumulate() finished in 0.00 seconds.
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.419
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.897
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.329
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.419
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.201
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.426
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.483
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.483
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000
[32m[01/03 21:37:03 d2.evaluation.coco_evaluation]: [0mEvaluation results for segm:
| AP | AP50 | AP75 | APs | APm | APl |
|:------:|:------:|:------:|:------:|:-----:|:-----:|
| 41.921 | 89.710 | 32.916 | 41.921 | nan | nan |
[32m[01/03 21:37:03 d2.evaluation.coco_evaluation]: [0mSome metrics cannot be computed and is shown as NaN.
OrderedDict([('bbox', {'AP': 46.3100665544986, 'AP50': 89.57897811578212, 'AP75': 42.501915529165416, 'APs': 46.3100665544986, 'APm': nan, 'APl': nan}), ('segm', {'AP': 41.920961171934465, 'AP50': 89.71002113641686, 'AP75': 32.916497582262814, 'APs': 41.920961171934465, 'APm': nan, 'APl': nan})])
====Start Evaluation on WMO 0 Test Set====
[32m[01/03 21:37:03 d2.data.datasets.coco]: [0mLoaded 1 images in COCO format from /home/eorslab/fatcat/SAR_DATASET/TSSWD/annotation/test/coco/annotation/WMO_0_test.json
[32m[01/03 21:37:03 d2.data.build]: [0mDistribution of instances among all 1 categories:
[36m| category | #instances |
|:----------:|:-------------|
| ship | 13 |
| | |[0m
[32m[01/03 21:37:03 d2.data.dataset_mapper]: [0m[DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(800, 800), max_size=1333, sample_style='choice')]
[32m[01/03 21:37:03 d2.data.common]: [0mSerializing 1 elements to byte tensors and concatenating them all ...
[32m[01/03 21:37:03 d2.data.common]: [0mSerialized dataset takes 0.00 MiB
[32m[01/03 21:37:03 d2.evaluation.evaluator]: [0mStart inference on 1 batches
[32m[01/03 21:37:04 d2.evaluation.evaluator]: [0mTotal inference time: 0:00:00.187386 (0.187386 s / iter per device, on 1 devices)
[32m[01/03 21:37:04 d2.evaluation.evaluator]: [0mTotal inference pure compute time: 0:00:00 (0.087798 s / iter per device, on 1 devices)
[32m[01/03 21:37:04 d2.evaluation.coco_evaluation]: [0mPreparing results for COCO format ...
[32m[01/03 21:37:04 d2.evaluation.coco_evaluation]: [0mSaving results to ./weather_test_final/WMO/WMO_0/coco_instances_results.json
[32m[01/03 21:37:04 d2.evaluation.coco_evaluation]: [0mEvaluating predictions with unofficial COCO API...
Loading and preparing results...
DONE (t=0.00s)
creating index...
index created!
[32m[01/03 21:37:04 d2.evaluation.fast_eval_api]: [0mEvaluate annotation type *bbox*
[32m[01/03 21:37:04 d2.evaluation.fast_eval_api]: [0mCOCOeval_opt.evaluate() finished in 0.00 seconds.
[32m[01/03 21:37:04 d2.evaluation.fast_eval_api]: [0mAccumulating evaluation results...
[32m[01/03 21:37:04 d2.evaluation.fast_eval_api]: [0mCOCOeval_opt.accumulate() finished in 0.00 seconds.
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.554
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 1.000
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.624
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.554
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.038
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.546
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.608
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.608
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000
[32m[01/03 21:37:04 d2.evaluation.coco_evaluation]: [0mEvaluation results for bbox:
| AP | AP50 | AP75 | APs | APm | APl |
|:------:|:-------:|:------:|:------:|:-----:|:-----:|
| 55.404 | 100.000 | 62.376 | 55.404 | nan | nan |
[32m[01/03 21:37:04 d2.evaluation.coco_evaluation]: [0mSome metrics cannot be computed and is shown as NaN.
Loading and preparing results...
DONE (t=0.00s)
creating index...
index created!
[32m[01/03 21:37:04 d2.evaluation.fast_eval_api]: [0mEvaluate annotation type *segm*
[32m[01/03 21:37:04 d2.evaluation.fast_eval_api]: [0mCOCOeval_opt.evaluate() finished in 0.00 seconds.
[32m[01/03 21:37:04 d2.evaluation.fast_eval_api]: [0mAccumulating evaluation results...
[32m[01/03 21:37:04 d2.evaluation.fast_eval_api]: [0mCOCOeval_opt.accumulate() finished in 0.00 seconds.
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.557
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 1.000
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.573
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.557
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.046
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.492
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.592
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.592
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000
[32m[01/03 21:37:04 d2.evaluation.coco_evaluation]: [0mEvaluation results for segm:
| AP | AP50 | AP75 | APs | APm | APl |
|:------:|:-------:|:------:|:------:|:-----:|:-----:|
| 55.699 | 100.000 | 57.284 | 55.699 | nan | nan |
[32m[01/03 21:37:04 d2.evaluation.coco_evaluation]: [0mSome metrics cannot be computed and is shown as NaN.
OrderedDict([('bbox', {'AP': 55.4044173648134, 'AP50': 100.0, 'AP75': 62.376237623762385, 'APs': 55.4044173648134, 'APm': nan, 'APl': nan}), ('segm', {'AP': 55.6986577778657, 'AP50': 100.0, 'AP75': 57.284299858557276, 'APs': 55.6986577778657, 'APm': nan, 'APl': nan})])
====Start Evaluation on WMO 2 Test Set====
[32m[01/03 21:37:04 d2.data.datasets.coco]: [0mLoaded 62 images in COCO format from /home/eorslab/fatcat/SAR_DATASET/TSSWD/annotation/test/coco/annotation/WMO_2_test.json
[32m[01/03 21:37:04 d2.data.build]: [0mDistribution of instances among all 1 categories:
[36m| category | #instances |
|:----------:|:-------------|
| ship | 160 |
| | |[0m
[32m[01/03 21:37:04 d2.data.dataset_mapper]: [0m[DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(800, 800), max_size=1333, sample_style='choice')]
[32m[01/03 21:37:04 d2.data.common]: [0mSerializing 62 elements to byte tensors and concatenating them all ...
[32m[01/03 21:37:04 d2.data.common]: [0mSerialized dataset takes 0.04 MiB
[32m[01/03 21:37:04 d2.evaluation.evaluator]: [0mStart inference on 62 batches
[32m[01/03 21:37:08 d2.evaluation.evaluator]: [0mInference done 44/62. Dataloading: 0.0012 s/iter. Inference: 0.0814 s/iter. Eval: 0.0073 s/iter. Total: 0.0901 s/iter. ETA=0:00:01
[32m[01/03 21:37:10 d2.evaluation.evaluator]: [0mTotal inference time: 0:00:05.094381 (0.089375 s / iter per device, on 1 devices)
[32m[01/03 21:37:10 d2.evaluation.evaluator]: [0mTotal inference pure compute time: 0:00:04 (0.080629 s / iter per device, on 1 devices)
[32m[01/03 21:37:10 d2.evaluation.coco_evaluation]: [0mPreparing results for COCO format ...
[32m[01/03 21:37:10 d2.evaluation.coco_evaluation]: [0mSaving results to ./weather_test_final/WMO/WMO_2/coco_instances_results.json
[32m[01/03 21:37:10 d2.evaluation.coco_evaluation]: [0mEvaluating predictions with unofficial COCO API...
Loading and preparing results...
DONE (t=0.00s)
creating index...
index created!
[32m[01/03 21:37:10 d2.evaluation.fast_eval_api]: [0mEvaluate annotation type *bbox*
[32m[01/03 21:37:10 d2.evaluation.fast_eval_api]: [0mCOCOeval_opt.evaluate() finished in 0.01 seconds.
[32m[01/03 21:37:10 d2.evaluation.fast_eval_api]: [0mAccumulating evaluation results...
[32m[01/03 21:37:10 d2.evaluation.fast_eval_api]: [0mCOCOeval_opt.accumulate() finished in 0.00 seconds.
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.457
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.875
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.419
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.457
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.224
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.455
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.519
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.519
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000
[32m[01/03 21:37:10 d2.evaluation.coco_evaluation]: [0mEvaluation results for bbox:
| AP | AP50 | AP75 | APs | APm | APl |
|:------:|:------:|:------:|:------:|:-----:|:-----:|
| 45.715 | 87.510 | 41.867 | 45.715 | nan | nan |
[32m[01/03 21:37:10 d2.evaluation.coco_evaluation]: [0mSome metrics cannot be computed and is shown as NaN.
Loading and preparing results...
DONE (t=0.00s)
creating index...
index created!
[32m[01/03 21:37:10 d2.evaluation.fast_eval_api]: [0mEvaluate annotation type *segm*
[32m[01/03 21:37:10 d2.evaluation.fast_eval_api]: [0mCOCOeval_opt.evaluate() finished in 0.01 seconds.
[32m[01/03 21:37:10 d2.evaluation.fast_eval_api]: [0mAccumulating evaluation results...
[32m[01/03 21:37:10 d2.evaluation.fast_eval_api]: [0mCOCOeval_opt.accumulate() finished in 0.00 seconds.
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.410
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.886
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.324
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.410
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.208
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.416
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.476
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.476
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000
[32m[01/03 21:37:10 d2.evaluation.coco_evaluation]: [0mEvaluation results for segm:
| AP | AP50 | AP75 | APs | APm | APl |
|:------:|:------:|:------:|:------:|:-----:|:-----:|
| 41.018 | 88.583 | 32.370 | 41.018 | nan | nan |
[32m[01/03 21:37:10 d2.evaluation.coco_evaluation]: [0mSome metrics cannot be computed and is shown as NaN.
OrderedDict([('bbox', {'AP': 45.7151248718828, 'AP50': 87.50995535305601, 'AP75': 41.8665359976179, 'APs': 45.7151248718828, 'APm': nan, 'APl': nan}), ('segm', {'AP': 41.01813150828569, 'AP50': 88.58324071278018, 'AP75': 32.3702752920994, 'APs': 41.01813150828569, 'APm': nan, 'APl': nan})])
====Start Evaluation on WMO 3 Test Set====
[32m[01/03 21:37:10 d2.data.datasets.coco]: [0mLoaded 63 images in COCO format from /home/eorslab/fatcat/SAR_DATASET/TSSWD/annotation/test/coco/annotation/WMO_3_test.json
[32m[01/03 21:37:10 d2.data.build]: [0mDistribution of instances among all 1 categories:
[36m| category | #instances |
|:----------:|:-------------|
| ship | 158 |
| | |[0m
[32m[01/03 21:37:10 d2.data.dataset_mapper]: [0m[DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(800, 800), max_size=1333, sample_style='choice')]
[32m[01/03 21:37:10 d2.data.common]: [0mSerializing 63 elements to byte tensors and concatenating them all ...
[32m[01/03 21:37:10 d2.data.common]: [0mSerialized dataset takes 0.04 MiB
[32m[01/03 21:37:10 d2.evaluation.evaluator]: [0mStart inference on 63 batches
[32m[01/03 21:37:13 d2.evaluation.evaluator]: [0mInference done 36/63. Dataloading: 0.0010 s/iter. Inference: 0.0798 s/iter. Eval: 0.0066 s/iter. Total: 0.0875 s/iter. ETA=0:00:02
[32m[01/03 21:37:15 d2.evaluation.evaluator]: [0mTotal inference time: 0:00:05.100236 (0.087935 s / iter per device, on 1 devices)
[32m[01/03 21:37:15 d2.evaluation.evaluator]: [0mTotal inference pure compute time: 0:00:04 (0.079800 s / iter per device, on 1 devices)
[32m[01/03 21:37:15 d2.evaluation.coco_evaluation]: [0mPreparing results for COCO format ...
[32m[01/03 21:37:15 d2.evaluation.coco_evaluation]: [0mSaving results to ./weather_test_final/WMO/WMO_3/coco_instances_results.json
[32m[01/03 21:37:15 d2.evaluation.coco_evaluation]: [0mEvaluating predictions with unofficial COCO API...
Loading and preparing results...
DONE (t=0.00s)
creating index...
index created!
[32m[01/03 21:37:15 d2.evaluation.fast_eval_api]: [0mEvaluate annotation type *bbox*
[32m[01/03 21:37:15 d2.evaluation.fast_eval_api]: [0mCOCOeval_opt.evaluate() finished in 0.01 seconds.
[32m[01/03 21:37:15 d2.evaluation.fast_eval_api]: [0mAccumulating evaluation results...
[32m[01/03 21:37:15 d2.evaluation.fast_eval_api]: [0mCOCOeval_opt.accumulate() finished in 0.00 seconds.
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.442
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.909
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.331
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.442
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.227
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.461
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.504
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.504
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000
[32m[01/03 21:37:15 d2.evaluation.coco_evaluation]: [0mEvaluation results for bbox:
| AP | AP50 | AP75 | APs | APm | APl |
|:------:|:------:|:------:|:------:|:-----:|:-----:|
| 44.188 | 90.880 | 33.122 | 44.188 | nan | nan |
[32m[01/03 21:37:15 d2.evaluation.coco_evaluation]: [0mSome metrics cannot be computed and is shown as NaN.
Loading and preparing results...
DONE (t=0.00s)
creating index...
index created!
[32m[01/03 21:37:15 d2.evaluation.fast_eval_api]: [0mEvaluate annotation type *segm*
[32m[01/03 21:37:15 d2.evaluation.fast_eval_api]: [0mCOCOeval_opt.evaluate() finished in 0.01 seconds.
[32m[01/03 21:37:15 d2.evaluation.fast_eval_api]: [0mAccumulating evaluation results...
[32m[01/03 21:37:15 d2.evaluation.fast_eval_api]: [0mCOCOeval_opt.accumulate() finished in 0.00 seconds.
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.407
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.894
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.318
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.407
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.218
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.428
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.470
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.470
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000
[32m[01/03 21:37:15 d2.evaluation.coco_evaluation]: [0mEvaluation results for segm:
| AP | AP50 | AP75 | APs | APm | APl |
|:------:|:------:|:------:|:------:|:-----:|:-----:|
| 40.661 | 89.391 | 31.849 | 40.661 | nan | nan |
[32m[01/03 21:37:15 d2.evaluation.coco_evaluation]: [0mSome metrics cannot be computed and is shown as NaN.
OrderedDict([('bbox', {'AP': 44.18778380154016, 'AP50': 90.87992764780107, 'AP75': 33.12188501179387, 'APs': 44.18778380154016, 'APm': nan, 'APl': nan}), ('segm', {'AP': 40.66055020416186, 'AP50': 89.39131602443476, 'AP75': 31.84851260836029, 'APs': 40.66055020416186, 'APm': nan, 'APl': nan})])
====Start Evaluation on WMO 4 Test Set====
[32m[01/03 21:37:15 d2.data.datasets.coco]: [0mLoaded 23 images in COCO format from /home/eorslab/fatcat/SAR_DATASET/TSSWD/annotation/test/coco/annotation/WMO_4_test.json
[32m[01/03 21:37:15 d2.data.build]: [0mDistribution of instances among all 1 categories:
[36m| category | #instances |
|:----------:|:-------------|
| ship | 61 |
| | |[0m
[32m[01/03 21:37:15 d2.data.dataset_mapper]: [0m[DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(800, 800), max_size=1333, sample_style='choice')]
[32m[01/03 21:37:15 d2.data.common]: [0mSerializing 23 elements to byte tensors and concatenating them all ...
[32m[01/03 21:37:15 d2.data.common]: [0mSerialized dataset takes 0.02 MiB
[32m[01/03 21:37:15 d2.evaluation.evaluator]: [0mStart inference on 23 batches
[32m[01/03 21:37:18 d2.evaluation.evaluator]: [0mTotal inference time: 0:00:01.623791 (0.090211 s / iter per device, on 1 devices)
[32m[01/03 21:37:18 d2.evaluation.evaluator]: [0mTotal inference pure compute time: 0:00:01 (0.079567 s / iter per device, on 1 devices)
[32m[01/03 21:37:18 d2.evaluation.coco_evaluation]: [0mPreparing results for COCO format ...
[32m[01/03 21:37:18 d2.evaluation.coco_evaluation]: [0mSaving results to ./weather_test_final/WMO/WMO_4/coco_instances_results.json
[32m[01/03 21:37:18 d2.evaluation.coco_evaluation]: [0mEvaluating predictions with unofficial COCO API...
Loading and preparing results...
DONE (t=0.00s)
creating index...
index created!
[32m[01/03 21:37:18 d2.evaluation.fast_eval_api]: [0mEvaluate annotation type *bbox*
[32m[01/03 21:37:18 d2.evaluation.fast_eval_api]: [0mCOCOeval_opt.evaluate() finished in 0.00 seconds.
[32m[01/03 21:37:18 d2.evaluation.fast_eval_api]: [0mAccumulating evaluation results...
[32m[01/03 21:37:18 d2.evaluation.fast_eval_api]: [0mCOCOeval_opt.accumulate() finished in 0.00 seconds.
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.545
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.964
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.591
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.545
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.254
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.592
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.592
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.592
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000
[32m[01/03 21:37:18 d2.evaluation.coco_evaluation]: [0mEvaluation results for bbox:
| AP | AP50 | AP75 | APs | APm | APl |
|:------:|:------:|:------:|:------:|:-----:|:-----:|
| 54.540 | 96.442 | 59.062 | 54.540 | nan | nan |
[32m[01/03 21:37:18 d2.evaluation.coco_evaluation]: [0mSome metrics cannot be computed and is shown as NaN.
Loading and preparing results...
DONE (t=0.00s)
creating index...
index created!
[32m[01/03 21:37:18 d2.evaluation.fast_eval_api]: [0mEvaluate annotation type *segm*
[32m[01/03 21:37:18 d2.evaluation.fast_eval_api]: [0mCOCOeval_opt.evaluate() finished in 0.00 seconds.
[32m[01/03 21:37:18 d2.evaluation.fast_eval_api]: [0mAccumulating evaluation results...
[32m[01/03 21:37:18 d2.evaluation.fast_eval_api]: [0mCOCOeval_opt.accumulate() finished in 0.00 seconds.
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.494
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.964
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.428
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.494
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.221
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.549
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.549
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.549
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000
[32m[01/03 21:37:18 d2.evaluation.coco_evaluation]: [0mEvaluation results for segm:
| AP | AP50 | AP75 | APs | APm | APl |
|:------:|:------:|:------:|:------:|:-----:|:-----:|
| 49.401 | 96.442 | 42.829 | 49.401 | nan | nan |
[32m[01/03 21:37:18 d2.evaluation.coco_evaluation]: [0mSome metrics cannot be computed and is shown as NaN.
OrderedDict([('bbox', {'AP': 54.53967984876118, 'AP50': 96.44230192861014, 'AP75': 59.061675207114774, 'APs': 54.53967984876118, 'APm': nan, 'APl': nan}), ('segm', {'AP': 49.40062052586553, 'AP50': 96.44230192861014, 'AP75': 42.82921038661844, 'APs': 49.40062052586553, 'APm': nan, 'APl': nan})])
====Start Evaluation on WMO 5 Test Set====
[32m[01/03 21:37:18 d2.data.datasets.coco]: [0mLoaded 66 images in COCO format from /home/eorslab/fatcat/SAR_DATASET/TSSWD/annotation/test/coco/annotation/WMO_5_test.json
[32m[01/03 21:37:18 d2.data.dataset_mapper]: [0m[DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(800, 800), max_size=1333, sample_style='choice')]
[32m[01/03 21:37:18 d2.data.common]: [0mSerializing 66 elements to byte tensors and concatenating them all ...
[32m[01/03 21:37:18 d2.data.common]: [0mSerialized dataset takes 0.05 MiB
[32m[01/03 21:37:18 d2.evaluation.evaluator]: [0mStart inference on 66 batches
[32m[01/03 21:37:19 d2.evaluation.evaluator]: [0mInference done 11/66. Dataloading: 0.0008 s/iter. Inference: 0.0786 s/iter. Eval: 0.0020 s/iter. Total: 0.0815 s/iter. ETA=0:00:04
[32m[01/03 21:37:24 d2.evaluation.evaluator]: [0mTotal inference time: 0:00:05.331509 (0.087402 s / iter per device, on 1 devices)
[32m[01/03 21:37:24 d2.evaluation.evaluator]: [0mTotal inference pure compute time: 0:00:04 (0.078934 s / iter per device, on 1 devices)
[32m[01/03 21:37:24 d2.evaluation.coco_evaluation]: [0mPreparing results for COCO format ...
[32m[01/03 21:37:24 d2.evaluation.coco_evaluation]: [0mSaving results to ./weather_test_final/WMO/WMO_5/coco_instances_results.json
[32m[01/03 21:37:24 d2.evaluation.coco_evaluation]: [0mEvaluating predictions with unofficial COCO API...
Loading and preparing results...
DONE (t=0.00s)
creating index...
index created!
[32m[01/03 21:37:24 d2.evaluation.fast_eval_api]: [0mEvaluate annotation type *bbox*
[32m[01/03 21:37:24 d2.evaluation.fast_eval_api]: [0mCOCOeval_opt.evaluate() finished in 0.01 seconds.
[32m[01/03 21:37:24 d2.evaluation.fast_eval_api]: [0mAccumulating evaluation results...
[32m[01/03 21:37:24 d2.evaluation.fast_eval_api]: [0mCOCOeval_opt.accumulate() finished in 0.00 seconds.
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.588
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.974
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.681
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.588
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.248
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.639
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.651
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.651
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000
[32m[01/03 21:37:24 d2.evaluation.coco_evaluation]: [0mEvaluation results for bbox:
| AP | AP50 | AP75 | APs | APm | APl |
|:------:|:------:|:------:|:------:|:-----:|:-----:|
| 58.821 | 97.356 | 68.111 | 58.821 | nan | nan |
[32m[01/03 21:37:24 d2.evaluation.coco_evaluation]: [0mSome metrics cannot be computed and is shown as NaN.
Loading and preparing results...
DONE (t=0.00s)
creating index...
index created!
[32m[01/03 21:37:24 d2.evaluation.fast_eval_api]: [0mEvaluate annotation type *segm*
[32m[01/03 21:37:24 d2.evaluation.fast_eval_api]: [0mCOCOeval_opt.evaluate() finished in 0.01 seconds.
[32m[01/03 21:37:24 d2.evaluation.fast_eval_api]: [0mAccumulating evaluation results...
[32m[01/03 21:37:24 d2.evaluation.fast_eval_api]: [0mCOCOeval_opt.accumulate() finished in 0.00 seconds.
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.538
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.974
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.546
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.538
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.226
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.585
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.597
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.597
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000
[32m[01/03 21:37:24 d2.evaluation.coco_evaluation]: [0mEvaluation results for segm:
| AP | AP50 | AP75 | APs | APm | APl |
|:------:|:------:|:------:|:------:|:-----:|:-----:|
| 53.785 | 97.421 | 54.620 | 53.785 | nan | nan |
[32m[01/03 21:37:24 d2.evaluation.coco_evaluation]: [0mSome metrics cannot be computed and is shown as NaN.
OrderedDict([('bbox', {'AP': 58.82099524388645, 'AP50': 97.35596620862778, 'AP75': 68.11141929502452, 'APs': 58.82099524388645, 'APm': nan, 'APl': nan}), ('segm', {'AP': 53.785344086330674, 'AP50': 97.42111501077935, 'AP75': 54.61993087764245, 'APs': 53.785344086330674, 'APm': nan, 'APl': nan})])
====Start Evaluation on WMO 6 Test Set====
[32m[01/03 21:37:24 d2.data.datasets.coco]: [0mLoaded 2 images in COCO format from /home/eorslab/fatcat/SAR_DATASET/TSSWD/annotation/test/coco/annotation/WMO_6_test.json
[32m[01/03 21:37:24 d2.data.build]: [0mDistribution of instances among all 1 categories:
[36m| category | #instances |
|:----------:|:-------------|
| ship | 8 |
| | |[0m
[32m[01/03 21:37:24 d2.data.dataset_mapper]: [0m[DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(800, 800), max_size=1333, sample_style='choice')]
[32m[01/03 21:37:24 d2.data.common]: [0mSerializing 2 elements to byte tensors and concatenating them all ...
[32m[01/03 21:37:24 d2.data.common]: [0mSerialized dataset takes 0.00 MiB
[32m[01/03 21:37:24 d2.evaluation.evaluator]: [0mStart inference on 2 batches
[32m[01/03 21:37:24 d2.evaluation.evaluator]: [0mTotal inference time: 0:00:00.149109 (0.149109 s / iter per device, on 1 devices)
[32m[01/03 21:37:24 d2.evaluation.evaluator]: [0mTotal inference pure compute time: 0:00:00 (0.079053 s / iter per device, on 1 devices)
[32m[01/03 21:37:24 d2.evaluation.coco_evaluation]: [0mPreparing results for COCO format ...
[32m[01/03 21:37:24 d2.evaluation.coco_evaluation]: [0mSaving results to ./weather_test_final/WMO/WMO_6/coco_instances_results.json
[32m[01/03 21:37:24 d2.evaluation.coco_evaluation]: [0mEvaluating predictions with unofficial COCO API...
Loading and preparing results...
DONE (t=0.00s)
creating index...
index created!
[32m[01/03 21:37:24 d2.evaluation.fast_eval_api]: [0mEvaluate annotation type *bbox*
[32m[01/03 21:37:24 d2.evaluation.fast_eval_api]: [0mCOCOeval_opt.evaluate() finished in 0.00 seconds.
[32m[01/03 21:37:24 d2.evaluation.fast_eval_api]: [0mAccumulating evaluation results...
[32m[01/03 21:37:24 d2.evaluation.fast_eval_api]: [0mCOCOeval_opt.accumulate() finished in 0.00 seconds.
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.762
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 1.000
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.762
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.225
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.775
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.775
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.775
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000
[32m[01/03 21:37:24 d2.evaluation.coco_evaluation]: [0mEvaluation results for bbox:
| AP | AP50 | AP75 | APs | APm | APl |
|:------:|:-------:|:-------:|:------:|:-----:|:-----:|
| 76.163 | 100.000 | 100.000 | 76.163 | nan | nan |
[32m[01/03 21:37:24 d2.evaluation.coco_evaluation]: [0mSome metrics cannot be computed and is shown as NaN.
Loading and preparing results...
DONE (t=0.00s)
creating index...
index created!
[32m[01/03 21:37:24 d2.evaluation.fast_eval_api]: [0mEvaluate annotation type *segm*
[32m[01/03 21:37:24 d2.evaluation.fast_eval_api]: [0mCOCOeval_opt.evaluate() finished in 0.00 seconds.
[32m[01/03 21:37:24 d2.evaluation.fast_eval_api]: [0mAccumulating evaluation results...
[32m[01/03 21:37:24 d2.evaluation.fast_eval_api]: [0mCOCOeval_opt.accumulate() finished in 0.00 seconds.
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.574
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 1.000
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.691
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.574
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.175
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.625
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.625
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.625
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000
[32m[01/03 21:37:24 d2.evaluation.coco_evaluation]: [0mEvaluation results for segm:
| AP | AP50 | AP75 | APs | APm | APl |
|:------:|:-------:|:------:|:------:|:-----:|:-----:|
| 57.401 | 100.000 | 69.059 | 57.401 | nan | nan |
[32m[01/03 21:37:24 d2.evaluation.coco_evaluation]: [0mSome metrics cannot be computed and is shown as NaN.
OrderedDict([('bbox', {'AP': 76.16336633663366, 'AP50': 100.0, 'AP75': 100.0, 'APs': 76.16336633663366, 'APm': nan, 'APl': nan}), ('segm', {'AP': 57.400990099009896, 'AP50': 100.0, 'AP75': 69.05940594059405, 'APs': 57.400990099009896, 'APm': nan, 'APl': nan})])
====Start Evaluation on Wind 1 Test Set====
[32m[01/03 21:37:24 d2.data.datasets.coco]: [0mLoaded 21 images in COCO format from /home/eorslab/fatcat/SAR_DATASET/TSSWD/annotation/test/coco/annotation/Wind_1.json
[32m[01/03 21:37:24 d2.data.build]: [0mDistribution of instances among all 1 categories:
[36m| category | #instances |
|:----------:|:-------------|
| ship | 32 |
| | |[0m
[32m[01/03 21:37:24 d2.data.dataset_mapper]: [0m[DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(800, 800), max_size=1333, sample_style='choice')]
[32m[01/03 21:37:24 d2.data.common]: [0mSerializing 21 elements to byte tensors and concatenating them all ...
[32m[01/03 21:37:24 d2.data.common]: [0mSerialized dataset takes 0.01 MiB
[32m[01/03 21:37:24 d2.evaluation.evaluator]: [0mStart inference on 21 batches
[32m[01/03 21:37:26 d2.evaluation.evaluator]: [0mInference done 11/21. Dataloading: 0.0007 s/iter. Inference: 0.0788 s/iter. Eval: 0.0040 s/iter. Total: 0.0835 s/iter. ETA=0:00:00
[32m[01/03 21:37:27 d2.evaluation.evaluator]: [0mTotal inference time: 0:00:01.470796 (0.091925 s / iter per device, on 1 devices)
[32m[01/03 21:37:27 d2.evaluation.evaluator]: [0mTotal inference pure compute time: 0:00:01 (0.078798 s / iter per device, on 1 devices)
[32m[01/03 21:37:27 d2.evaluation.coco_evaluation]: [0mPreparing results for COCO format ...
[32m[01/03 21:37:27 d2.evaluation.coco_evaluation]: [0mSaving results to ./weather_test_final/Wind/Wind_1/coco_instances_results.json
[32m[01/03 21:37:27 d2.evaluation.coco_evaluation]: [0mEvaluating predictions with unofficial COCO API...
Loading and preparing results...
DONE (t=0.00s)
creating index...
index created!
[32m[01/03 21:37:27 d2.evaluation.fast_eval_api]: [0mEvaluate annotation type *bbox*
[32m[01/03 21:37:27 d2.evaluation.fast_eval_api]: [0mCOCOeval_opt.evaluate() finished in 0.00 seconds.
[32m[01/03 21:37:27 d2.evaluation.fast_eval_api]: [0mAccumulating evaluation results...
[32m[01/03 21:37:27 d2.evaluation.fast_eval_api]: [0mCOCOeval_opt.accumulate() finished in 0.00 seconds.
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.501
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.976
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.434
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.501
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.375
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.569
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.569
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.569
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000
[32m[01/03 21:37:27 d2.evaluation.coco_evaluation]: [0mEvaluation results for bbox:
| AP | AP50 | AP75 | APs | APm | APl |
|:------:|:------:|:------:|:------:|:-----:|:-----:|
| 50.098 | 97.584 | 43.394 | 50.098 | nan | nan |
[32m[01/03 21:37:27 d2.evaluation.coco_evaluation]: [0mSome metrics cannot be computed and is shown as NaN.
Loading and preparing results...
DONE (t=0.00s)
creating index...
index created!
[32m[01/03 21:37:27 d2.evaluation.fast_eval_api]: [0mEvaluate annotation type *segm*
[32m[01/03 21:37:27 d2.evaluation.fast_eval_api]: [0mCOCOeval_opt.evaluate() finished in 0.00 seconds.
[32m[01/03 21:37:27 d2.evaluation.fast_eval_api]: [0mAccumulating evaluation results...
[32m[01/03 21:37:27 d2.evaluation.fast_eval_api]: [0mCOCOeval_opt.accumulate() finished in 0.00 seconds.
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.469
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.976
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.448
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.469
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.353
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.531
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.531
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.531
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000
[32m[01/03 21:37:27 d2.evaluation.coco_evaluation]: [0mEvaluation results for segm:
| AP | AP50 | AP75 | APs | APm | APl |
|:------:|:------:|:------:|:------:|:-----:|:-----:|
| 46.934 | 97.584 | 44.849 | 46.934 | nan | nan |
[32m[01/03 21:37:27 d2.evaluation.coco_evaluation]: [0mSome metrics cannot be computed and is shown as NaN.
OrderedDict([('bbox', {'AP': 50.09790777021694, 'AP50': 97.58407602546853, 'AP75': 43.394295473503384, 'APs': 50.09790777021694, 'APm': nan, 'APl': nan}), ('segm', {'AP': 46.93375151588183, 'AP50': 97.58407602546853, 'AP75': 44.84934207706485, 'APs': 46.93375151588183, 'APm': nan, 'APl': nan})])
====Start Evaluation on Wind 2 Test Set====
[32m[01/03 21:37:27 d2.data.datasets.coco]: [0mLoaded 54 images in COCO format from /home/eorslab/fatcat/SAR_DATASET/TSSWD/annotation/test/coco/annotation/Wind_2.json
[32m[01/03 21:37:27 d2.data.build]: [0mDistribution of instances among all 1 categories:
[36m| category | #instances |
|:----------:|:-------------|
| ship | 127 |
| | |[0m
[32m[01/03 21:37:27 d2.data.dataset_mapper]: [0m[DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(800, 800), max_size=1333, sample_style='choice')]
[32m[01/03 21:37:27 d2.data.common]: [0mSerializing 54 elements to byte tensors and concatenating them all ...
[32m[01/03 21:37:27 d2.data.common]: [0mSerialized dataset takes 0.03 MiB
[32m[01/03 21:37:27 d2.evaluation.evaluator]: [0mStart inference on 54 batches
[32m[01/03 21:37:31 d2.evaluation.evaluator]: [0mInference done 41/54. Dataloading: 0.0013 s/iter. Inference: 0.0824 s/iter. Eval: 0.0074 s/iter. Total: 0.0912 s/iter. ETA=0:00:01
[32m[01/03 21:37:32 d2.evaluation.evaluator]: [0mTotal inference time: 0:00:04.519844 (0.092242 s / iter per device, on 1 devices)
[32m[01/03 21:37:32 d2.evaluation.evaluator]: [0mTotal inference pure compute time: 0:00:04 (0.082490 s / iter per device, on 1 devices)
[32m[01/03 21:37:32 d2.evaluation.coco_evaluation]: [0mPreparing results for COCO format ...
[32m[01/03 21:37:32 d2.evaluation.coco_evaluation]: [0mSaving results to ./weather_test_final/Wind/Wind_2/coco_instances_results.json
[32m[01/03 21:37:32 d2.evaluation.coco_evaluation]: [0mEvaluating predictions with unofficial COCO API...
Loading and preparing results...
DONE (t=0.00s)
creating index...
index created!
[32m[01/03 21:37:32 d2.evaluation.fast_eval_api]: [0mEvaluate annotation type *bbox*
[32m[01/03 21:37:32 d2.evaluation.fast_eval_api]: [0mCOCOeval_opt.evaluate() finished in 0.01 seconds.
[32m[01/03 21:37:32 d2.evaluation.fast_eval_api]: [0mAccumulating evaluation results...
[32m[01/03 21:37:32 d2.evaluation.fast_eval_api]: [0mCOCOeval_opt.accumulate() finished in 0.00 seconds.
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.524
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.959
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.544
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.524
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.259
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.583
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.590
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.590
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000
[32m[01/03 21:37:32 d2.evaluation.coco_evaluation]: [0mEvaluation results for bbox:
| AP | AP50 | AP75 | APs | APm | APl |
|:------:|:------:|:------:|:------:|:-----:|:-----:|
| 52.391 | 95.865 | 54.433 | 52.391 | nan | nan |
[32m[01/03 21:37:32 d2.evaluation.coco_evaluation]: [0mSome metrics cannot be computed and is shown as NaN.
Loading and preparing results...
DONE (t=0.00s)
creating index...
index created!
[32m[01/03 21:37:32 d2.evaluation.fast_eval_api]: [0mEvaluate annotation type *segm*
[32m[01/03 21:37:32 d2.evaluation.fast_eval_api]: [0mCOCOeval_opt.evaluate() finished in 0.01 seconds.
[32m[01/03 21:37:32 d2.evaluation.fast_eval_api]: [0mAccumulating evaluation results...
[32m[01/03 21:37:32 d2.evaluation.fast_eval_api]: [0mCOCOeval_opt.accumulate() finished in 0.00 seconds.
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.494
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.959
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.450
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.494
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.254
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.552
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.562
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.562
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000
[32m[01/03 21:37:32 d2.evaluation.coco_evaluation]: [0mEvaluation results for segm:
| AP | AP50 | AP75 | APs | APm | APl |
|:------:|:------:|:------:|:------:|:-----:|:-----:|
| 49.359 | 95.865 | 44.963 | 49.359 | nan | nan |
[32m[01/03 21:37:32 d2.evaluation.coco_evaluation]: [0mSome metrics cannot be computed and is shown as NaN.
OrderedDict([('bbox', {'AP': 52.39057518608819, 'AP50': 95.86523111566065, 'AP75': 54.4327181492567, 'APs': 52.39057518608819, 'APm': nan, 'APl': nan}), ('segm', {'AP': 49.3586364230507, 'AP50': 95.86523111566065, 'AP75': 44.962837293213575, 'APs': 49.3586364230507, 'APm': nan, 'APl': nan})])
====Start Evaluation on Wind 3 Test Set====
[32m[01/03 21:37:32 d2.data.datasets.coco]: [0mLoaded 50 images in COCO format from /home/eorslab/fatcat/SAR_DATASET/TSSWD/annotation/test/coco/annotation/Wind_3.json
[32m[01/03 21:37:32 d2.data.build]: [0mDistribution of instances among all 1 categories:
[36m| category | #instances |
|:----------:|:-------------|
| ship | 120 |
| | |[0m
[32m[01/03 21:37:32 d2.data.dataset_mapper]: [0m[DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(800, 800), max_size=1333, sample_style='choice')]
[32m[01/03 21:37:32 d2.data.common]: [0mSerializing 50 elements to byte tensors and concatenating them all ...
[32m[01/03 21:37:32 d2.data.common]: [0mSerialized dataset takes 0.03 MiB
[32m[01/03 21:37:32 d2.evaluation.evaluator]: [0mStart inference on 50 batches
[32m[01/03 21:37:36 d2.evaluation.evaluator]: [0mInference done 39/50. Dataloading: 0.0010 s/iter. Inference: 0.0790 s/iter. Eval: 0.0051 s/iter. Total: 0.0852 s/iter. ETA=0:00:00
[32m[01/03 21:37:37 d2.evaluation.evaluator]: [0mTotal inference time: 0:00:03.899344 (0.086652 s / iter per device, on 1 devices)
[32m[01/03 21:37:37 d2.evaluation.evaluator]: [0mTotal inference pure compute time: 0:00:03 (0.079012 s / iter per device, on 1 devices)
[32m[01/03 21:37:37 d2.evaluation.coco_evaluation]: [0mPreparing results for COCO format ...
[32m[01/03 21:37:37 d2.evaluation.coco_evaluation]: [0mSaving results to ./weather_test_final/Wind/Wind_3/coco_instances_results.json
[32m[01/03 21:37:37 d2.evaluation.coco_evaluation]: [0mEvaluating predictions with unofficial COCO API...
Loading and preparing results...
DONE (t=0.00s)
creating index...
index created!
[32m[01/03 21:37:37 d2.evaluation.fast_eval_api]: [0mEvaluate annotation type *bbox*
[32m[01/03 21:37:37 d2.evaluation.fast_eval_api]: [0mCOCOeval_opt.evaluate() finished in 0.00 seconds.
[32m[01/03 21:37:37 d2.evaluation.fast_eval_api]: [0mAccumulating evaluation results...