add metric mFscore #509
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add metric mFscore #509
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## master #509 +/- ##
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+ Coverage 86.48% 86.69% +0.20%
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Files 97 99 +2
Lines 4974 5192 +218
Branches 807 838 +31
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+ Hits 4302 4501 +199
- Misses 519 533 +14
- Partials 153 158 +5
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Nice PR! Thx! |
xiexinch
approved these changes
Apr 26, 2021
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Hi @sshuair |
xvjiarui
reviewed
Apr 29, 2021
mmseg/core/evaluation/metrics.py
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| @@ -146,7 +166,7 @@ def mean_iou(results, | |||
| nan_to_num=nan_to_num, | |||
| label_map=label_map, | |||
| reduce_zero_label=reduce_zero_label) | |||
| return all_acc, acc, iou | |||
| return mIoU_result | |||
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Suggested change
| return mIoU_result | |
| return iou_result |
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We may use snake case for all variables.
mmseg/core/evaluation/metrics.py
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| @@ -185,7 +206,52 @@ def mean_dice(results, | |||
| nan_to_num=nan_to_num, | |||
| label_map=label_map, | |||
| reduce_zero_label=reduce_zero_label) | |||
| return all_acc, acc, dice | |||
| return mDice_result | |||
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Suggested change
| return mDice_result | |
| return dice_result |
mmseg/datasets/custom.py
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| summary_table_data = PrettyTable() | ||
| for key, val in ret_metrics_summary.items(): | ||
| summary_table_data.add_column(key, [val]) |
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We may also use term mIoU in the table.
mmseg/core/evaluation/metrics.py
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| <aAcc> float: Overall accuracy on all images. | ||
| <Acc> ndarray: Per category accuracy, shape (num_classes, ). | ||
| <Dice> ndarray: Per category dice, shape (num_classes, ). |
mmseg/core/evaluation/metrics.py
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| <aAcc> float: Overall accuracy on all images. | ||
| <Fscore> ndarray: Per category recall, shape (num_classes, ). | ||
| <Precision> ndarray: Per category precision, shape (num_classes, ). | ||
| <Recall> ndarray: Per category f-score, shape (num_classes, ). |
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We may use space 4 indent here.
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@sshuair Very nice PR. Just a few comments. |
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@xvjiarui the comments has been fixed. Please check it out. .....
| person | 24.65 | 63.46 | 15.3 | 14.06 | 15.3 | 24.65 |
| pottedplant | 0.23 | 30.68 | 0.12 | 0.12 | 0.12 | 0.23 |
| sheep | 0.81 | 17.06 | 0.41 | 0.4 | 0.41 | 0.81 |
| sofa | 15.42 | 12.16 | 21.04 | 8.35 | 21.04 | 15.42 |
| train | 9.86 | 5.48 | 48.95 | 5.19 | 48.95 | 9.86 |
| tvmonitor | 3.23 | 5.39 | 2.31 | 1.64 | 2.31 | 3.23 |
+-------------+--------+-----------+--------+-------+-------+-------+
2021-04-30 13:55:06,861 - mmseg - INFO - Summary:
2021-04-30 13:55:06,861 - mmseg - INFO -
+-------+---------+------------+---------+------+-------+-------+
| aAcc | mFscore | mPrecision | mRecall | mIoU | mAcc | mDice |
+-------+---------+------------+---------+------+-------+-------+
| 32.62 | 10.72 | 20.85 | 16.05 | 5.76 | 16.05 | 9.69 |
+-------+---------+------------+---------+------+-------+-------+
2021-04-30 13:55:06,864 - mmseg - INFO - Iter(val) [10] aAcc: 0.3262....
..... |
xvjiarui
approved these changes
Apr 30, 2021
bowenroom
pushed a commit
to bowenroom/mmsegmentation
that referenced
this pull request
Feb 25, 2022
* add mFscore and refactor the metrics return value * fix linting * some docstring and name fix
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Hi! Isn't mFscore in the current implementatin the same as mDice score, since the default beta=1 is used? Am I mistaken? |
aravind-h-v
pushed a commit
to aravind-h-v/mmsegmentation
that referenced
this pull request
Mar 27, 2023
* Finally fix the image-based SD tests * Remove autocast * Remove autocast in image tests
sibozhang
pushed a commit
to sibozhang/mmsegmentation
that referenced
this pull request
Mar 22, 2024
* resolve comments * update changelog * add class_weight in loss arguments * switch to mmcv 1.2.4 * use v1.1.1 as mmcv version lower bound * reorganize code * resolve comments
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This PR contributed a new feature: support for
f-score,recallandprecisionevaluation metrics. Issued by #420There are three main modifications:
mFscoremetric, it contain three sub-metrics,f-score,recallandprecision.metrics.pyreturn value from tuple to dict.custom.pyevaluatemethod log usingprettytablepackage instead ofterminaltables, because theterminaltablesis archived and no longer maintained.And the logs look like this: