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On uncertainty estimation in active learning for image segmentation
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papers
On uncertainty estimation in active learning for image segmentation
Commits
86802244
Commit
86802244
authored
4 years ago
by
blia
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86802244
...
...
@@ -33,7 +33,7 @@ In order to evaluate the model at each acquisition step, run
```
python
python3
-
c
'
import Test as te;te.running_test_for_single_acquisition_step(model_dir)
'
Args
:
model_dir
:
the
directory
that
saves
the
model
ckpt
model_dir
:
the
directory
that
saves
the
model
ckpt
```
#### Train the model
...
...
@@ -43,15 +43,16 @@ Args:
```
python
python3
-
c
'
import Train_Active_Full_Im as tafi;tafi.running_loop_active_learning_full_image(stage)
'
Args
:
stage
:
int
,
0
:
random
,
1
:
VarRatio
,
2
:
Entropy
,
3
:
BALD
stage
:
int
,
0
:
random
,
1
:
VarRatio
,
2
:
Entropy
,
3
:
BALD
```
-
For region based active learning, run
```
python
python3
-
c
'
import Train_Active_Region_Im as tari;tari.running_loop_active_learning_region(stage)
'
Args
:
stage
:
int
,
0
:
random
,
1
:
VarRatio
,
2
:
Entropy
,
3
:
BALD
```
```
python
python3
-
c
'
import Train_Active_Region_Im as tari;tari.running_loop_active_learning_region(stage)
'
Args
:
stage
:
int
,
0
:
random
,
1
:
VarRatio
,
2
:
Entropy
,
3
:
BALD
```
#### Reproduce figures
...
...
@@ -64,6 +65,6 @@ wget https:
tar
-xzvf
calibration_score.tar.gz
cd
..
cd
eval_calibration
python3 visualize_calibration_score.py
--save
Fals
e
python3 visualize_calibration_score.py
--save
Tru
e
```
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