Datasets:
Label by rule: infected (2) vs uninfected (1); hand annotations removed
Browse files- settings/classify_settings.csv +52 -52
settings/classify_settings.csv
CHANGED
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@@ -1,52 +1,52 @@
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Key,Value
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amsgrad,True
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annotation_column,
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augment,False
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batch_size,32
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class_balance,none
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class_folder_names,"['nc', 'pc']"
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classes,"['1', '2']"
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classifier_evaluation,True
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crop_source,auto
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cross_validation_enabled,False
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cross_validation_folds,0
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custom_model_path,
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cv_group_by,well
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dropout_rate,0.1
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epochs,10
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evaluation_bins,10
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evaluation_calibration,temperature
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| 19 |
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evaluation_fail_on_leakage,True
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gradient_accumulation,True
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gradient_accumulation_steps,4
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holdout_plate,
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image_size,224
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init_weights,True
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intermedeate_save,True
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leakage_audit_train_test,True
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leakage_hash_content,True
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leakage_require_identity,True
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learning_rate,0.001
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loss_type,focal_loss
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max_failure_rate,
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mixed_precision,False
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model_type,maxvit_t
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n_jobs,30
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nested_cv_inner_folds,0
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normalize,True
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optimizer_type,adamw
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pin_memory,False
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plot,True
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resume_checkpoint,
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schedule,cosine
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src,<dataset>
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strict_errors,
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tensorboard,True
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test,False
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train,True
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train_channels,"['r', 'g', 'b']"
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train_mode,erm
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use_checkpoint,True
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val_split,0.1
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verbose,False
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| 52 |
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weight_decay,1e-05
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Key,Value
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+
amsgrad,True
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| 3 |
+
annotation_column,infected
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| 4 |
+
augment,False
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| 5 |
+
batch_size,32
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| 6 |
+
class_balance,none
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| 7 |
+
class_folder_names,"['nc', 'pc']"
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| 8 |
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classes,"['1', '2']"
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| 9 |
+
classifier_evaluation,True
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| 10 |
+
crop_source,auto
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| 11 |
+
cross_validation_enabled,False
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| 12 |
+
cross_validation_folds,0
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| 13 |
+
custom_model_path,
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| 14 |
+
cv_group_by,well
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| 15 |
+
dropout_rate,0.1
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| 16 |
+
epochs,10
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| 17 |
+
evaluation_bins,10
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| 18 |
+
evaluation_calibration,temperature
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| 19 |
+
evaluation_fail_on_leakage,True
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| 20 |
+
gradient_accumulation,True
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| 21 |
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gradient_accumulation_steps,4
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| 22 |
+
holdout_plate,
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| 23 |
+
image_size,224
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| 24 |
+
init_weights,True
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| 25 |
+
intermedeate_save,True
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| 26 |
+
leakage_audit_train_test,True
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| 27 |
+
leakage_hash_content,True
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| 28 |
+
leakage_require_identity,True
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| 29 |
+
learning_rate,0.001
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| 30 |
+
loss_type,focal_loss
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| 31 |
+
max_failure_rate,
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| 32 |
+
mixed_precision,False
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| 33 |
+
model_type,maxvit_t
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| 34 |
+
n_jobs,30
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| 35 |
+
nested_cv_inner_folds,0
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| 36 |
+
normalize,True
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| 37 |
+
optimizer_type,adamw
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| 38 |
+
pin_memory,False
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| 39 |
+
plot,True
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resume_checkpoint,
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+
schedule,cosine
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+
src,<dataset>
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+
strict_errors,
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+
tensorboard,True
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| 45 |
+
test,False
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| 46 |
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train,True
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| 47 |
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train_channels,"['r', 'g', 'b']"
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| 48 |
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train_mode,erm
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| 49 |
+
use_checkpoint,True
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| 50 |
+
val_split,0.1
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| 51 |
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verbose,False
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weight_decay,1e-05
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