Results of classifying a custom dataset with the Ganomaly model

[mj16git]

If I change the 'normalization_method' in 'config.yaml' to 'min_max', the percentage of results should be in the range 0-100%, but some results are over 100%.
What causes the normalized score to exceed 100%?
I would also like to know what the resulting percentages represent.
I found out that the percentage is calculated from the normalized anomaly score from "#751", but how is the actual percentage calculated?
Please tell me the percentage when the anomaly score is 0.75 and when it is 0.25. (Assuming a threshold of 0.50)
The 'Config.yaml' settings for the custom dataset are as follows.

Config.yaml
dataset:
name: tube
format: folder
path: ./datasets/tube5
normal_dir: good
abnormal_dir: contamination
normal_test_dir: null
task: classification
mask: #optional
extensions: null
split_ratio: 0.2
train_batch_size: 32
test_batch_size: 32
eval_batch_size: 32
inference_batch_size: 32
num_workers: 8
image_size: 256 # dimensions to which images are resized (mandatory)
center_crop: null # dimensions to which images are center-cropped after resizing (optional)
normalization: imagenet # data distribution to which the images will be normalized: [none, imagenet]
transform_config:
train: null
val: null
create_validation_set: false
test_split_mode: from_dir # options: [from_dir, synthetic]
test_split_ratio: 0.2 # fraction of train images held out testing (usage depends on test_split_mode)
val_split_mode: same_as_test # options: [same_as_test, from_test, synthetic]
val_split_ratio: 0.5 # fraction of train/test images held out for validation (usage depends on val_split_mode)
tiling:
apply: false
tile_size: 64
stride: null
remove_border_count: 0
use_random_tiling: False
random_tile_count: 16

model:
name: ganomaly
latent_vec_size: 100
n_features: 64
extra_layers: 0
add_final_conv: true
early_stopping:
patience: 3
metric: image_AUROC
mode: max
lr: 0.0002
beta1: 0.5
beta2: 0.999
wadv: 1
wcon: 50
wenc: 1
normalization_method: min_max

metrics:
image:
- F1Score
- AUROC
threshold:
method: adaptive #options: [adaptive, manual]
manual_image: null

visualization:
show_images: False # show images on the screen
save_images: True # save images to the file system
log_images: True # log images to the available loggers (if any)
image_save_path: null # path to which images will be saved
mode: simple # options: ["full", "simple"]

project:
seed: 42
path: ./results

logging:
logger: [] # options: [comet, tensorboard, wandb, csv] or combinations.
log_graph: false # Logs the model graph to respective logger.

optimization:
export_mode: null

PL Trainer Args. Don't add extra parameter here.

trainer:
accelerator: auto # <"cpu", "gpu", "tpu", "ipu", "hpu", "auto">
accumulate_grad_batches: 1
amp_backend: native
auto_lr_find: false
auto_scale_batch_size: false
auto_select_gpus: false
benchmark: false
check_val_every_n_epoch: 2
default_root_dir: null
detect_anomaly: false
deterministic: false
devices: 1
enable_checkpointing: true
enable_model_summary: true
enable_progress_bar: true
fast_dev_run: false
gpus: null # Set automatically
gradient_clip_val: 0
ipus: null
limit_predict_batches: 1.0
limit_test_batches: 1.0
limit_train_batches: 1.0
limit_val_batches: 1.0
log_every_n_steps: 50
log_gpu_memory: null
max_epochs: 30
max_steps: -1
min_epochs: 30
min_steps: null
move_metrics_to_cpu: false
multiple_trainloader_mode: max_size_cycle
num_nodes: 1
num_processes: null
num_sanity_val_steps: 0
overfit_batches: 0.0
plugins: null
precision: 32
profiler: null
reload_dataloaders_every_n_epochs: 0
replace_sampler_ddp: true
strategy: null
sync_batchnorm: false
tpu_cores: null
track_grad_norm: -1
val_check_interval: 1.0