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# GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training | ||
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This is the implementation of the [GANomaly](https://arxiv.org/abs/1805.06725) paper. | ||
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Model Type: Classification | ||
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## Description | ||
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GANomaly uses the conditional GAN approach to train a Generator to produce images of the normal data. This Generator consists of an encoder-decoder-encoder architecture to generate the normal images. The distance between the latent vector $z$ between the first encoder-decoder and the output vector $\hat{z}$ is minimized during training. | ||
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The key idea here is that, during inference, when an anomalous image is passed through the first encoder the latent vector $z$ will not be able to capture the data correctly. This would leave to poor reconstruction $\hat{x}$ thus resulting in a very different $\hat{z}$. The difference between $z$ and $\hat{z}$ gives the anomaly score. | ||
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## Architecture | ||
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![GANomaly Architecture](../../../docs/source/images/ganomaly/architecture.jpg "GANomaly Architecture") | ||
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## Usage | ||
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`python tools/train.py --model ganomaly` | ||
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## Benchmark | ||
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All results gathered with seed `42`. | ||
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## [MVTec Dataset](https://www.mvtec.com/company/research/datasets/mvtec-ad) | ||
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### Image-Level AUC | ||
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### Image F1 Score |
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"""GANomaly Model.""" | ||
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# Copyright (C) 2020 Intel Corporation | ||
# | ||
# Licensed under the Apache License, Version 2.0 (the "License"); | ||
# you may not use this file except in compliance with the License. | ||
# You may obtain a copy of the License at | ||
# | ||
# http://www.apache.org/licenses/LICENSE-2.0 | ||
# | ||
# Unless required by applicable law or agreed to in writing, | ||
# software distributed under the License is distributed on an "AS IS" BASIS, | ||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
# See the License for the specific language governing permissions | ||
# and limitations under the License. | ||
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from .model import GanomalyLightning | ||
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__all__ = ["GanomalyLightning"] |
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dataset: | ||
name: mvtec | ||
format: mvtec | ||
path: ./datasets/MVTec | ||
url: ftp://guest:GU.205dldo@ftp.softronics.ch/mvtec_anomaly_detection/mvtec_anomaly_detection.tar.xz | ||
category: bottle | ||
task: classification | ||
label_format: None | ||
tiling: | ||
apply: true | ||
tile_size: 64 | ||
stride: null | ||
remove_border_count: 0 | ||
use_random_tiling: False | ||
random_tile_count: 16 | ||
image_size: 256 | ||
train_batch_size: 32 | ||
test_batch_size: 32 | ||
inference_batch_size: 32 | ||
num_workers: 32 | ||
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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 | ||
threshold: | ||
image_default: 0 | ||
adaptive: true | ||
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project: | ||
seed: 0 | ||
path: ./results | ||
log_images_to: [] | ||
logger: false | ||
save_to_csv: false | ||
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optimization: | ||
compression: | ||
apply: false | ||
nncf: | ||
apply: false | ||
input_info: | ||
sample_size: null | ||
compression: | ||
algorithm: quantization | ||
initializer: | ||
range: | ||
num_init_samples: 256 | ||
update_config: | ||
init_weights: snapshot.ckpt | ||
hyperparameter_search: | ||
parameters: | ||
lr: | ||
min: 1e-4 | ||
max: 1e-2 | ||
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# PL Trainer Args. Don't add extra parameter here. | ||
trainer: | ||
accelerator: null | ||
accumulate_grad_batches: 1 | ||
amp_backend: native | ||
amp_level: O2 | ||
auto_lr_find: false | ||
auto_scale_batch_size: false | ||
auto_select_gpus: false | ||
benchmark: false | ||
check_val_every_n_epoch: 2 | ||
checkpoint_callback: true | ||
default_root_dir: null | ||
deterministic: true | ||
distributed_backend: null | ||
fast_dev_run: false | ||
flush_logs_every_n_steps: 100 | ||
gpus: 1 | ||
gradient_clip_val: 0 | ||
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: 100 | ||
max_steps: null | ||
min_epochs: null | ||
min_steps: null | ||
move_metrics_to_cpu: false | ||
multiple_trainloader_mode: max_size_cycle | ||
num_nodes: 1 | ||
num_processes: 1 | ||
num_sanity_val_steps: 0 | ||
overfit_batches: 0.0 | ||
plugins: null | ||
precision: 32 | ||
prepare_data_per_node: true | ||
process_position: 0 | ||
profiler: null | ||
progress_bar_refresh_rate: null | ||
reload_dataloaders_every_epoch: false | ||
replace_sampler_ddp: true | ||
stochastic_weight_avg: false | ||
sync_batchnorm: false | ||
terminate_on_nan: false | ||
tpu_cores: null | ||
track_grad_norm: -1 | ||
truncated_bptt_steps: null | ||
val_check_interval: 1.0 | ||
weights_save_path: null | ||
weights_summary: top |
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