AnomaVision

⚙️ Configuration Guide

AnomaVision scripts (train.py, detect.py, eval.py, export.py) all accept a YAML/JSON config file. You can override any field via CLI arguments.

1. Dataset & Preprocessing

Key	Type	Default	Description
dataset_path	str	None	Root dataset folder containing MVTec-style structure.
class_name	str	None	Target class name.
resize	[int,int]	None	Resize before processing.
crop_size	[int,int]	None	Center crop size.
normalize	bool	True	Apply input normalization.
norm_mean	[float]	[0.485,0.456,0.406]	RGB mean.
norm_std	[float]	[0.229,0.224,0.225]	RGB standard deviation.

2. Algorithm selection

The historical selector remains supported:

algorithm: padim

Available values are padim, patchcore, and efficientad.

A native model selector is also supported:

model:
  name: efficientad

When model.name is used, load_config() maps it to the same internal algorithm value, so the existing CLI workflow does not change.

3. Training

Key	Type	Default	Description
backbone	str	resnet18	PaDiM/PatchCore feature extractor.
batch_size	int	16	Training batch size.
feat_dim	int	100	PaDiM feature dimensions.
layer_indices	list	[0,1,2]	PaDiM/PatchCore feature layers.
run_name	str	exp	Training run name.
model_data_path	str	./distributions	Model artifact root.
output_model	str	padim_model.pt	Saved model filename.

EfficientAD

Key	Type	Default	Description
efficientad_model_size	str	s	EfficientAD size: s or m.
efficientad_lr	float	0.0001	Adam learning rate.
efficientad_weight_decay	float	0.00001	Adam weight decay.
efficientad_epochs	int	1	Number of normal-data training epochs.
efficientad_pretrained_teacher	bool	true	Use ImageNet-pretrained EfficientNet teacher.

EfficientAD requires normalize: true in the current integration because the teacher uses ImageNet preprocessing.

4. Detection

Key	Type	Default	Description
img_path	str	None	Path to test images or folder.
model	str	None	Model file (.pt, .pth, .onnx).
device	str	auto	Device (cpu, cuda, or auto).
batch_size	int	1	Inference batch size.
thresh	float	None	Legacy global anomaly threshold.
thresh_padim	float	None	PaDiM-specific threshold.
thresh_patchcore	float	None	PatchCore-specific threshold.
thresh_efficientad	float	None	EfficientAD-specific threshold.
enable_visualization	bool	False	Enable heatmap overlays.
save_visualizations	bool	False	Save visualization images.
viz_output_dir	str	./results/	Visualization directory.

5. PatchCore

Key	Type	Default	Description
coreset_ratio	float	0.02	Fraction of normal patches retained.
max_memory_patches	int	2048	Hard memory-bank cap.
patch_grid	int	14	Spatial pooling grid.
search_chunk_size	int	1024	Nearest-neighbor chunk size.
coreset_method	str	kcenter	kcenter or random.
coreset_seed	int	42	Coreset reproducibility seed.

6. Evaluation

Key	Type	Default	Description
memory_efficient	bool	True	Use memory-efficient evaluation.
detailed_timing	bool	False	Log detailed timings.

Other keys mirror Detection and Training.

7. Export

Key	Type	Default	Description
format	str	onnx	onnx, torchscript, openvino, all.
precision	str	auto	fp32, fp16, or auto.
opset	int	17	ONNX opset version.
static_batch	bool	False	Disable dynamic batch.
quantize_dynamic	bool	False	Export dynamic INT8 ONNX.
quantize_static	bool	False	Export static INT8 ONNX.
calib_samples	int	100	Static quantization samples.

Example: switch PaDiM → EfficientAD

# Everything else in the existing config can remain unchanged.
algorithm: efficientad

efficientad_model_size: s
efficientad_lr: 0.0001
efficientad_weight_decay: 0.00001
efficientad_epochs: 1
efficientad_pretrained_teacher: true

Then use the same commands:

anomavision train --config config.yml
anomavision export --config config.yml --model model.pt --format onnx
anomavision detect --config config.yml --model model.onnx --img_path ./test_images
anomavision eval --config config.yml --model model.pt --class_name bottle

See docs/efficientad.md for the complete EfficientAD guide.

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