TensorFlow Datasets for Defect Detection
To directly jump into the code look at the sample notebook
Install
Create a new python=3.9 env and install tfds_defect_detection from pip
pip install tfds_defect_detection
Examples
import tfds_defect_detection as tfd
tfd.load()
Usage
All parmeters
import tfds_defect_detection as tfd
impor albumentations as A
ds = tfd.load(
names = ("mvtec", "visa"),
data_dir=Path("."),
pairing_mode = "result_with_contrastive_pair", # "result_only", "result_with_original"
create_artificial_anomalies=True,
validation_split=0.2,
subset_mode = "training", # "validation", "test", "holdout", None
drop_masks=False,
width=256,
height=256,
repeat=True,
anomaly_size = None,
global_transform=A.Compose([
A.RandomBrightnessContrast(),
A.HueSaturationValue(),
]),
process_deviation=A.Compose([
A.ShiftScaleRotate(
shift_limit=0.01,
scale_limit=0.0,
rotate_limit=1.5,
p=1
),
A.Blur(blur_limit=3),
A.RandomBrightnessContrast(),
A.RandomGamma(),
A.HueSaturationValue(),
]),
anomaly_composition=A.Compose([
A.RandomRotate90(),
A.Transpose(),
A.ShiftScaleRotate(
shift_limit=0.0625,
scale_limit=0.50,
rotate_limit=45, p=1
),
A.RandomGamma(),
A.OpticalDistortion(),
A.GridDistortion(),
A.RandomContrast(0.5, p=1),
]),
batch_size=9,
seed=123,
shuffle=True,
peek=True,
image_validation=False,
delete_tmp=True,
crop_to_aspect_ratio=True
)
Release files for tfds-defect-detection 1.0.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| tfds_defect_detection-1.0.0.tar.gz | 19.4 kB | Details |
Release files / tfds_defect_detection-1.0.0.tar.gz
| Download URL | tfds_defect_detection-1.0.0.tar.gz |
|---|---|
| Size | 19.4 kB |
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|
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