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Author:
Shantanusinh Parmar
Summary:
PhysAugNet: VQ-VAE and physics-inspired (thermal + grain) augmentation pipeline for metal defect segmentation
Latest version:
0.4.2
Required dependencies:
absl-py
|
addict
|
basicsr
|
certifi
|
charset-normalizer
|
contourpy
|
cycler
|
debugpy
|
facexlib
|
filelock
|
filterpy
|
fonttools
|
fsspec
|
future
|
gfpgan
|
grpcio
|
idna
|
imageio
|
ipykernel
|
jinja2
|
joblib
|
jupyter_client
|
kiwisolver
|
lazy_loader
|
llvmlite
|
lmdb
|
markdown
|
markupsafe
|
matplotlib
|
mpmath
|
networkx
|
numba
|
numpy
|
opencv-python
|
packaging
|
pillow
|
platformdirs
|
protobuf
|
pyparsing
|
python-dateutil
|
pyyaml
|
pyzmq
|
realesrgan
|
requests
|
scikit-image
|
scikit-learn
|
scipy
|
six
|
sympy
|
tb-nightly
|
tensorboard
|
tensorboard-data-server
|
threadpoolctl
|
tifffile
|
tomli
|
torch
|
torchaudio
|
torchvision
|
tornado
|
tqdm
|
typing_extensions
|
urllib3
|
werkzeug
|
yapf
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