zeekay
dda5b2a367
The previous commit was meant to remove .github/workflows and removed every tracked path instead — 892 files, the whole repo. This puts back the 874 that were never in scope; the eighteen workflow files stay gone, for the reasons that commit gives. Content is byte-identical to 9d2aec9 outside .github/workflows.
3.6 KiB
Image Models
::::{grid} 2 :margin: 1 1 0 0 :gutter: 1
:::{grid-item-card} {material-regular}model_training;1.5em CFA
🔗 ./cfa
:link-type: doc
Coupled-hypersphere-based Feature Adaptation for Target-Oriented Anomaly Localization :::
:::{grid-item-card} {material-regular}model_training;1.5em C-Flow
🔗 ./cflow
:link-type: doc
Real-Time Unsupervised Anomaly Detection via Conditional Normalizing Flows :::
:::{grid-item-card} {material-regular}model_training;1.5em CS-Flow
🔗 ./csflow
:link-type: doc
Fully Convolutional Cross-Scale-Flows for Image-based Defect Detection :::
:::{grid-item-card} {material-regular}model_training;1.5em DFKDE
🔗 ./dfkde
:link-type: doc
Deep Feature Kernel Density Estimation :::
:::{grid-item-card} {material-regular}model_training;1.5em DFM
🔗 ./dfm
:link-type: doc
Probabilistic Modeling of Deep Features for Out-of-Distribution and Adversarial Detection :::
:::{grid-item-card} {material-regular}model_training;1.5em DRAEM
🔗 ./draem
:link-type: doc
DRÆM – A discriminatively trained reconstruction embedding for surface anomaly detection :::
:::{grid-item-card} {material-regular}model_training;1.5em DSR
🔗 ./dsr
:link-type: doc
DSR – A Dual Subspace Re-Projection Network for Surface Anomaly Detection :::
:::{grid-item-card} {material-regular}model_training;1.5em Efficient AD
🔗 ./efficient_ad
:link-type: doc
EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies :::
:::{grid-item-card} {material-regular}model_training;1.5em FastFlow
🔗 ./fastflow
:link-type: doc
FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flows :::
:::{grid-item-card} {material-regular}model_training;1.5em FRE
🔗 ./fre
:link-type: doc
FRE: A Fast Method For Anomaly Detection And Segmentation :::
:::{grid-item-card} {material-regular}model_training;1.5em GANomaly
🔗 ./ganomaly
:link-type: doc
GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training :::
:::{grid-item-card} {material-regular}model_training;1.5em PaDiM
🔗 ./padim
:link-type: doc
PaDiM: A Patch Distribution Modeling Framework for Anomaly Detection and Localization :::
:::{grid-item-card} {material-regular}model_training;1.5em Patchcore
🔗 ./patchcore
:link-type: doc
Towards Total Recall in Industrial Anomaly Detection :::
:::{grid-item-card} {material-regular}model_training;1.5em Reverse Distillation
🔗 ./reverse_distillation
:link-type: doc
Anomaly Detection via Reverse Distillation from One-Class Embedding. :::
:::{grid-item-card} {material-regular}model_training;1.5em STFPM
🔗 ./stfpm
:link-type: doc
Student-Teacher Feature Pyramid Matching for Unsupervised Anomaly Detection :::
:::{grid-item-card} {material-regular}model_training;1.5em SuperSimpleNet
🔗 ./supersimplenet
:link-type: doc
SuperSimpleNet: Unifying Unsupervised and Supervised Learning for Fast and Reliable Surface Defect Detection :::
:::{grid-item-card} {material-regular}model_training;1.5em U-Flow
🔗 ./uflow
:link-type: doc
U-Flow: A U-shaped Normalizing Flow for Anomaly Detection with Unsupervised Threshold :::
:::{grid-item-card} {material-regular}model_training;1.5em VLM-AD
🔗 ./vlm_ad
:link-type: doc
VLM-AD: Vision-Language Model for Anomaly Detection :::
:::{grid-item-card} {material-regular}model_training;1.5em WinCLIP
🔗 ./winclip
:link-type: doc
WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentation ::: ::::
:caption: Data
:hidden:
./cfa
./cflow
./csflow
./dfkde
./dfm
./draem
./dsr
./efficient_ad
./fastflow
./fre
./ganomaly
./padim
./patchcore
./reverse_distillation
./stfpm
./uflow
./winclip