From 18025d10c575bc0ecae15376b78008dd4427dd6e Mon Sep 17 00:00:00 2001 From: lukas-heilgenbrunner Date: Mon, 9 Dec 2024 16:20:48 +0100 Subject: [PATCH] add patchcore overview --- materialandmethods.typ | 30 +++++++++++++++++++++++++++--- rsc/patchcore_overview.png | Bin 0 -> 428340 bytes 2 files changed, 27 insertions(+), 3 deletions(-) create mode 100644 rsc/patchcore_overview.png diff --git a/materialandmethods.typ b/materialandmethods.typ index eb16fc4..ab7d81a 100644 --- a/materialandmethods.typ +++ b/materialandmethods.typ @@ -98,12 +98,36 @@ This helps the model to learn the underlying patterns and to generalize well to In few-shot learning the model has to generalize from just a few samples. === Patchcore +PatchCore is an advanced method designed for cold-start anomaly detection and localization, primarily focused on industrial image data. +It operates on the principle that an image is anomalous if any of its patches is anomalous. +The method achieves state-of-the-art performance on benchmarks like MVTec AD with high accuracy, low computational cost, and competitive inference times. #cite() -%todo also show values how they perform on MVTec AD +// todo vll ersten absatz umofrmulieren und vereinfachen +The PatchCore framework leverages a pre-trained convolutional neural network (e.g., WideResNet50) to extract mid-level features from image patches. +By focusing on intermediate layers, PatchCore balances the retention of localized information with a reduction in bias associated with high-level features pre-trained on ImageNet. +To enhance robustness to spatial variations, the method aggregates features from local neighborhoods using adaptive pooling, which increases the receptive field without sacrificing spatial resolution. #cite() + +A crucial component of PatchCore is its memory bank, which stores patch-level features derived from the training dataset. +This memory bank represents the nominal distribution of features against which test patches are compared. +To ensure computational efficiency and scalability, PatchCore employs a coreset reduction technique to condense the memory bank by selecting the most representative patch features. +This optimization reduces both storage requirements and inference times while maintaining the integrity of the feature space. #cite() + +During inference, PatchCore computes anomaly scores by measuring the distance between patch features from test images and their nearest neighbors in the memory bank. +If any patch exhibits a significant deviation, the corresponding image is flagged as anomalous. +For localization, the anomaly scores of individual patches are spatially aligned and upsampled to generate segmentation maps, providing pixel-level insights into the anomalous regions. #cite() + + +Patchcore reaches a 99.6% AUROC on the MVTec AD dataset when detecting anomalies. +A great advantage of this method is the coreset subsampling reducing the memory bank size significantly. +This lowers computational costs while maintaining detection accuracy. #cite() + +#figure( + image("rsc/patchcore_overview.png", width: 80%), + caption: [Architecture of Patchcore. #cite()], +) -=== EfficientAD -todo stuff #cite() // https://arxiv.org/pdf/2106.08265 +=== EfficientAD todo stuff #cite() // https://arxiv.org/pdf/2303.14535 diff --git a/rsc/patchcore_overview.png b/rsc/patchcore_overview.png new file mode 100644 index 0000000000000000000000000000000000000000..1a185bb8c3e059147b34292a7e6cb98e16368a0a GIT binary patch literal 428340 zcmaI81zc1A_dkxlZAAq{6hxF5(hbt0fD9NQ-5||iAl+diEesfpR7Q-H*kGhVw~QX4 z)aZsu|1RL$&$s^n`*=9rYxlnHE6(ej=XsuU3|3QlN`8s<5*Zm8Iaonfos8@vhK%eq z^1?Zw~uTSsob+gNNHB?6m2S3yi%@UiW zRj$LexTpt71g-lNOwga7knCTLc)gl3@7Zw5JcX|_-nva-`u2L5SA)dgue;W5{~6Jj zjdN&NjLQ`0mdMvz|NXwdk45$zZD4xI*xAI$_=xywm&Ia|#MbW6lxo%7m!T=5gs0x( zT8X1{y8CvC!^0YPuR#_1;<1V;qSWq8e5Y2Xm<1h`Q-QOJTa!@ydzV-u>Z;dOBFV5( z{A$DBL5KbGUq<-%&C**gi)Yp8M9!37Z1}&X0^GRGCNvG~Xqxn|qbZcHw*FuDv1&o# zCfxIN<(t7=Y9-y))6`1;TPzg4SE 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