patchcore-resnet50
by amazon
Memory-bank anomaly detection achieving 99.6% AUROC on manufacturing defects
amazon/patchcore-resnet50mixpeek://image_extractor@v1/amazon_patchcore_r50_v1Overview
PatchCore solves cold-start anomaly detection in industrial manufacturing using only normal (non-defective) images. It builds a maximally representative memory bank of nominal patch-level features from ImageNet-pretrained models, then uses nearest-neighbor outlier detection.
On Mixpeek, PatchCore enables visual quality inspection — upload examples of normal products, and detect defects, anomalies, and deviations automatically.
Architecture
Builds a coreset memory bank of mid-level patch features from a frozen ResNet-50 (ImageNet-pretrained). Uses greedy coreset subsampling for efficient memory. Anomaly scoring via nearest-neighbor distance to the memory bank.
Mixpeek SDK Integration
import { Mixpeek } from "mixpeek";
const mx = new Mixpeek({ apiKey: "API_KEY" });
// Managed: create a collection over a bucket; Mixpeek runs this model's extractor
const collection = await mx.collections.create({
namespace_id: "my-namespace",
collection_name: "my-collection",
source: { type: "bucket", bucket_ids: ["bkt_your_bucket"] },
feature_extractor: {
feature_extractor_name: "anomaly_detection",
version: "v1",
parameters: { model_id: "amazon/patchcore-resnet50" },
},
});Capabilities
- 99.6% AUROC on MVTec AD benchmark
- Cold-start: only needs normal images, no defect examples
- Pixel-level anomaly localization maps
- Halved the error of previous best methods
Use Cases on Mixpeek
Benchmarks
| Dataset | Metric | Score | Source |
|---|---|---|---|
| MVTec AD | Image AUROC | 99.1% | Roth et al., 2022 — Table 1 |
| MVTec AD | Pixel AUROC | 98.1% | Roth et al., 2022 — Table 1 |
Performance
Requires a reference corpus of normal images for comparison
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Specification
Research Paper
Towards Total Recall in Industrial Anomaly Detection
arxiv.orgBuild a pipeline with patchcore-resnet50
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