Content for tag PyTorch:

Official implementation of 3DSR / 3DRÆM-style depth-simulation-based methods for 3D anomaly detection used in the WACV 2024, Pattern Recognition Letters 2024, and related MV4.0 outputs.

Code for AnomalyVFM, a zero-shot anomaly detection framework based on vision foundation models.

PyTorch implementation of Center Direction Regression Network for object counting and localization with point supervision from Pattern Recognition 2024 paper.

PyTorch implementation of Center Direction Regression Network for Grasping Point Localization on Cloths from IEEE Robotics and Automation Letters 2024 paper.

Official implementation of DAVE, a detect-and-verify paradigm for low-shot counting from the CVPR 2024 paper.

Official implementation of DSR, a dual subspace re-projection network for surface anomaly detection from the ECCV 2022 paper.

Code for SALAD, a semantics-aware logical anomaly detection method developed within MUXAD.

Implementation for automated crack detection and segmentation in concrete surfaces from the Construction and Building Materials 2023 paper.

PyTorch implementation of SuperSimpleNet for fast and reliable surface defect detection across unsupervised and supervised settings.

Official PyTorch implementation of TransFusion, a transparency-based diffusion model for anomaly detection from the ECCV 2024 paper.

PyTorch implementation of SegDec-Net using weakly, mixed and fully supervised learning for surface defect detection. Implementation from ICPR2020 and COMIND2021 papers.