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AD Cobots Synthetic

This repository contains trained model checkpoints for anomaly detection in industrial robotic environments developed within the DistriMuSe Project.

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233 MB
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vRevision 80973c8
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Sep 28, 2026
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Hugging Face

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This repository contains trained model checkpoints for anomaly detection in industrial robotic environments developed within the DistriMuSe Project.

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Hugging FacePrimaryVersion 80973c8ba89afb4eb8eda0df14cb41dd3d23b205
huggingface.co/rashidrao/AD_Cobots_Synthetic/tree/80973c8ba89afb4eb8eda0df14cb41dd3d23b205

Verified Sep 28

Model artifacts

4 artifacts

checkpoints/model_ConvBelt_64.pt

pt · 233 MB · SHA-256 5e895d942931…1689 · Hugging Face

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checkpoints/model_PLeft_64.pt

pt · 233 MB · SHA-256 09c32b2ffd83…376a · Hugging Face

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checkpoints/model_PRight_64.pt

pt · 233 MB · SHA-256 2b7186db9c58…4092 · Hugging Face

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checkpoints/model_RoboArm_64.pt

pt · 233 MB · SHA-256 1eeec5484d75…2882 · Hugging Face

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Source excerpts

2 excerpts

--- license: mit language: - en tags: - anomaly-detection - computer-vision - vae-gan - industrial-ai - robotics - safety-monitoring - visual-anomaly-detection - pytorch - distrimuse library_name: pytorch pipeline_tag: image-classification --- # DistriMuSe UC3 - Safety Area VAE-GAN Checkpoints <img src="docs/workflow.svg"> ## Overview This repository contains trained model checkpoints for anomaly detection in industrial robotic environments developed within the DistriMuSe Project. The models were trained on a synthetic palletizing workflow generated by the Valeria-Lab, University of Granada, Spain, for the use case: **UC3 — Safe Interaction and Cooperation with Robots** The objective of the system is to monitor safety-critical areas around a collaborative robotic workstation and automatically detect abnormal situations that may indicate unsafe operating conditions. ## Safety Areas The repository provides independently trained VAE-GAN models for the following safety regions: - PLeft (Left Personnel Area) - PRight (Right Personnel Area) - RoboArm (Robot Manipulator Area) - ConvBelt (Conveyor Belt Area) Each model has been trained exclusively on normal operating conditions following a one-class anomaly detection paradigm. ## Dataset The checkpoints were trained on a synthetic palletizing dataset generated by: **Valeria-Lab** Department of Computer Engineering, Automatics and Robotics University of Granada, Spain The dataset was created as part of the DistriMuSe project to support the development and validation of safety monitoring systems for collaborative robotics. ## Project **DistriMuSe — Distributed Multi-Sensory Safety Platform** Use Case 3: **Safe Interaction and Cooperation with Robots** The anomaly detection framework was developed by: - University of Turin, Italy - Rulex Innovation Labs, Italy in collaboration with: - Valeria-Lab, University of Granada, Spain ## Source Code The complete training and inference framework used to generate these checkpoints is available at: ### AD_MultiPointThreshold https://github.com/rashidrao-pk/AD_MultiPointThreshold This repository contains: - VAE training - VAE-GAN training - Multi-point threshold calibration - Safety-area masking - Evaluation tools - Visualization utilities - Anomaly score analysis ### DistriMuSe Industrial Pipeline The original DistriMuSe implementation, including R...

Source context: 0 downloads · 0 likes · Pipeline image-classification · Library pytorch · Repo rashidrao/AD_Cobots_Synthetic