kumuji/ugains
[GCPR 2023] UGainS: Uncertainty Guided Anomaly Instance Segmentation
This project helps automotive engineers and quality control specialists automatically identify unusual or problematic objects in images. It takes raw image data, like car sensor output, and highlights unexpected items (e.g., debris, foreign objects) by drawing precise boundaries around them and indicating how anomalous each pixel within that object is. This is ideal for flagging potential issues on roads or in manufacturing processes.
No commits in the last 6 months.
Use this if you need to automatically detect and precisely outline unexpected objects or defects in visual data, such as images from autonomous vehicles or factory inspection cameras.
Not ideal if you need to detect anomalies in non-image data, or if you only need a general alert without specific object boundaries and pixel-level anomaly scores.
Stars
16
Forks
—
Language
Python
License
—
Category
Last pushed
Jul 31, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/computer-vision/kumuji/ugains"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
blaz-r/SuperSimpleNet
Official implementation of SuperSimpleNet [ICPR 2024, JIMS 2025]
tianyu0207/RTFM
Official code for 'Weakly-supervised Video Anomaly Detection with Robust Temporal Feature...
tientrandinh/Revisiting-Reverse-Distillation
(CVPR 2023) Revisiting Reverse Distillation for Anomaly Detection
eliahuhorwitz/3D-ADS
Official Implementation for the "Back to the Feature: Classical 3D Features are (Almost) All You...
MaticFuc/SALAD
[ICCV 2025] SALAD -- Semantics-Aware Logical Anomaly Detection