PADIM method consumes too much memory
See original GitHub issueHi there,
I am using the pipeline based on the padim method and backbone_name=“wide_resnet50_2”. However, I see excessive raise in my memory when I check it with htop
command in another terminal. I don’t have same experience with spade method.
As picture shows, it consumes up to 121G in the memory. I even tried to reduce the ‘d_reduced’ parameter, but it is still around 100G. May I ask about your opinion how can I make it better? did U also faced same issue?
Best
Issue Analytics
- State:
- Created 2 years ago
- Comments:5 (3 by maintainers)
Top Results From Across the Web
PaDiM : A machine learning model for detecting defective ...
There are three methods for detecting defective products using machine learning. Learning from normal and defective products (at least 10000 ...
Read more >PaDiM: a Patch Distribution Modeling Framework for Anomaly ...
Abstract—We present a new framework for Patch Distribution. Modeling, PaDiM, to concurrently detect and localize anomalies.
Read more >A practical guide to anomaly detection using Anomalib
A short guide on unsupervised anomaly detection and how to apply it using Anomalib.
Read more >The IDE is really taking up too much memory
Java application running in JVM. If high memory usage is accompanied by slowness or high CPU usage, that could be a problem.
Read more >Why does my Java process consume more memory than Xmx?
You have added -Xmx option to your startup scripts and were sure no way your Java process is going to use more memory...
Read more >
Top Related Medium Post
No results found
Top Related StackOverflow Question
No results found
Troubleshoot Live Code
Lightrun enables developers to add logs, metrics and snapshots to live code - no restarts or redeploys required.
Start Free
Top Related Reddit Thread
No results found
Top Related Hackernoon Post
No results found
Top Related Tweet
No results found
Top Related Dev.to Post
No results found
Top Related Hashnode Post
No results found
Djezus!
First of all, I shed some light on memory issues and possible solutions here: https://github.com/rvorias/ind_knn_ad/issues/8#issuecomment-902633484
What dataset are you using? If custom, how many training samples are in there?
I think you will run into trouble for every method. Maybe you can try it with a subset of the training images?