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hmsch / natural-synthetic-anomalies

Code for ECCV 2022 paper "Natural Synthetic Anomalies for Self-Supervised Anomaly Detection and Localization".
https://arxiv.org/abs/2109.15222
MIT License
48 stars 6 forks source link

[Request] pre-trained model #5

Open YeongHyeon opened 1 year ago

YeongHyeon commented 1 year ago

Thank you for providing research materials.

The provided source code seems to be structured so that training can proceed with the public dataset. Apart from this, if you don't mind, can I ask you to provide the pre-trained model made for your experiments?

Hope for positive consideration.

TerryMelody commented 7 months ago

Thank you for providing research materials.

The provided source code seems to be structured so that training can proceed with the public dataset. Apart from this, if you don't mind, can I ask you to provide the pre-trained model made for your experiments?

Hope for positive consideration.

Hi have you reproducded the results as paper stated? I have run some classes but the results are different from the paper.

YeongHyeon commented 7 months ago

@TerryMelody Unfortunatly, I have forgot this repo. However, the reason for opening this issue may same as your experience probabily.

TerryMelody commented 7 months ago

@TerryMelody Unfortunatly, I have forgot this repo. However, the reason for opening this issue may same as your experience probabily.

Okay thank you for your reply!