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Domain Generalization through Distilling CLIP with Language Guidance
Hi, thanks for your good work, i've found a small error in https://github.com/OoDBag/RISE#view-the-results)
python evaluate_results.py\ --dataset "PACS" --output_foler "sweep1"
'output_foler' should be 'output_folder'
By the way, I have a question about the final result: In your paper, which result did you choose? The average corresponding test result or the average best test result? THANKS~
When will the source code be released?
I have run the code in VLCS dataset successfully and got several test accuracy in each domain, but after that how can I get a final result in this dataset? By calculating the weighted average accuracy of these four different domains or some other methods like a simple average of those test accuracy? It seems the result higher a lot than that showed in paper(81.7%) when I calculated in the weighted average way, which makes me a little confused.
Thanks for your impressive work!
For a fair comparison, I am wondering if it's possible to release the CLIP weights pre-trained on the Terra dataset?
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