Comments (3)
Thank you for your interest @RGring.
Here are a couple of suggestions that the authors have put in the official repository and we have verified that ourselves and they work! We are yet to publish the changes but just wanted to confirm that those suggestions actually work.
Just curious, if you achieved the same results with the original pytorch implementation?
Actually, we did not run the official implementation so won't be able to lament.
I hope this helps.
from swav-tf.
Thanks for your answer. I've seen the suggestions and some of them helped, indeed. I guess, now it is a matter of hyperparameter-tuning. Looking forward to your new best score on the flower dataset :).
Thanks again!
from swav-tf.
Yes, that is generally the case. Sometimes simplifying the data augmentation pipeline also helps quite a lot.
You can also take a look at SimSiam (https://github.com/sayakpaul/SimSiam-TF) which I open-sourced yesterday. It's by FAIR. It's way simpler and it provides pretty comparable results to SwAV, BYOL, MoCov2, and SimCLR.
from swav-tf.
Related Issues (9)
- Pointers on Train_Step_And_Loss.ipynb HOT 6
- Pointers on Minimal_Sinkhorn_Knopp.ipynb HOT 1
- Fine-tuning with 10% labeled data with SwAV-learned embeddings
- Linear evaluation on the entire dataset with pre-trained ResNet50 features
- Copyright information HOT 1
- image loaders don't produce augmentations from the same image HOT 4
- Classify a single image HOT 2
- model loss plateau's after 2 epochs HOT 2
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