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Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time
Hello, Thank you for the great work. I understand that you provide the finetuned weights for ImageNet as well as the hyperparameter config. Furthermore, you have also privded the models...
Hello, Thank you for sharing your code - it's been very helpful. I have a question regarding the _timm_ version used in your research, specifically about the RandAug settings. I...
Hello, I found the comparison between the performance of Greedy Soup and Learned Soup in your recent paper particularly intriguing. I'm reaching out to ask if you could share a...
Currently, this repository is provided without a license. I recommend adding one to help clarify what users can (and not) do with the code.
Hello, I have a regression model that I composed by taking a MobileNet classifier (pre-trained with ImageNet weights), then removing its classification head and adding a flatten+dense layer that spits...
Hey guys, I have this classification dataset with currency denominations but I'm not sure how to use model-soup for fine-tuning. The way my dataset is structured is that I have...
Dear M. Wortsman, I am experimenting with Model Soups for four-class brain tumor classification. I use ViT-B32 with AdamW and CategoricalCrossentropy (with label_smoothing). I randomly created 12 model configurations from...
Is it possible with the existing code here on github to run the text classification experiments (fine-tuning a NLP model) in section 3.3.3 of the paper (https://arxiv.org/pdf/2203.05482.pdf) ? I mean,...
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Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time