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Pre-trained weights? #2
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Beacause of the ploicy of our institution, we cannot send the pre-trained models out directly. We plan to find some gpu servers outside, but it will take time. So we are afraid the models will not be released recently. |
Hi, I trained a model with the provided codes on ImageNet-1k only with 4x2080ti (batch100), finally reach 82.0 around. I upload this temporal alternative in google drive to facilate other's needs. https://drive.google.com/drive/folders/18GpH1SeVOsq3_2QGTA5Z_3O1UFtKugEu?usp=sharing |
That's great! I will add it to readme for someone else need it. Thanks a lot! |
Assuming this is Visformer small? |
yes, I trained the visformer small with 224: visformer_small |
Thanks for sharing your works! I really love the architecture and experiments that you guys did. I could find out how to improve the performance of transformer models with convolutional layer. I trained the visformer tiny with 224. If I upload the pretrained weight, will it can help other researchers? |
Thanks for your attention! Now only the weights of Visformer-small are available. So I think tiny weights can be helpful for someone. By the way, for tiny model, setting '--drop-path=0.0' can slightly improve the performance. |
I trained the model with the below command having set '--drop-path' to 0.
Please check my weight and share this link on Readme file! https://drive.google.com/file/d/1LLBGbj7-ok1fDvvMCab-Fn5T3cjTzOKB/view?usp=sharing |
I have added it. Thanks for your sharing! |
@danczs Okay! Thanks! |
By slightly adjusting the model, Visformer can use amp now. During inference, old weights can utilize amp as well. One can refer to ReadMe for details. |
Hi, I want to extend the model on my own task, will you release pre-trained weights?
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