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The pre-trained GearNet-Edge model for Fold Classification #41
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Hi, the config file for GearNet-Edge-IEConv on Fold is |
Thank you. It seems that fold_mc_gearnet_edge_ieconv.pth includes the encoder and decoder parameters after finetuning. I just would like to do some experiments on my own, i.e., I would like to have the pretrained GearNet-Edge-IEConv encoder before finetuning, obtain the finetuning configuration script and corresponding running command (e.g., how many GPUs/batch size were actually used in finetuning), and do the finetuning experiment on my own. Whether it is convenient for you to provide these for me? Thank you very much. |
I see. The original pre-trained checkpoints were deleted by my cluster. I've pre-trained a new GearNet-Edge-IEConv recently. You can download the checkpoint from this link and have a try. Please ping me if there is any problem with the checkpoint. For finetuning, just use the following command python script/downstream.py -c config/downstream/Fold3D/gearnet_edge_ieconv.yaml --gpus [0] --ckpt <path_to_your_model> |
Thank you very much. I will have a try. |
Hi, it seems that the model contained in the above link is not in line with/cannot fit the model (size) in official https://zenodo.org/record/7723075 (the hidden dimensions of each layer are different), I guess the model in https://zenodo.org/record/7723075 is based on the following new implementation version of GearNet-Edge-IEConv (with extra input embedding etc). @R.register("models.GearNetIEConv")
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Thank you for your amazing work! I found that for the Fold Classification task, the GearNet-Edge model was implemented based on the GearNetIEConv script rather than the GearNet script, which has some detail differences (e.g., extra input embedding and ieconv layers). Based on this, I would like to ask whether you could provide the pretrained GearNet-Edge model based on multiview contrast learning and the GearNetIEConv script for Fold Classification (rather than based on GearNet script for EC task)? Thank you.
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