Closed shantzhou closed 2 years ago
Hi, Thanks for the interest in our work!
The major components of SeqCLR are implemented in this codebase. In particular:
- The data loader is implemented in
ImageDatasetSelfSupervised
.- The augmentation pipeline in
get_augmentation_pipeline
withaugmentation_severity=2
.- The projection module and the instance mapping function are implemented in
SeqCLRProj
.- Lastly, the loss function is implemented in
SeqCLRLoss
.The main difference of this codebase with SeqCLR is the base architecture, on which we apply SeqCLR. In the paper, we used SCATTER, and here, we employ ABI-Net. In addition, here, we offer a combined stage of SeqCLR with supervised CE (this can be removed in the config).
Thank you for your reply, I am very interested in your work, I try to use seqclr to do captcha recognition, I hope to get a powerful pre-trained model.
Sounds interesting, good luck!
Hi, Thanks for the interest in our work!
The major components of SeqCLR are implemented in this codebase. In particular:
ImageDatasetSelfSupervised
.get_augmentation_pipeline
withaugmentation_severity=2
.SeqCLRProj
.SeqCLRLoss
.The main difference of this codebase with SeqCLR is the base architecture, on which we apply SeqCLR. In the paper, we used SCATTER, and here, we employ ABI-Net. In addition, here, we offer a combined stage of SeqCLR with supervised CE (this can be removed in the config).