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@@ -132,19 +132,18 @@ A setting to unleash all its potential is (MedSegDiff++):
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--image_size 256 --num_channels 512 --class_cond False --num_res_blocks 12 --num_heads 8 --learn_sigma True --use_scale_shift_norm True --attention_resolutions 24
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Then train it with batch size ``--batch_size 64`` and sample it with ensemble number ``--num_ensemble 25``.
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## Be a part of MedSegDiff ! Authors are YOU !
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Welcome to contribute to MedSegDiff. Any technique can improve the performance or speed up the algorithm is appreciated🙏. I am writting MedSegDiff V2, aiming at Nature journals/CVPR like publication. I'm glad to list the contributors as my co-authors🤗.
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## TODO LIST
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- [x] Fix bugs in BRATS. Add BRATS example.
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- [ ] Release REFUGE and DDIT dataloaders and examples
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- [x] Release REFUGE and DDIT dataloaders and examples
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- [x] Speed up sampling by DPM-solver
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- [ ] Inference of depth
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- [x] Fix bugs in Multi-GPU parallel
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- [ ] Sample and Vis in training
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- [ ] Release pre processing and post processing
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- [x] Sample and Vis in training
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- [x] Release pre processing and post processing
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- [x] Release evaluation
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- [ ] Deploy on HuggingFace
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- [x] Deploy on HuggingFace
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- [x] configuration
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## Thanks

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