I have reproduced the plausible outcomes of generation on the Pascal dataset. Can we generate/augment medical images that distinguish a lot from common/natural images? I have fine-tuned DA-Fusion with the customized medical dataset; notwithstanding, over-fancy results are produced by executing generating_images.py, which is impossible for augmentation. Specifically, I built the dataset as a subclass of semantic_aug/few_shot_dataset.py and followed the identical configurations in the official codes. I also noted that the results of different .bin files from customized-x-y vary; what does x and y mean? Similarly, I attempted to augment medical images with generate_augmentations.py, setting --embed-path with learned_embeds.bin derived from fine-tuning operations mentioned above. I feel it is an inappropriate trial because of the default setting of ***.pt, which I have no idea to obtain thus far. In short, I have a customized medical dataset and corresponding labels and intend to achieve favorable augmentations with DA-Fusion thanks to its image-image generation.
Hello @brandontrabucco ,
Thanks for your reputed work.
I have reproduced the plausible outcomes of generation on the Pascal dataset. Can we generate/augment medical images that distinguish a lot from common/natural images? I have fine-tuned DA-Fusion with the customized medical dataset; notwithstanding, over-fancy results are produced by executing
generating_images.py
, which is impossible for augmentation. Specifically, I built the dataset as a subclass ofsemantic_aug/few_shot_dataset.py
and followed the identical configurations in the official codes. I also noted that the results of different.bin
files fromcustomized-x-y
vary; what doesx
andy
mean? Similarly, I attempted to augment medical images withgenerate_augmentations.py
, setting--embed-path
withlearned_embeds.bin
derived from fine-tuning operations mentioned above. I feel it is an inappropriate trial because of the default setting of***.pt
, which I have no idea to obtain thus far. In short, I have a customized medical dataset and corresponding labels and intend to achieve favorable augmentations with DA-Fusion thanks to its image-image generation.I would appreciate any guidance or suggestions.
Best, Young