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List in General:
**Can Be Done Without Training Images**
1. Gamma Correction: Adjust image brightness.
2. Filtering: Apply Gaussian blur, median filter, etc.
3. Color Space Transformation: Conve…
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Hi,
is it possible to also upload the training scripts and resulting network weights for the multimodal configuration? (Training on both Optical and Radar data with RandomSensorDrop)
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Hi mmdet maintainers,
Thanks for the repo. We are the project lead of [Kornia](https://github.com/kornia/kornia), and thinking of the possibilities of integrating Kornia augmentation pipeline into …
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I had a quick q. We are using 3D MRI data sets for segmentation (multi label). Some of the training data are acquired in sagittal and some in axial. So if you look at the x y z information, it is obvi…
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Sincerely thanks for your contribution to this work, Im now following your brilliant work with appreciation. But recently, when I browsed your paper and given code, I found an inconsistent side betwee…
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- coco.yaml is missing
- I just run the code for coco dataset. it seems the time window is always 1. According to your mem_update logic in this case it doesn't experience any voltage membrane decay …
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Sometimes URLs are written in text rather than hidden behind the HTML element. The URL should be copied as is in this case.
There are two ways to fix this:
1. Maybe an easier way: identify a URL w…
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`torchvision` defines [Transforms](https://pytorch.org/docs/stable/torchvision/transforms.html) objects to apply data augmentation and other transformations to data.
We could and should define our …
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Data augmentation is a technique to increase the amount and diversity of data without actually collecting new data. This comes in handy when labelled data are not easy to come by, and can help increas…
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## More Than Augmentation
In a longer vision, our augmentation module will be more focused on differentiability, with an aim of assisting both training and deployment. We can see there are more resea…