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[Feature] Reduce overfitting when training #354

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adamltyson opened this issue Feb 27, 2020 · 1 comment
Open

[Feature] Reduce overfitting when training #354

adamltyson opened this issue Feb 27, 2020 · 1 comment
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enhancement New feature or request

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@adamltyson
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Users are generating small amounts of data, and the network seems to be over fitting. They're having to manually get existing data to constrain their models. This could be improved by:

  • Providing old training data via GIN
  • Allowing upper layers to be frozen

Interested to know what aspects of training have helped @FedeClaudi @larsrollik

@adamltyson adamltyson added the enhancement New feature or request label Feb 27, 2020
@adamltyson adamltyson self-assigned this Feb 27, 2020
@FedeClaudi
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I'm trying training with my data + a large set of training examples from the initial cellfinder network, will let you know if it helps.

Meanwhile I will also try to create a larger and more balanced training set for my data, in my case, let's see if it helps.

@adamltyson adamltyson removed their assignment Feb 14, 2022
willGraham01 referenced this issue Sep 1, 2023
* include missing YAML files in sdist

* Expand the `pre-commit` checks to other useful options

* Add separate linting CI

* Run `pre-commit` and fix besides `mypy`

* Move badges (#61)

Co-authored-by: alessandrofelder <a.felder@ucl.ac.uk>
Co-authored-by: David Pérez-Suárez <dps.helio@gmail.com>
willGraham01 referenced this issue Sep 1, 2023
Add cellfinder_core specific logger
@willGraham01 willGraham01 transferred this issue from brainglobe/cellfinder Dec 13, 2023
@adamltyson adamltyson transferred this issue from brainglobe/brainglobe-workflows Jan 4, 2024
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enhancement New feature or request
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