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feat(train): auto-log 5 annotated training samples at train start for annotation verification #1203
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Thanks for the contribution @Amitjoiya. Before we can consider the contents of the PR we need you to submit a clean PR that only includes the relevant changes, not large numbers of formatting changes across dozens of files. Once you've updated the PR accordingly let us know and we'll come back and consider the contribution. |
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@ethanwhite, Thanks for the review! I’ve cleaned up the PR to include only the relevant changes for the auto-logging feature. |
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I don't see any changes. Did you forget to push your updates? |
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@ethanwhite, Please see my PR description now that's clean and meaningful. Thanks ! |
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Sorry for the confusion. The main issue is with the code changes themselves. If you look at https://github.com/weecology/DeepForest/pull/1203/files you'll see that 29 files have been changed and almost all of the changes are formatting changes. That makes it too time consuming to read the code changes, so each PR should only include the changes to the code necessary to implement the specific thing you're working on. Does that make sense? |
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Hey @ethanwhite, Please review, Thanks ! |
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If you look at the Files Changed tab you'll see that you have still changed the formatting on (now) 31 files. I'm guessing you have some sort of automated system that is running Ruff on the entire code base (including files that are ignored by our build system). You need to turn it off so that you can submit a clean PR. |
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@ethanwhite, Please Review, Thanks ! |
henrykironde
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Thank you, @Amitjoiya, for the contribution. Could you please add a screenshot showing the outcome? We’ll also need tests covering the newly added logic. I haven’t reviewed the code in depth yet, but at a glance it feels more complex than necessary. It would be good to explore simpler ways to achieve the same result, refine the implementation for clarity and only comment where necessary.
Overview
Added automatic logging of a few annotated training images before training starts.
This helps verify image–annotation alignment and improves user debugging experience.
Implementation
on_train_start()incallbacks.pyvisualize.plot_annotationsTesting