Suggestion: merge dataset in `val` and `train` folder to train for final epochs

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Description

Hello,

Practically, after completion of a normal training pipeline until the last epoch or early-stop condition, we will:

1. Check train\loss and val metrics to confirm if the training already hit its limits and satisfy our requirement
2. Manually copy dataset in folder .\dataset_dir\val into folder .\dataset_dir\train; and create new _annotations.coco.json file to merge them all. There is no change to val dataset, but only train dataset. Purpose: utilize all labeled dataset for training before ending and exporting the final best checkpoint.
3. Extend the training argument epochs 10-20 extra epochs and resume on training from last.ckpt
4. Finish and use the best checkpoint for prediction or ONNX conversion

Suggestion: Simplify step 2 by establishing a new training argument, for example called train_all_dataset:bool to let the program either automatically include the labeled dataset in .\dataset_dir\val into training (if set True) or train on .\dataset_dir\train dataset only (if set False)

Use case

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Additional

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Are you willing to submit a PR?

• Yes I'd like to help by submitting a PR!