Merging Training and Validation Sets for better accuracy I am training an Image Classification Model and my train-test set distribution is 80-20.

After Training my train-test loss curve looks like this


As the model is converged after around 20-30 epochs and is not overfitting.
Can I also try to train the same model by merging my training and testing set hoping it would become more accurate with increased size of training data ?

Is it a good idea to merge train, test set in this case ?
What are the disadvantages if I do so ?
After you have finished with the model building process (in which it is assumed that you have used your test set once and only once for assessing the performance of your final model on unseen data), and before deploying your model, both common sense and standard practice say that you should re-train it on all the available data, including the portion that, until then, had been put aside as test. Leaving out available data is a luxury which normally we cannot afford; and, provided that there are no issues with your model building process, and your test set is qualitatively similar to your training one (an assumption implicitly always present), there is nothing to worry about.
Qualitatively speaking, this approach is similar with what we do with cross validation, where afterwards we routinely re-train the model using all the available data.
The following Cross Validated threads might be useful; although they address the cross-validation issue, the rationale is similar -  at the end, use all the data:

Training on the full dataset after cross-validation?
How to choose a predictive model after k-fold cross-validation?