Goodness is subjective. Reliable knowledge isn't possible with that flimsy a quality objective.
The sturdy objective criteria you gave is 95%, so it is bad by that criteria. (I'm assuming that the 95% is expected for a given data set or a randomized sample from a given data set.)
However, the 80% accuracy is good by the criteria where the you sum the measures of the accuracy of the individual models, divide by the number of models, and find you have gained ten percentage points of accuracy over that average with your aggregated execution strategy. (I'm assuming here that you used a defined set of network meta-parameters, layers depths and widths, starting parameters, activation model mapping to layers, inter-network connectivity, and loss/error methods for each model that is similar to the aggregated execution strategy.)
I have four questions. (My apologies that this question leads to the two assumptions above and a bunch more questions.)
- Is the 80% accuracy also well over the maximum of the accuracies of the set of accuracies from the individual models?
- Is the computing resource draw to achieve 80% accuracy achievable with additional run time of the best of the individual models, using less than or equal to the computing resource draw to achieve 80% that way?
- Have you run your evaluation with a full set of meta parameter vectors to check the entire meta-space for best case?
- What economic, contractual, or operational hard stop is dictating the 90%?
If we know these answers, we may be able to respond more effectively and possibly find a loop hole in the logic that appears to leave you with an undesirable foregone conclusion.