Closed andrefdre closed 1 year ago
As I previously suggested, I think that one run for each case and network is sufficient. The tables show that there are no significant differences between runs.
Right. Let's do 1 run per approach. Later if we have more gpus we can go back to this ...
@DanielCoelho112 , can't we give some priority to @andrefdre since he's close to finishing the thesis?
@DanielCoelho112 , can't we give some priority to @andrefdre since he's close to finishing the thesis?
Yes, we can. I'll liberate one GPU by the end of the day. Still, I think one run is enough. All papers in localization (and in Deep Learning in general) are only trained once. Running 5 runs, would take around 50 experiments, which would take too much time.
Right. One is enough. Just i case he needs it for some other test...
On Thu, May 4, 2023, 6:02 PM Daniel Coelho @.***> wrote:
@DanielCoelho112 https://github.com/DanielCoelho112 , can't we give some priority to @andrefdre https://github.com/andrefdre since he's close to finishing the thesis?
Yes, we can. I'll liberate one GPU by the end of the day. Still, I think one run is enough. All papers in localization (and in Deep Learning in general) are only trained once. Running 5 runs, would take around 50 experiments, which would take too much time.
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Thanks, I will then train only one model, with 2 GPU I will end the trains faster.
Due to time limitations, it's not possible to train 5 models for each case and network. Currently, I only have available 1 GPU in deeplar. What should I do, @miguelriemoliveira?