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I was running the network with all defeault settings on the GC dataset till convergence (up to about 1000 epochs) several times with different random seeds and never got the reported error of 0.0125.
The best I was actually able to achieve was 0.0138 by doing some small changes.
However, by using only a simple LSTM (no social component) I already got an error of 0.0136.
Is there some bug in the repository?
Could you upload working network weights?
Edit: By using 3 layers in the regression net I got 0.0125 with a simple LSTM, so exactly what the paper reports.
The text was updated successfully, but these errors were encountered:
I changed the source code to accept variable number of pedestrians in each main_compute_step().
Then I trained the network on ETH dataset.
The results are far from what is reported (ade_err > 1.0 for 8 prediction step on both ETH & Hotel)
Is there something wrong?
@Binbose Hi, I also tried to reproduce the results produced in the paper but find it difficult. What changes have you made to get a lower MSE error? Actually, the code is not so inconsistent with the paper, for example, the input into the motion module is locations but not the displacements.
I was running the network with all defeault settings on the GC dataset till convergence (up to about 1000 epochs) several times with different random seeds and never got the reported error of 0.0125.
The best I was actually able to achieve was 0.0138 by doing some small changes.
However, by using only a simple LSTM (no social component) I already got an error of 0.0136.
Is there some bug in the repository?
Could you upload working network weights?
Edit: By using 3 layers in the regression net I got 0.0125 with a simple LSTM, so exactly what the paper reports.
The text was updated successfully, but these errors were encountered: