My approach is to take the SDD data set according to the ETH-UCY processing method, take data every 10 frames, use the first 8 coordinates of pedestrians to predict the coordinates of the next 12 points, and test it on the O-S-TT model. The results are as follows :
################## BEST PERFORMANCE 0.22 ######## Saved model to: ../content/trained.pt Epoch: 1 Train Loss 10908859.606394859 RCL 4337671.330541314 KLD 3969445.6071976754 ADL 2601742.6686558584 Test ADE 0.21815821937219357 Test Average FDE (Across all samples) 1.1173672070395309 Test Min FDE 0.4298632619006239 Test Best ADE Loss So Far (N = 20) 0.21815821937219357 Test Best Min FDE (N = 20) 0.4298632619006239
Obviously not in the same order of magnitude as the results of the paper。
I would like to ask whether there is a problem with my data set division (using the data set division method in ynet), or a parameter setting problem, or my data set processing idea is wrong.
My approach is to take the SDD data set according to the ETH-UCY processing method, take data every 10 frames, use the first 8 coordinates of pedestrians to predict the coordinates of the next 12 points, and test it on the O-S-TT model. The results are as follows :
################## BEST PERFORMANCE 0.22 ######## Saved model to: ../content/trained.pt Epoch: 1 Train Loss 10908859.606394859 RCL 4337671.330541314 KLD 3969445.6071976754 ADL 2601742.6686558584 Test ADE 0.21815821937219357 Test Average FDE (Across all samples) 1.1173672070395309 Test Min FDE 0.4298632619006239 Test Best ADE Loss So Far (N = 20) 0.21815821937219357 Test Best Min FDE (N = 20) 0.4298632619006239Obviously not in the same order of magnitude as the results of the paper。
I would like to ask whether there is a problem with my data set division (using the data set division method in ynet), or a parameter setting problem, or my data set processing idea is wrong.