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Data Science

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  • $\begingroup$ A few questions to better understand. What kind of data is it? what exactly are the metric and the loss? are the metric levels obtained (strangely from the 1st epoch for the val set) any good? do you apply preprocessing to both datasets? how many samples do you have in both sets? $\endgroup$ Commented Mar 12 at 23:04
  • $\begingroup$ @rehaqds: 1) video data, the task of predicting a 6D object pose from frame t-1 to t; 2) the losses are both MSE, the metrics are MSE for 3D position (t_err) and geodesic distance for 3D orientation (r_err); ADD denotes a distance between a pointcloud transformed by predicted and gt poses; 3) metrics are not good; qualitatively, on the val set the model predicts 6D pose that is seemingly uncorrelated with observations (and very small); 4) the pipeline is correct, having ideal analytical predictions without the learnable model; 5) both sets are sufficiently large $\endgroup$ Commented Mar 13 at 16:27