Evaluated chi3d dataset.
This commit is contained in:
Daniel
270
media/RESULTS.md
270
media/RESULTS.md
@ -2099,6 +2099,276 @@ Results of the model in various experiments on different datasets.
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}
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```
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### CHI3D
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```json
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{
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"avg_time_2d": 0.060682081548791185,
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"avg_time_3d": 0.00043821836772717926,
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"avg_fps": 16.36117626002912
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}
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{
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"person_nums": {
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"total_frames": 390,
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"total_labels": 780,
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"total_preds": 777,
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"considered_empty": 0,
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"valid_preds": 777,
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"invalid_preds": 0,
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"missing": 3,
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"invalid_fraction": 0.0,
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"precision": 1.0,
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"recall": 0.99615,
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"f1": 0.99807,
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"non_empty": 777
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},
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"mpjpe": {
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"count": 777,
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"mean": 0.068242,
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"median": 0.065186,
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"std": 0.013462,
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"sem": 0.000483,
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"min": 0.051948,
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"max": 0.217357,
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"recall-0.025": 0.0,
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"recall-0.05": 0.0,
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"recall-0.1": 0.976923,
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"recall-0.15": 0.987179,
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"recall-0.25": 0.996154,
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"recall-0.5": 0.996154,
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"num_labels": 780,
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"ap-0.025": 0.0,
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"ap-0.05": 0.0,
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"ap-0.1": 0.971991,
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"ap-0.15": 0.987001,
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"ap-0.25": 0.996154,
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"ap-0.5": 0.996154
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},
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"head": {
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"count": 777,
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"mean": 0.037924,
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"median": 0.025795,
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"std": 0.041221,
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"sem": 0.00148,
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"min": 0.003652,
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"max": 0.338066,
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"recall-0.025": 0.469231,
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"recall-0.05": 0.835897,
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"recall-0.1": 0.939744,
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"recall-0.15": 0.961538,
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"recall-0.25": 0.991026,
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"recall-0.5": 0.996154,
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"num_labels": 780
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},
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"shoulder_left": {
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"count": 777,
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"mean": 0.068426,
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"median": 0.068295,
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"std": 0.02725,
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"sem": 0.000978,
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"min": 0.016575,
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"max": 0.357954,
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"recall-0.025": 0.005128,
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"recall-0.05": 0.24359,
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"recall-0.1": 0.937179,
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"recall-0.15": 0.985897,
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"recall-0.25": 0.99359,
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"recall-0.5": 0.996154,
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"num_labels": 780
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},
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"shoulder_right": {
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"count": 775,
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"mean": 0.077543,
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"median": 0.074897,
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"std": 0.031788,
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"sem": 0.001143,
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"min": 0.010535,
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"max": 0.485894,
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"recall-0.025": 0.007692,
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"recall-0.05": 0.075641,
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"recall-0.1": 0.898718,
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"recall-0.15": 0.982051,
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"recall-0.25": 0.987179,
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"recall-0.5": 0.99359,
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"num_labels": 780
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},
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"elbow_left": {
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"count": 776,
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"mean": 0.060755,
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"median": 0.05218,
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"std": 0.033518,
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"sem": 0.001204,
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"min": 0.016563,
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"max": 0.417809,
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"recall-0.025": 0.002564,
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"recall-0.05": 0.432051,
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"recall-0.1": 0.932051,
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"recall-0.15": 0.966667,
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"recall-0.25": 0.989744,
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"recall-0.5": 0.994872,
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"num_labels": 780
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},
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"elbow_right": {
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"count": 777,
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"mean": 0.066781,
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"median": 0.065762,
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"std": 0.020878,
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"sem": 0.000749,
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"min": 0.027359,
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"max": 0.224832,
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"recall-0.025": 0.0,
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"recall-0.05": 0.194872,
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"recall-0.1": 0.941026,
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"recall-0.15": 0.983333,
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"recall-0.25": 0.996154,
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"recall-0.5": 0.996154,
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"num_labels": 780
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},
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"wrist_left": {
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"count": 774,
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"mean": 0.049563,
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"median": 0.042911,
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"std": 0.042084,
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"sem": 0.001514,
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"min": 0.006736,
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"max": 0.497826,
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"recall-0.025": 0.171795,
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"recall-0.05": 0.679487,
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"recall-0.1": 0.939744,
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"recall-0.15": 0.967949,
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"recall-0.25": 0.982051,
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"recall-0.5": 0.992308,
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"num_labels": 780
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},
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"wrist_right": {
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"count": 776,
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"mean": 0.064678,
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"median": 0.062017,
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"std": 0.041104,
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"sem": 0.001477,
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"min": 0.008905,
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"max": 0.430827,
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"recall-0.025": 0.042308,
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"recall-0.05": 0.369231,
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"recall-0.1": 0.928205,
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"recall-0.15": 0.952564,
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"recall-0.25": 0.985897,
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"recall-0.5": 0.994872,
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"num_labels": 780
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},
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"hip_left": {
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"count": 777,
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"mean": 0.109321,
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"median": 0.109336,
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"std": 0.017307,
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"sem": 0.000621,
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"min": 0.065587,
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"max": 0.157263,
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"recall-0.025": 0.0,
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"recall-0.05": 0.0,
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"recall-0.1": 0.325641,
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"recall-0.15": 0.987179,
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"recall-0.25": 0.996154,
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"recall-0.5": 0.996154,
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"num_labels": 780
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},
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"hip_right": {
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"count": 777,
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"mean": 0.134702,
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"median": 0.134688,
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"std": 0.014986,
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"sem": 0.000538,
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"min": 0.086478,
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"max": 0.195378,
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"recall-0.025": 0.0,
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"recall-0.05": 0.0,
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"recall-0.1": 0.010256,
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"recall-0.15": 0.858974,
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"recall-0.25": 0.996154,
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"recall-0.5": 0.996154,
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"num_labels": 780
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},
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"knee_left": {
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"count": 777,
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"mean": 0.050968,
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"median": 0.049197,
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"std": 0.018325,
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"sem": 0.000658,
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"min": 0.007513,
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"max": 0.198729,
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"recall-0.025": 0.026923,
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"recall-0.05": 0.535897,
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"recall-0.1": 0.957692,
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"recall-0.15": 0.99359,
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"recall-0.25": 0.996154,
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"recall-0.5": 0.996154,
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"num_labels": 780
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},
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"knee_right": {
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"count": 777,
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"mean": 0.056941,
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"median": 0.054107,
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"std": 0.016649,
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"sem": 0.000598,
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"min": 0.018899,
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"max": 0.125673,
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"recall-0.025": 0.002564,
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"recall-0.05": 0.396154,
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"recall-0.1": 0.984615,
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"recall-0.15": 0.996154,
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"recall-0.25": 0.996154,
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"recall-0.5": 0.996154,
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"num_labels": 780
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},
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"ankle_left": {
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"count": 777,
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"mean": 0.061821,
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"median": 0.059825,
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"std": 0.017211,
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"sem": 0.000618,
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"min": 0.031091,
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"max": 0.319784,
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"recall-0.025": 0.0,
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"recall-0.05": 0.144872,
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"recall-0.1": 0.973077,
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"recall-0.15": 0.991026,
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"recall-0.25": 0.994872,
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"recall-0.5": 0.996154,
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"num_labels": 780
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},
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"ankle_right": {
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"count": 777,
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"mean": 0.04225,
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"median": 0.03841,
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"std": 0.014422,
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"sem": 0.000518,
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"min": 0.015158,
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"max": 0.170293,
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"recall-0.025": 0.033333,
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"recall-0.05": 0.8,
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"recall-0.1": 0.992308,
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"recall-0.15": 0.994872,
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"recall-0.25": 0.996154,
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"recall-0.5": 0.996154,
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"num_labels": 780
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},
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"joint_recalls": {
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"num_labels": 10140,
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"recall-0.025": 0.05799,
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"recall-0.05": 0.36164,
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"recall-0.1": 0.82702,
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"recall-0.15": 0.97022,
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"recall-0.25": 0.99211,
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"recall-0.5": 0.99546
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}
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}
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{
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"total_parts": 10920,
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"correct_parts": 10328,
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"pcp": 0.945788
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}
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```
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### Tsinghua
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(duration 00:01:51)
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@ -23,6 +23,7 @@ dataset_use = "human36m"
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# dataset_use = "shelf"
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# dataset_use = "campus"
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# dataset_use = "ikeaasm"
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# dataset_use = "chi3d"
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# dataset_use = "tsinghua"
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# dataset_use = "human36m_wb"
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# dataset_use = "egohumans"
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@ -44,10 +45,6 @@ datasets = {
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"take_interval": 1,
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"with_depth": False,
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},
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"ikeaasm": {
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"path": "/datasets/ikeaasm/skelda/test.json",
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"take_interval": 2,
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},
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"campus": {
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"path": "/datasets/campus/skelda/test.json",
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"take_interval": 1,
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@ -56,6 +53,14 @@ datasets = {
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"path": "/datasets/shelf/skelda/test.json",
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"take_interval": 1,
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},
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"ikeaasm": {
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"path": "/datasets/ikeaasm/skelda/test.json",
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"take_interval": 2,
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},
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"chi3d": {
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"path": "/datasets/chi3d/skelda/all.json",
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"take_interval": 5,
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},
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"tsinghua": {
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"path": "/datasets/tsinghua/skelda/test.json",
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"take_interval": 3,
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@ -178,6 +183,11 @@ def load_labels(dataset: dict):
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for label in labels:
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label["bodyids"] = list(range(len(label["bodies3D"])))
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elif "chi3d" in dataset:
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labels = load_json(dataset["chi3d"]["path"])
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labels = [lb for lb in labels if lb["setup"] == "s03"]
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labels = [lb for i, lb in enumerate(labels) if i % 2000 < 150]
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elif "human36m_wb" in dataset:
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labels = load_json(dataset["human36m_wb"]["path"])
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@ -238,10 +248,10 @@ def main():
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"campus": 0.96,
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"shelf": 0.96,
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"ikeaasm": 0.89,
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"chi3d": 0.94,
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"tsinghua": 0.96,
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"egohumans": 0.95,
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"human36m_wb": 0.94,
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"koarob": 0.91,
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}
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minscore = minscores.get(dataset_use, 0.95)
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min_group_sizes = {
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@ -249,6 +259,7 @@ def main():
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# If the number of cameras is high, and the views are not occluded, use a higher value
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"panoptic": 1,
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"shelf": 2,
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"chi3d": 1,
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"tsinghua": 2,
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"egohumans": 4,
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}
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2
skelda
2
skelda
Submodule skelda updated: 0127e9de87...3c741392c2
Reference in New Issue
Block a user