diff --git a/media/RESULTS.md b/media/RESULTS.md
index 92b120f..90c510f 100644
--- a/media/RESULTS.md
+++ b/media/RESULTS.md
@@ -7,9 +7,9 @@ Results of the model in various experiments on different datasets.
 (duration 00:01:20)
 ```json
 {
-  "avg_time_2d": 0.07035206899804584,
-  "avg_time_3d": 0.003138972945132498,
-  "avg_fps": 13.607100587486267
+  "avg_time_2d": 0.07016186108023434,
+  "avg_time_3d": 0.001361288458614026,
+  "avg_fps": 13.981487203060627
 }
 {
   "person_nums": {
@@ -28,9 +28,9 @@ Results of the model in various experiments on different datasets.
   },
   "mpjpe": {
     "count": 600,
-    "mean": 0.066893,
-    "median": 0.059279,
-    "std": 0.027761,
+    "mean": 0.066851,
+    "median": 0.059172,
+    "std": 0.027766,
     "sem": 0.001134,
     "min": 0.043707,
     "max": 0.189867,
@@ -43,23 +43,23 @@ Results of the model in various experiments on different datasets.
     "num_labels": 600,
     "ap-0.025": 0.0,
     "ap-0.05": 0.002244,
-    "ap-0.1": 0.895111,
-    "ap-0.15": 0.917246,
+    "ap-0.1": 0.895131,
+    "ap-0.15": 0.917269,
     "ap-0.25": 1.0,
     "ap-0.5": 1.0
   },
   "nose": {
     "count": 600,
-    "mean": 0.117674,
+    "mean": 0.11743,
     "median": 0.101455,
-    "std": 0.042793,
-    "sem": 0.001748,
-    "min": 0.046466,
+    "std": 0.042492,
+    "sem": 0.001736,
+    "min": 0.046467,
     "max": 0.271134,
     "recall-0.025": 0.0,
     "recall-0.05": 0.003333,
     "recall-0.1": 0.486667,
-    "recall-0.15": 0.803333,
+    "recall-0.15": 0.806667,
     "recall-0.25": 0.995,
     "recall-0.5": 1.0,
     "num_labels": 600
@@ -82,10 +82,10 @@ Results of the model in various experiments on different datasets.
   },
   "shoulder_right": {
     "count": 600,
-    "mean": 0.048174,
+    "mean": 0.048134,
     "median": 0.033305,
-    "std": 0.042712,
-    "sem": 0.001745,
+    "std": 0.04268,
+    "sem": 0.001744,
     "min": 0.003882,
     "max": 0.247363,
     "recall-0.025": 0.238333,
@@ -130,10 +130,10 @@ Results of the model in various experiments on different datasets.
   },
   "wrist_left": {
     "count": 600,
-    "mean": 0.040627,
+    "mean": 0.040641,
     "median": 0.024203,
-    "std": 0.042377,
-    "sem": 0.001731,
+    "std": 0.042385,
+    "sem": 0.001732,
     "min": 0.001517,
     "max": 0.18543,
     "recall-0.025": 0.516667,
@@ -146,14 +146,14 @@ Results of the model in various experiments on different datasets.
   },
   "wrist_right": {
     "count": 600,
-    "mean": 0.042646,
+    "mean": 0.042497,
     "median": 0.025673,
-    "std": 0.046896,
-    "sem": 0.001916,
+    "std": 0.046851,
+    "sem": 0.001914,
     "min": 0.001698,
     "max": 0.425617,
     "recall-0.025": 0.481667,
-    "recall-0.05": 0.771667,
+    "recall-0.05": 0.775,
     "recall-0.1": 0.896667,
     "recall-0.15": 0.921667,
     "recall-0.25": 0.998333,
@@ -178,15 +178,15 @@ Results of the model in various experiments on different datasets.
   },
   "hip_right": {
     "count": 600,
-    "mean": 0.114501,
+    "mean": 0.114452,
     "median": 0.114981,
-    "std": 0.026109,
-    "sem": 0.001067,
+    "std": 0.026143,
+    "sem": 0.001068,
     "min": 0.046173,
     "max": 0.234935,
     "recall-0.025": 0.0,
     "recall-0.05": 0.001667,
-    "recall-0.1": 0.248333,
+    "recall-0.1": 0.25,
     "recall-0.15": 0.948333,
     "recall-0.25": 1.0,
     "recall-0.5": 1.0,
@@ -242,16 +242,16 @@ Results of the model in various experiments on different datasets.
   },
   "ankle_right": {
     "count": 600,
-    "mean": 0.085539,
+    "mean": 0.08545,
     "median": 0.068594,
-    "std": 0.058554,
-    "sem": 0.002392,
+    "std": 0.058449,
+    "sem": 0.002388,
     "min": 0.032987,
     "max": 0.463238,
     "recall-0.025": 0.0,
     "recall-0.05": 0.015,
     "recall-0.1": 0.885,
-    "recall-0.15": 0.906667,
+    "recall-0.15": 0.908333,
     "recall-0.25": 0.978333,
     "recall-0.5": 1.0,
     "num_labels": 600
@@ -259,17 +259,17 @@ Results of the model in various experiments on different datasets.
   "joint_recalls": {
     "num_labels": 7800,
     "recall-0.025": 0.17949,
-    "recall-0.05": 0.47564,
-    "recall-0.1": 0.82154,
-    "recall-0.15": 0.92923,
+    "recall-0.05": 0.4759,
+    "recall-0.1": 0.82179,
+    "recall-0.15": 0.92962,
     "recall-0.25": 0.99397,
     "recall-0.5": 0.99974
   }
 }
 {
   "total_parts": 8400,
-  "correct_parts": 8091,
-  "pcp": 0.963214
+  "correct_parts": 8092,
+  "pcp": 0.963333
 }
 ```
 
@@ -278,9 +278,9 @@ Results of the model in various experiments on different datasets.
 (duration 00:00:56)
 ```json
 {
-  "avg_time_2d": 0.10030525082984741,
-  "avg_time_3d": 0.00994173521848069,
-  "avg_fps": 9.07054274990917
+  "avg_time_2d": 0.10439699048438843,
+  "avg_time_3d": 0.003671272513792687,
+  "avg_fps": 9.253410504218346
 }
 {
   "person_nums": {
@@ -299,31 +299,31 @@ Results of the model in various experiments on different datasets.
   },
   "mpjpe": {
     "count": 477,
-    "mean": 0.048235,
+    "mean": 0.04824,
     "median": 0.04301,
-    "std": 0.014642,
+    "std": 0.014639,
     "sem": 0.000671,
     "min": 0.029586,
-    "max": 0.106122,
+    "max": 0.106389,
     "recall-0.025": 0.0,
-    "recall-0.05": 0.675052,
+    "recall-0.05": 0.677149,
     "recall-0.1": 0.991614,
     "recall-0.15": 1.0,
     "recall-0.25": 1.0,
     "recall-0.5": 1.0,
     "num_labels": 477,
     "ap-0.025": 0.0,
-    "ap-0.05": 0.340912,
-    "ap-0.1": 0.6919,
-    "ap-0.15": 0.701795,
-    "ap-0.25": 0.701795,
-    "ap-0.5": 0.701795
+    "ap-0.05": 0.342452,
+    "ap-0.1": 0.692315,
+    "ap-0.15": 0.702141,
+    "ap-0.25": 0.702141,
+    "ap-0.5": 0.702141
   },
   "head": {
     "count": 477,
-    "mean": 0.055384,
+    "mean": 0.055391,
     "median": 0.049788,
-    "std": 0.025996,
+    "std": 0.026004,
     "sem": 0.001192,
     "min": 0.006374,
     "max": 0.147303,
@@ -353,9 +353,9 @@ Results of the model in various experiments on different datasets.
   },
   "shoulder_right": {
     "count": 477,
-    "mean": 0.050381,
+    "mean": 0.050386,
     "median": 0.045185,
-    "std": 0.024432,
+    "std": 0.024435,
     "sem": 0.00112,
     "min": 0.00639,
     "max": 0.15065,
@@ -369,10 +369,10 @@ Results of the model in various experiments on different datasets.
   },
   "elbow_left": {
     "count": 477,
-    "mean": 0.040845,
+    "mean": 0.040857,
     "median": 0.034194,
-    "std": 0.025928,
-    "sem": 0.001188,
+    "std": 0.025942,
+    "sem": 0.001189,
     "min": 0.005533,
     "max": 0.152226,
     "recall-0.025": 0.301887,
@@ -385,15 +385,15 @@ Results of the model in various experiments on different datasets.
   },
   "elbow_right": {
     "count": 477,
-    "mean": 0.053498,
+    "mean": 0.053459,
     "median": 0.043749,
-    "std": 0.042037,
-    "sem": 0.001927,
+    "std": 0.041996,
+    "sem": 0.001925,
     "min": 0.002858,
     "max": 0.244706,
     "recall-0.025": 0.259958,
     "recall-0.05": 0.561845,
-    "recall-0.1": 0.888889,
+    "recall-0.1": 0.890985,
     "recall-0.15": 0.958071,
     "recall-0.25": 1.0,
     "recall-0.5": 1.0,
@@ -401,15 +401,15 @@ Results of the model in various experiments on different datasets.
   },
   "wrist_left": {
     "count": 477,
-    "mean": 0.060633,
-    "median": 0.053257,
-    "std": 0.043196,
-    "sem": 0.00198,
+    "mean": 0.060765,
+    "median": 0.053276,
+    "std": 0.043274,
+    "sem": 0.001983,
     "min": 0.003745,
     "max": 0.359369,
     "recall-0.025": 0.12369,
-    "recall-0.05": 0.415094,
-    "recall-0.1": 0.916143,
+    "recall-0.05": 0.412998,
+    "recall-0.1": 0.914046,
     "recall-0.15": 0.962264,
     "recall-0.25": 0.985325,
     "recall-0.5": 1.0,
@@ -417,9 +417,9 @@ Results of the model in various experiments on different datasets.
   },
   "wrist_right": {
     "count": 477,
-    "mean": 0.057929,
+    "mean": 0.057975,
     "median": 0.053722,
-    "std": 0.029957,
+    "std": 0.02995,
     "sem": 0.001373,
     "min": 0.009013,
     "max": 0.202256,
@@ -433,15 +433,15 @@ Results of the model in various experiments on different datasets.
   },
   "hip_left": {
     "count": 477,
-    "mean": 0.048001,
+    "mean": 0.0479,
     "median": 0.042849,
-    "std": 0.026773,
-    "sem": 0.001227,
+    "std": 0.026619,
+    "sem": 0.00122,
     "min": 0.005757,
     "max": 0.140667,
     "recall-0.025": 0.213836,
-    "recall-0.05": 0.618449,
-    "recall-0.1": 0.949686,
+    "recall-0.05": 0.620545,
+    "recall-0.1": 0.951782,
     "recall-0.15": 1.0,
     "recall-0.25": 1.0,
     "recall-0.5": 1.0,
@@ -449,14 +449,14 @@ Results of the model in various experiments on different datasets.
   },
   "hip_right": {
     "count": 477,
-    "mean": 0.057906,
-    "median": 0.056146,
-    "std": 0.025472,
+    "mean": 0.057871,
+    "median": 0.056052,
+    "std": 0.025477,
     "sem": 0.001168,
     "min": 0.005189,
     "max": 0.136207,
     "recall-0.025": 0.109015,
-    "recall-0.05": 0.419287,
+    "recall-0.05": 0.421384,
     "recall-0.1": 0.930818,
     "recall-0.15": 1.0,
     "recall-0.25": 1.0,
@@ -465,13 +465,13 @@ Results of the model in various experiments on different datasets.
   },
   "knee_left": {
     "count": 477,
-    "mean": 0.039986,
-    "median": 0.037315,
-    "std": 0.02438,
-    "sem": 0.001117,
+    "mean": 0.040016,
+    "median": 0.03745,
+    "std": 0.024403,
+    "sem": 0.001118,
     "min": 0.005407,
     "max": 0.195502,
-    "recall-0.025": 0.259958,
+    "recall-0.025": 0.262055,
     "recall-0.05": 0.763103,
     "recall-0.1": 0.974843,
     "recall-0.15": 0.989518,
@@ -513,10 +513,10 @@ Results of the model in various experiments on different datasets.
   },
   "ankle_right": {
     "count": 477,
-    "mean": 0.041445,
+    "mean": 0.041454,
     "median": 0.031787,
-    "std": 0.038031,
-    "sem": 0.001743,
+    "std": 0.038012,
+    "sem": 0.001742,
     "min": 0.005439,
     "max": 0.298397,
     "recall-0.025": 0.312369,
@@ -529,9 +529,9 @@ Results of the model in various experiments on different datasets.
   },
   "joint_recalls": {
     "num_labels": 6201,
-    "recall-0.025": 0.21045,
-    "recall-0.05": 0.62006,
-    "recall-0.1": 0.93824,
+    "recall-0.025": 0.21077,
+    "recall-0.05": 0.62038,
+    "recall-0.1": 0.93807,
     "recall-0.15": 0.98742,
     "recall-0.25": 0.99871,
     "recall-0.5": 1.0
@@ -549,16 +549,16 @@ Results of the model in various experiments on different datasets.
 (duration 00:00:29)
 ```json
 {
-  "avg_time_2d": 0.056824864081616674,
-  "avg_time_3d": 0.0035331103036988455,
-  "avg_fps": 16.56781908577916
+  "avg_time_2d": 0.05871032543902127,
+  "avg_time_3d": 0.0016240331361878594,
+  "avg_fps": 16.574303988886548
 }
 {
   "person_nums": {
     "total_frames": 222,
     "total_labels": 376,
-    "total_preds": 466,
-    "considered_empty": 3,
+    "total_preds": 464,
+    "considered_empty": 1,
     "valid_preds": 376,
     "invalid_preds": 87,
     "missing": 0,
@@ -820,189 +820,189 @@ Results of the model in various experiments on different datasets.
 (duration 00:02:31)
 ```json
 {
-  "avg_time_2d": 0.054314954600542156,
-  "avg_time_3d": 0.0029571287466366573,
-  "avg_fps": 17.460513771396784
+  "avg_time_2d": 0.05640246679401552,
+  "avg_time_3d": 0.0013555948876795361,
+  "avg_fps": 17.31360040284947
 }
 {
   "person_nums": {
     "total_frames": 629,
     "total_labels": 1061,
-    "total_preds": 1049,
-    "considered_empty": 119,
-    "valid_preds": 711,
+    "total_preds": 1047,
+    "considered_empty": 118,
+    "valid_preds": 710,
     "invalid_preds": 219,
-    "missing": 350,
-    "invalid_fraction": 0.23548,
-    "precision": 0.76452,
-    "recall": 0.67012,
-    "f1": 0.71421,
-    "non_empty": 930
+    "missing": 351,
+    "invalid_fraction": 0.23574,
+    "precision": 0.76426,
+    "recall": 0.66918,
+    "f1": 0.71357,
+    "non_empty": 929
   },
   "mpjpe": {
-    "count": 711,
-    "mean": 0.113411,
-    "median": 0.094476,
-    "std": 0.065788,
-    "sem": 0.002469,
+    "count": 710,
+    "mean": 0.11365,
+    "median": 0.094542,
+    "std": 0.066461,
+    "sem": 0.002496,
     "min": 0.040489,
     "max": 0.497874,
     "recall-0.025": 0.0,
     "recall-0.05": 0.006598,
-    "recall-0.1": 0.367578,
+    "recall-0.1": 0.366635,
     "recall-0.15": 0.570217,
-    "recall-0.25": 0.645617,
-    "recall-0.5": 0.670123,
+    "recall-0.25": 0.64279,
+    "recall-0.5": 0.66918,
     "num_labels": 1061,
     "ap-0.025": 0.0,
     "ap-0.05": 0.000155,
-    "ap-0.1": 0.23825,
-    "ap-0.15": 0.474984,
-    "ap-0.25": 0.574206,
-    "ap-0.5": 0.603097
+    "ap-0.1": 0.237214,
+    "ap-0.15": 0.475129,
+    "ap-0.25": 0.571172,
+    "ap-0.5": 0.602435
   },
   "head": {
-    "count": 710,
-    "mean": 0.062288,
-    "median": 0.049204,
-    "std": 0.0522,
-    "sem": 0.00196,
+    "count": 709,
+    "mean": 0.062285,
+    "median": 0.049078,
+    "std": 0.052765,
+    "sem": 0.001983,
     "min": 0.006565,
     "max": 0.436815,
-    "recall-0.025": 0.081056,
-    "recall-0.05": 0.340245,
+    "recall-0.025": 0.080113,
+    "recall-0.05": 0.341188,
     "recall-0.1": 0.591894,
-    "recall-0.15": 0.633365,
-    "recall-0.25": 0.655985,
-    "recall-0.5": 0.66918,
+    "recall-0.15": 0.632422,
+    "recall-0.25": 0.6541,
+    "recall-0.5": 0.668238,
     "num_labels": 1061
   },
   "shoulder_left": {
-    "count": 709,
-    "mean": 0.06144,
-    "median": 0.043151,
-    "std": 0.064106,
-    "sem": 0.002409,
+    "count": 708,
+    "mean": 0.062058,
+    "median": 0.043195,
+    "std": 0.066177,
+    "sem": 0.002489,
     "min": 0.003155,
-    "max": 0.48718,
-    "recall-0.025": 0.144204,
-    "recall-0.05": 0.400566,
-    "recall-0.1": 0.579642,
-    "recall-0.15": 0.62771,
-    "recall-0.25": 0.649387,
-    "recall-0.5": 0.668238,
+    "max": 0.498748,
+    "recall-0.025": 0.142319,
+    "recall-0.05": 0.401508,
+    "recall-0.1": 0.577757,
+    "recall-0.15": 0.625825,
+    "recall-0.25": 0.647502,
+    "recall-0.5": 0.667295,
     "num_labels": 1061
   },
   "shoulder_right": {
-    "count": 709,
-    "mean": 0.058254,
-    "median": 0.03951,
-    "std": 0.063443,
-    "sem": 0.002384,
-    "min": 0.001883,
+    "count": 708,
+    "mean": 0.05826,
+    "median": 0.039432,
+    "std": 0.064247,
+    "sem": 0.002416,
+    "min": 0.001884,
     "max": 0.470146,
-    "recall-0.025": 0.148916,
+    "recall-0.025": 0.147031,
     "recall-0.05": 0.428841,
     "recall-0.1": 0.588124,
     "recall-0.15": 0.630537,
-    "recall-0.25": 0.647502,
-    "recall-0.5": 0.668238,
+    "recall-0.25": 0.64656,
+    "recall-0.5": 0.667295,
     "num_labels": 1061
   },
   "elbow_left": {
-    "count": 697,
-    "mean": 0.100689,
-    "median": 0.073042,
-    "std": 0.081503,
-    "sem": 0.003089,
+    "count": 696,
+    "mean": 0.100812,
+    "median": 0.073006,
+    "std": 0.081769,
+    "sem": 0.003102,
     "min": 0.005709,
     "max": 0.481769,
     "recall-0.025": 0.067861,
     "recall-0.05": 0.216777,
     "recall-0.1": 0.399623,
-    "recall-0.15": 0.518379,
-    "recall-0.25": 0.617342,
-    "recall-0.5": 0.656927,
+    "recall-0.15": 0.516494,
+    "recall-0.25": 0.615457,
+    "recall-0.5": 0.655985,
     "num_labels": 1061
   },
   "elbow_right": {
-    "count": 700,
-    "mean": 0.079932,
-    "median": 0.056737,
-    "std": 0.074413,
-    "sem": 0.002815,
+    "count": 699,
+    "mean": 0.079988,
+    "median": 0.05676,
+    "std": 0.074464,
+    "sem": 0.002819,
     "min": 0.003313,
     "max": 0.498068,
-    "recall-0.025": 0.088596,
-    "recall-0.05": 0.29689,
+    "recall-0.025": 0.087653,
+    "recall-0.05": 0.295947,
     "recall-0.1": 0.499529,
     "recall-0.15": 0.585297,
-    "recall-0.25": 0.633365,
-    "recall-0.5": 0.659755,
+    "recall-0.25": 0.632422,
+    "recall-0.5": 0.658812,
     "num_labels": 1061
   },
   "wrist_left": {
-    "count": 703,
-    "mean": 0.127245,
-    "median": 0.091393,
-    "std": 0.103887,
-    "sem": 0.003921,
+    "count": 702,
+    "mean": 0.12747,
+    "median": 0.091245,
+    "std": 0.104152,
+    "sem": 0.003934,
     "min": 0.004181,
     "max": 0.497384,
     "recall-0.025": 0.06409,
     "recall-0.05": 0.173421,
     "recall-0.1": 0.351555,
-    "recall-0.15": 0.448633,
-    "recall-0.25": 0.569274,
-    "recall-0.5": 0.662582,
+    "recall-0.15": 0.446748,
+    "recall-0.25": 0.567389,
+    "recall-0.5": 0.66164,
     "num_labels": 1061
   },
   "wrist_right": {
-    "count": 698,
-    "mean": 0.112384,
-    "median": 0.078226,
-    "std": 0.098965,
-    "sem": 0.003749,
+    "count": 697,
+    "mean": 0.112895,
+    "median": 0.078236,
+    "std": 0.100097,
+    "sem": 0.003794,
     "min": 0.006423,
-    "max": 0.485199,
+    "max": 0.494757,
     "recall-0.025": 0.071631,
     "recall-0.05": 0.218662,
-    "recall-0.1": 0.39868,
+    "recall-0.1": 0.397738,
     "recall-0.15": 0.485391,
-    "recall-0.25": 0.583412,
-    "recall-0.5": 0.65787,
+    "recall-0.25": 0.581527,
+    "recall-0.5": 0.656927,
     "num_labels": 1061
   },
   "hip_left": {
-    "count": 697,
-    "mean": 0.18656,
-    "median": 0.167596,
-    "std": 0.084805,
-    "sem": 0.003215,
+    "count": 696,
+    "mean": 0.186824,
+    "median": 0.167605,
+    "std": 0.084772,
+    "sem": 0.003216,
     "min": 0.021758,
     "max": 0.499358,
     "recall-0.025": 0.000943,
     "recall-0.05": 0.005655,
-    "recall-0.1": 0.066918,
+    "recall-0.1": 0.065975,
     "recall-0.15": 0.249764,
-    "recall-0.25": 0.542884,
-    "recall-0.5": 0.656927,
+    "recall-0.25": 0.540999,
+    "recall-0.5": 0.655985,
     "num_labels": 1061
   },
   "hip_right": {
-    "count": 696,
-    "mean": 0.177844,
-    "median": 0.163941,
-    "std": 0.077814,
-    "sem": 0.002952,
+    "count": 695,
+    "mean": 0.178149,
+    "median": 0.163969,
+    "std": 0.078021,
+    "sem": 0.002962,
     "min": 0.024563,
     "max": 0.492847,
     "recall-0.025": 0.000943,
     "recall-0.05": 0.00377,
-    "recall-0.1": 0.077286,
-    "recall-0.15": 0.272385,
-    "recall-0.25": 0.558907,
-    "recall-0.5": 0.655985,
+    "recall-0.1": 0.076343,
+    "recall-0.15": 0.271442,
+    "recall-0.25": 0.557964,
+    "recall-0.5": 0.655042,
     "num_labels": 1061
   },
   "knee_left": {},
@@ -1011,18 +1011,18 @@ Results of the model in various experiments on different datasets.
   "ankle_right": {},
   "joint_recalls": {
     "num_labels": 9549,
-    "recall-0.025": 0.07404,
+    "recall-0.025": 0.0732,
     "recall-0.05": 0.23123,
-    "recall-0.1": 0.39428,
-    "recall-0.15": 0.49419,
-    "recall-0.25": 0.60593,
-    "recall-0.5": 0.66133
+    "recall-0.1": 0.39376,
+    "recall-0.15": 0.49314,
+    "recall-0.25": 0.60425,
+    "recall-0.5": 0.66028
   }
 }
 {
   "total_parts": 10610,
-  "correct_parts": 5509,
-  "pcp": 0.519227
+  "correct_parts": 5503,
+  "pcp": 0.518662
 }
 ```
 
@@ -1031,56 +1031,56 @@ Results of the model in various experiments on different datasets.
 (duration 00:02:28)
 ```json
 {
-  "avg_time_2d": 0.11225916583363603,
-  "avg_time_3d": 0.012731300912252287,
-  "avg_fps": 8.000610174798759
+  "avg_time_2d": 0.11131117460204334,
+  "avg_time_3d": 0.004892377737091808,
+  "avg_fps": 8.605588898707179
 }
 {
   "person_nums": {
     "total_frames": 420,
     "total_labels": 1466,
-    "total_preds": 1553,
-    "considered_empty": 9,
+    "total_preds": 1545,
+    "considered_empty": 6,
     "valid_preds": 1463,
-    "invalid_preds": 81,
+    "invalid_preds": 76,
     "missing": 3,
-    "invalid_fraction": 0.05246,
-    "precision": 0.94754,
+    "invalid_fraction": 0.04938,
+    "precision": 0.95062,
     "recall": 0.99795,
-    "f1": 0.97209,
-    "non_empty": 1544
+    "f1": 0.97371,
+    "non_empty": 1539
   },
   "mpjpe": {
     "count": 1463,
-    "mean": 0.042892,
-    "median": 0.038001,
-    "std": 0.020419,
-    "sem": 0.000534,
+    "mean": 0.042929,
+    "median": 0.038037,
+    "std": 0.02037,
+    "sem": 0.000533,
     "min": 0.01189,
     "max": 0.154352,
-    "recall-0.025": 0.152797,
-    "recall-0.05": 0.738745,
-    "recall-0.1": 0.982265,
+    "recall-0.025": 0.152115,
+    "recall-0.05": 0.738063,
+    "recall-0.1": 0.981583,
     "recall-0.15": 0.997271,
     "recall-0.25": 0.997954,
     "recall-0.5": 0.997954,
     "num_labels": 1466,
-    "ap-0.025": 0.063255,
-    "ap-0.05": 0.66489,
-    "ap-0.1": 0.960737,
-    "ap-0.15": 0.982534,
-    "ap-0.25": 0.983185,
-    "ap-0.5": 0.983185
+    "ap-0.025": 0.063073,
+    "ap-0.05": 0.663867,
+    "ap-0.1": 0.960176,
+    "ap-0.15": 0.982657,
+    "ap-0.25": 0.98331,
+    "ap-0.5": 0.98331
   },
   "nose": {
     "count": 1461,
-    "mean": 0.016222,
-    "median": 0.0125,
-    "std": 0.018762,
+    "mean": 0.016212,
+    "median": 0.012492,
+    "std": 0.01876,
     "sem": 0.000491,
     "min": 0.001407,
     "max": 0.282177,
-    "recall-0.025": 0.898838,
+    "recall-0.025": 0.899522,
     "recall-0.05": 0.963773,
     "recall-0.1": 0.995215,
     "recall-0.15": 0.995899,
@@ -1090,14 +1090,14 @@ Results of the model in various experiments on different datasets.
   },
   "shoulder_left": {
     "count": 1463,
-    "mean": 0.021439,
-    "median": 0.019011,
-    "std": 0.011407,
-    "sem": 0.000298,
+    "mean": 0.021455,
+    "median": 0.019023,
+    "std": 0.011436,
+    "sem": 0.000299,
     "min": 0.002677,
-    "max": 0.095793,
-    "recall-0.025": 0.70191,
-    "recall-0.05": 0.976126,
+    "max": 0.095794,
+    "recall-0.025": 0.702592,
+    "recall-0.05": 0.975443,
     "recall-0.1": 0.997954,
     "recall-0.15": 0.997954,
     "recall-0.25": 0.997954,
@@ -1106,14 +1106,14 @@ Results of the model in various experiments on different datasets.
   },
   "shoulder_right": {
     "count": 1462,
-    "mean": 0.02267,
-    "median": 0.019712,
-    "std": 0.014213,
+    "mean": 0.022691,
+    "median": 0.019716,
+    "std": 0.014235,
     "sem": 0.000372,
     "min": 0.00112,
     "max": 0.146597,
-    "recall-0.025": 0.712628,
-    "recall-0.05": 0.955631,
+    "recall-0.025": 0.711945,
+    "recall-0.05": 0.954949,
     "recall-0.1": 0.995904,
     "recall-0.15": 0.997952,
     "recall-0.25": 0.997952,
@@ -1122,15 +1122,15 @@ Results of the model in various experiments on different datasets.
   },
   "elbow_left": {
     "count": 1462,
-    "mean": 0.026445,
-    "median": 0.017876,
-    "std": 0.028559,
-    "sem": 0.000747,
+    "mean": 0.026381,
+    "median": 0.017863,
+    "std": 0.028393,
+    "sem": 0.000743,
     "min": 0.001846,
     "max": 0.323155,
     "recall-0.025": 0.701706,
     "recall-0.05": 0.880546,
-    "recall-0.1": 0.975427,
+    "recall-0.1": 0.976792,
     "recall-0.15": 0.985666,
     "recall-0.25": 0.996587,
     "recall-0.5": 0.997952,
@@ -1138,14 +1138,14 @@ Results of the model in various experiments on different datasets.
   },
   "elbow_right": {
     "count": 1461,
-    "mean": 0.02379,
+    "mean": 0.023727,
     "median": 0.018347,
-    "std": 0.01872,
-    "sem": 0.00049,
+    "std": 0.018642,
+    "sem": 0.000488,
     "min": 0.001407,
     "max": 0.219498,
     "recall-0.025": 0.684211,
-    "recall-0.05": 0.919344,
+    "recall-0.05": 0.920711,
     "recall-0.1": 0.992481,
     "recall-0.15": 0.997266,
     "recall-0.25": 0.998633,
@@ -1154,47 +1154,47 @@ Results of the model in various experiments on different datasets.
   },
   "wrist_left": {
     "count": 1431,
-    "mean": 0.036589,
-    "median": 0.017197,
-    "std": 0.053668,
-    "sem": 0.001419,
+    "mean": 0.036984,
+    "median": 0.017216,
+    "std": 0.05458,
+    "sem": 0.001443,
     "min": 0.000713,
     "max": 0.397255,
-    "recall-0.025": 0.669456,
-    "recall-0.05": 0.817992,
-    "recall-0.1": 0.905858,
-    "recall-0.15": 0.949791,
-    "recall-0.25": 0.98675,
+    "recall-0.025": 0.668759,
+    "recall-0.05": 0.816597,
+    "recall-0.1": 0.904463,
+    "recall-0.15": 0.948396,
+    "recall-0.25": 0.986053,
     "recall-0.5": 0.997908,
     "num_labels": 1434
   },
   "wrist_right": {
     "count": 1455,
-    "mean": 0.025451,
-    "median": 0.016755,
-    "std": 0.026894,
-    "sem": 0.000705,
+    "mean": 0.025496,
+    "median": 0.016754,
+    "std": 0.027589,
+    "sem": 0.000724,
     "min": 0.001905,
     "max": 0.394822,
-    "recall-0.025": 0.688874,
+    "recall-0.025": 0.68956,
     "recall-0.05": 0.882555,
-    "recall-0.1": 0.96978,
+    "recall-0.1": 0.970467,
     "recall-0.15": 0.993132,
-    "recall-0.25": 0.998626,
+    "recall-0.25": 0.99794,
     "recall-0.5": 0.999313,
     "num_labels": 1456
   },
   "hip_left": {
     "count": 1462,
-    "mean": 0.059024,
-    "median": 0.053496,
-    "std": 0.029585,
-    "sem": 0.000774,
+    "mean": 0.059122,
+    "median": 0.053514,
+    "std": 0.029657,
+    "sem": 0.000776,
     "min": 0.005563,
     "max": 0.17979,
-    "recall-0.025": 0.068942,
-    "recall-0.05": 0.423208,
-    "recall-0.1": 0.902389,
+    "recall-0.025": 0.068259,
+    "recall-0.05": 0.423891,
+    "recall-0.1": 0.901706,
     "recall-0.15": 0.969283,
     "recall-0.25": 0.997952,
     "recall-0.5": 0.997952,
@@ -1202,98 +1202,98 @@ Results of the model in various experiments on different datasets.
   },
   "hip_right": {
     "count": 1463,
-    "mean": 0.058525,
-    "median": 0.054915,
-    "std": 0.028493,
-    "sem": 0.000745,
+    "mean": 0.058599,
+    "median": 0.054934,
+    "std": 0.028583,
+    "sem": 0.000748,
     "min": 0.003776,
     "max": 0.304524,
     "recall-0.025": 0.05457,
-    "recall-0.05": 0.414052,
-    "recall-0.1": 0.91337,
-    "recall-0.15": 0.985675,
+    "recall-0.05": 0.412688,
+    "recall-0.1": 0.912688,
+    "recall-0.15": 0.984993,
     "recall-0.25": 0.997271,
     "recall-0.5": 0.997954,
     "num_labels": 1466
   },
   "knee_left": {
     "count": 1462,
-    "mean": 0.051154,
+    "mean": 0.051285,
     "median": 0.042752,
-    "std": 0.041788,
-    "sem": 0.001093,
+    "std": 0.04192,
+    "sem": 0.001097,
     "min": 0.001938,
     "max": 0.353024,
-    "recall-0.025": 0.187031,
-    "recall-0.05": 0.636177,
-    "recall-0.1": 0.931741,
-    "recall-0.15": 0.970648,
+    "recall-0.025": 0.185666,
+    "recall-0.05": 0.634812,
+    "recall-0.1": 0.931058,
+    "recall-0.15": 0.969966,
     "recall-0.25": 0.9843,
     "recall-0.5": 0.997952,
     "num_labels": 1465
   },
   "knee_right": {
     "count": 1456,
-    "mean": 0.048236,
-    "median": 0.042754,
-    "std": 0.02839,
-    "sem": 0.000744,
+    "mean": 0.048348,
+    "median": 0.042693,
+    "std": 0.029301,
+    "sem": 0.000768,
     "min": 0.003542,
-    "max": 0.269652,
-    "recall-0.025": 0.208362,
-    "recall-0.05": 0.59013,
-    "recall-0.1": 0.938314,
-    "recall-0.15": 0.993146,
-    "recall-0.25": 0.996573,
+    "max": 0.305725,
+    "recall-0.025": 0.210418,
+    "recall-0.05": 0.590816,
+    "recall-0.1": 0.938999,
+    "recall-0.15": 0.991775,
+    "recall-0.25": 0.995888,
     "recall-0.5": 0.997944,
     "num_labels": 1459
   },
   "ankle_left": {
     "count": 1453,
-    "mean": 0.08407,
-    "median": 0.037492,
-    "std": 0.106553,
-    "sem": 0.002796,
+    "mean": 0.083857,
+    "median": 0.037654,
+    "std": 0.106287,
+    "sem": 0.002789,
     "min": 0.002453,
     "max": 0.495773,
-    "recall-0.025": 0.362269,
-    "recall-0.05": 0.585099,
-    "recall-0.1": 0.725906,
-    "recall-0.15": 0.816131,
+    "recall-0.025": 0.360902,
+    "recall-0.05": 0.583732,
+    "recall-0.1": 0.726589,
+    "recall-0.15": 0.817498,
     "recall-0.25": 0.910458,
     "recall-0.5": 0.993165,
     "num_labels": 1463
   },
   "ankle_right": {
     "count": 1446,
-    "mean": 0.077209,
-    "median": 0.033338,
-    "std": 0.103299,
-    "sem": 0.002717,
+    "mean": 0.077158,
+    "median": 0.033178,
+    "std": 0.103249,
+    "sem": 0.002716,
     "min": 0.001061,
     "max": 0.493086,
     "recall-0.025": 0.372603,
-    "recall-0.05": 0.682877,
-    "recall-0.1": 0.778082,
+    "recall-0.05": 0.685616,
+    "recall-0.1": 0.777397,
     "recall-0.15": 0.815753,
-    "recall-0.25": 0.893836,
+    "recall-0.25": 0.893151,
     "recall-0.5": 0.990411,
     "num_labels": 1460
   },
   "joint_recalls": {
     "num_labels": 18990,
-    "recall-0.025": 0.48489,
-    "recall-0.05": 0.74776,
-    "recall-0.1": 0.92454,
-    "recall-0.15": 0.95882,
-    "recall-0.25": 0.98062,
+    "recall-0.025": 0.48468,
+    "recall-0.05": 0.74771,
+    "recall-0.1": 0.92428,
+    "recall-0.15": 0.95856,
+    "recall-0.25": 0.98052,
     "recall-0.5": 0.99695
   }
 }
 {
   "total_parts": 20444,
-  "correct_parts": 19973,
-  "pcp": 0.976961
+  "correct_parts": 19964,
+  "pcp": 0.976521
 }
 ```
 
diff --git a/scripts/test_skelda_dataset.py b/scripts/test_skelda_dataset.py
index 67065f4..3f37856 100644
--- a/scripts/test_skelda_dataset.py
+++ b/scripts/test_skelda_dataset.py
@@ -1,5 +1,6 @@
 import json
 import os
+import sys
 import time
 
 import cv2
@@ -8,10 +9,12 @@ import numpy as np
 import tqdm
 
 import test_triangulate
-import triangulate_poses
 import utils_2d_pose
 from skelda import evals, utils_pose
 
+sys.path.append("/SimplePoseTriangulation/swig/")
+import spt
+
 # ==================================================================================================
 
 # dataset_use = "panoptic"
@@ -316,12 +319,27 @@ def main():
     # Print a dataset sample for debugging
     print(labels[0])
 
+    minscores = {
+        # Choose this depending on the fraction of invalid/missing persons
+        # A higher value reduces the number of proposals
+        "panoptic": 0.94,
+        "human36m": 0.94,
+        "mvor": 0.86,
+        "campus": 0.96,
+        "shelf": 0.96,
+        "ikeaasm": 0.89,
+        "tsinghua": 0.96,
+        "human36m_wb": 0.94,
+        "koarob": 0.91,
+    }
+    minscore = minscores.get(dataset_use, 0.95)
+
     print("\nRunning predictions ...")
     all_poses = []
     all_ids = []
     all_paths = []
     times = []
-    last_poses_3d = np.array([])
+    triangulator = spt.Triangulator(min_score=minscore)
     old_scene = ""
     for label in tqdm.tqdm(labels):
         images_2d = []
@@ -329,7 +347,7 @@ def main():
         if old_scene != label.get("scene", ""):
             # Reset last poses if scene changes
             old_scene = label.get("scene", "")
-            last_poses_3d = np.array([])
+            triangulator.reset()
 
         try:
             start = time.time()
@@ -366,28 +384,20 @@ def main():
         time_2d = time.time() - start
         print("2D time:", time_2d)
 
-        minscores = {
-            # Choose this depending on the fraction of invalid/missing persons
-            # A higher value reduces the number of proposals
-            "panoptic": 0.94,
-            "human36m": 0.94,
-            "mvor": 0.86,
-            "campus": 0.96,
-            "shelf": 0.96,
-            "ikeaasm": 0.89,
-            "tsinghua": 0.96,
-            "human36m_wb": 0.94,
-        }
-        minscore = minscores.get(dataset_use, 0.95)
-
         start = time.time()
         if sum(np.sum(p) for p in poses_2d) == 0:
             poses3D = np.zeros([1, len(joint_names_3d), 4])
             poses2D = np.zeros([len(images_2d), 1, len(joint_names_3d), 3])
         else:
-            poses3D = triangulate_poses.get_3d_pose(
-                poses_2d, label["cameras"], roomparams, joint_names_2d, last_poses_3d, minscore
+            cameras = spt.convert_cameras(label["cameras"])
+            roomp = [roomparams["room_center"], roomparams["room_size"]]
+            poses_3d = triangulator.triangulate_poses(
+                poses_2d, cameras, roomp, joint_names_2d
             )
+            poses3D = np.array(poses_3d)
+            if len(poses3D) == 0:
+                poses3D = np.zeros([1, len(joint_names_3d), 4])
+
             poses2D = []
             for cam in label["cameras"]:
                 poses_2d, _ = utils_pose.project_poses(poses3D, cam)
@@ -401,7 +411,6 @@ def main():
                 drop_few_limbs=(dataset_use != "mvor"),
             )
             poses3D = add_missing_joints(poses3D, joint_names_3d)
-        last_poses_3d = poses3D
 
         time_3d = time.time() - start
         print("3D time:", time_3d)
diff --git a/scripts/test_triangulate.py b/scripts/test_triangulate.py
index 7a38f5a..ea6f594 100644
--- a/scripts/test_triangulate.py
+++ b/scripts/test_triangulate.py
@@ -1,6 +1,7 @@
 import copy
 import json
 import os
+import sys
 import time
 from typing import List
 
@@ -9,10 +10,12 @@ import matplotlib
 import numpy as np
 
 import draw_utils
-import triangulate_poses
 import utils_2d_pose
 from skelda import utils_pose
 
+sys.path.append("/SimplePoseTriangulation/swig/")
+import spt
+
 # ==================================================================================================
 
 filepath = os.path.dirname(os.path.realpath(__file__)) + "/"
@@ -437,7 +440,7 @@ def main():
         poses_2d = utils_2d_pose.get_2d_pose(kpt_model, images_2d)
         poses_2d = update_keypoints(poses_2d, joint_names_2d)
         print("2D time:", time.time() - stime)
-        # print(np.array(poses_2d).round(3).tolist())
+        # print([np.array(p).round(6).tolist() for p in poses_2d])
 
         fig1 = draw_utils.show_poses2d(
             poses_2d, np.array(images_2d), joint_names_2d, "2D detections"
@@ -454,11 +457,19 @@ def main():
             poses3D = np.zeros([1, len(joint_names_3d), 4])
             poses2D = np.zeros([len(images_2d), 1, len(joint_names_3d), 3])
         else:
+            cameras = spt.convert_cameras(camparams)
+            roomp = [roomparams["room_center"], roomparams["room_size"]]
+            triangulator = spt.Triangulator(min_score=0.95)
+
             stime = time.time()
-            poses3D = triangulate_poses.get_3d_pose(
-                poses_2d, camparams, roomparams, joint_names_2d
+            poses_3d = triangulator.triangulate_poses(
+                poses_2d, cameras, roomp, joint_names_2d
             )
+            poses3D = np.array(poses_3d)
+            if len(poses3D) == 0:
+                poses3D = np.zeros([1, len(joint_names_3d), 4])
             print("3D time:", time.time() - stime)
+
             poses2D = []
             for cam in camparams:
                 poses_2d, _ = utils_pose.project_poses(poses3D, cam)