diff --git a/media/RESULTS.md b/media/RESULTS.md
index df99e2d..9b16064 100644
--- a/media/RESULTS.md
+++ b/media/RESULTS.md
@@ -7,10 +7,10 @@ Results of the model in various experiments on different datasets. \
 
 ```json
 {
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-  "avg_time_2d": 0.008879996154267909,
-  "avg_time_3d": 0.0003021462488982637,
-  "avg_fps": 108.9070454467338
+  "img_loading": 0.02141373965699794,
+  "avg_time_2d": 0.009271658073037358,
+  "avg_time_3d": 0.00028209807509082857,
+  "avg_fps": 104.67087337119489
 }
 {
   "person_nums": {
@@ -29,165 +29,165 @@ Results of the model in various experiments on different datasets. \
   },
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@@ -195,82 +195,82 @@ Results of the model in various experiments on different datasets. \
   },
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 }
 {
   "total_parts": 8400,
-  "correct_parts": 8098,
-  "pcp": 0.964048
+  "correct_parts": 8118,
+  "pcp": 0.966429
 }
 ```
 
@@ -278,107 +278,107 @@ Results of the model in various experiments on different datasets. \
 
 ```json
 {
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-  "avg_time_2d": 0.01779815503412096,
-  "avg_time_3d": 0.0006907936633657344,
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+  "img_loading": 0.044489096120460744,
+  "avg_time_2d": 0.018255191979949008,
+  "avg_time_3d": 0.0006648879690268605,
+  "avg_fps": 52.85389928038487
 }
 {
   "person_nums": {
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     "missing": 0,
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@@ -386,79 +386,79 @@ Results of the model in various experiments on different datasets. \
   },
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-  "correct_parts": 4946,
-  "pcp": 0.93959
+  "correct_parts": 4931,
+  "pcp": 0.93674
 }
 ```
 
@@ -820,206 +820,206 @@ Results of the model in various experiments on different datasets. \
 
 ```json
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+  "avg_fps": 94.72857150206536
 }
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 }
 {
   "total_parts": 10610,
-  "correct_parts": 6144,
-  "pcp": 0.579076
+  "correct_parts": 6280,
+  "pcp": 0.591894
 }
 ```
 
@@ -1027,270 +1027,270 @@ Results of the model in various experiments on different datasets. \
 
 ```json
 {
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+  "avg_time_3d": 0.0008079400876673256,
+  "avg_fps": 45.76659085673456
 }
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   "total_parts": 20444,
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-  "pcp": 0.977744
+  "correct_parts": 20124,
+  "pcp": 0.984347
 }
 ```
 
@@ -1841,270 +1841,270 @@ Results of the model in various experiments on different datasets. \
 (duration 00:01:45)
 ```json
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   "total_parts": 4313,
-  "correct_parts": 3703,
-  "pcp": 0.858567
+  "correct_parts": 3837,
+  "pcp": 0.889636
 }
 ```
 
@@ -2112,270 +2112,270 @@ Results of the model in various experiments on different datasets. \
 
 ```json
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 }
 ```
 
@@ -2384,10 +2384,10 @@ Results of the model in various experiments on different datasets. \
 (duration 00:01:51)
 ```json
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@@ -2476,15 +2476,15 @@ Results of the model in various experiments on different datasets. \
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@@ -2492,79 +2492,79 @@ Results of the model in various experiments on different datasets. \
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@@ -2572,82 +2572,82 @@ Results of the model in various experiments on different datasets. \
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 }
 ```
 
@@ -5201,270 +5201,270 @@ Results of the model in various experiments on different datasets. \
 
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+  "correct_parts": 5107,
+  "pcp": 0.911964
 }
 ```
 
@@ -5472,10 +5472,10 @@ Results of the model in various experiments on different datasets. \
 
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@@ -5516,14 +5516,14 @@ Results of the model in various experiments on different datasets. \
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@@ -5532,15 +5532,15 @@ Results of the model in various experiments on different datasets. \
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@@ -5548,14 +5548,14 @@ Results of the model in various experiments on different datasets. \
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@@ -5564,14 +5564,14 @@ Results of the model in various experiments on different datasets. \
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@@ -5580,15 +5580,15 @@ Results of the model in various experiments on different datasets. \
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@@ -5596,47 +5596,47 @@ Results of the model in various experiments on different datasets. \
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@@ -5644,15 +5644,15 @@ Results of the model in various experiments on different datasets. \
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@@ -5660,30 +5660,30 @@ Results of the model in various experiments on different datasets. \
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@@ -5692,15 +5692,15 @@ Results of the model in various experiments on different datasets. \
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@@ -5743,59 +5743,59 @@ Results of the model in various experiments on different datasets. \
 
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@@ -5803,14 +5803,14 @@ Results of the model in various experiments on different datasets. \
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@@ -5819,15 +5819,15 @@ Results of the model in various experiments on different datasets. \
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@@ -5835,14 +5835,14 @@ Results of the model in various experiments on different datasets. \
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@@ -5867,15 +5867,15 @@ Results of the model in various experiments on different datasets. \
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@@ -5883,30 +5883,30 @@ Results of the model in various experiments on different datasets. \
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@@ -5915,15 +5915,15 @@ Results of the model in various experiments on different datasets. \
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@@ -5931,14 +5931,14 @@ Results of the model in various experiments on different datasets. \
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@@ -5947,15 +5947,15 @@ Results of the model in various experiments on different datasets. \
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@@ -5963,14 +5963,14 @@ Results of the model in various experiments on different datasets. \
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@@ -5979,15 +5979,15 @@ Results of the model in various experiments on different datasets. \
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@@ -5995,10 +5995,10 @@ Results of the model in various experiments on different datasets. \
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@@ -6014,270 +6014,270 @@ Results of the model in various experiments on different datasets. \
 
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-  "pcp": 0.991775
+  "correct_parts": 4601,
+  "pcp": 0.995887
 }
 ```
 
@@ -6285,270 +6285,270 @@ Results of the model in various experiments on different datasets. \
 
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-  "correct_parts": 6708,
-  "pcp": 0.989965
+  "correct_parts": 6751,
+  "pcp": 0.996311
 }
 ```
 
@@ -6556,270 +6556,270 @@ Results of the model in various experiments on different datasets. \
 
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-    "median": 0.021864,
-    "std": 0.012415,
-    "sem": 0.000459,
-    "min": 0.002299,
-    "max": 0.105891,
-    "recall-0.025": 0.606557,
-    "recall-0.05": 0.960383,
-    "recall-0.1": 0.997268,
-    "recall-0.15": 0.998634,
-    "recall-0.25": 0.998634,
-    "recall-0.5": 0.998634,
+    "count": 732,
+    "mean": 0.022957,
+    "median": 0.020533,
+    "std": 0.012582,
+    "sem": 0.000465,
+    "min": 0.00129,
+    "max": 0.092388,
+    "recall-0.025": 0.65847,
+    "recall-0.05": 0.956284,
+    "recall-0.1": 1.0,
+    "recall-0.15": 1.0,
+    "recall-0.25": 1.0,
+    "recall-0.5": 1.0,
     "num_labels": 732
   },
   "ankle_left": {
-    "count": 731,
-    "mean": 0.024101,
-    "median": 0.02261,
-    "std": 0.012047,
-    "sem": 0.000446,
-    "min": 0.002694,
-    "max": 0.097153,
-    "recall-0.025": 0.587432,
-    "recall-0.05": 0.967213,
-    "recall-0.1": 0.998634,
-    "recall-0.15": 0.998634,
-    "recall-0.25": 0.998634,
-    "recall-0.5": 0.998634,
+    "count": 732,
+    "mean": 0.021423,
+    "median": 0.0198,
+    "std": 0.011635,
+    "sem": 0.00043,
+    "min": 0.000537,
+    "max": 0.091134,
+    "recall-0.025": 0.68306,
+    "recall-0.05": 0.976776,
+    "recall-0.1": 1.0,
+    "recall-0.15": 1.0,
+    "recall-0.25": 1.0,
+    "recall-0.5": 1.0,
     "num_labels": 732
   },
   "ankle_right": {
-    "count": 731,
-    "mean": 0.025867,
-    "median": 0.024513,
-    "std": 0.012665,
-    "sem": 0.000469,
-    "min": 0.002228,
-    "max": 0.093989,
-    "recall-0.025": 0.521858,
-    "recall-0.05": 0.956284,
-    "recall-0.1": 0.998634,
-    "recall-0.15": 0.998634,
-    "recall-0.25": 0.998634,
-    "recall-0.5": 0.998634,
+    "count": 732,
+    "mean": 0.022716,
+    "median": 0.020516,
+    "std": 0.011964,
+    "sem": 0.000442,
+    "min": 0.00268,
+    "max": 0.086346,
+    "recall-0.025": 0.644809,
+    "recall-0.05": 0.968579,
+    "recall-0.1": 1.0,
+    "recall-0.15": 1.0,
+    "recall-0.25": 1.0,
+    "recall-0.5": 1.0,
     "num_labels": 732
   },
   "joint_recalls": {
     "num_labels": 9516,
-    "recall-0.025": 0.51555,
-    "recall-0.05": 0.90017,
-    "recall-0.1": 0.99086,
-    "recall-0.15": 0.99748,
-    "recall-0.25": 0.99853,
-    "recall-0.5": 0.99863
+    "recall-0.025": 0.55212,
+    "recall-0.05": 0.90858,
+    "recall-0.1": 0.99233,
+    "recall-0.15": 0.99884,
+    "recall-0.25": 1.0,
+    "recall-0.5": 1.0
   }
 }
 {
   "total_parts": 10248,
-  "correct_parts": 10227,
-  "pcp": 0.997951
+  "correct_parts": 10237,
+  "pcp": 0.998927
 }
 ```
 
@@ -6827,59 +6827,59 @@ Results of the model in various experiments on different datasets. \
 
 ```json
 {
-  "img_loading": 0.6606097068005835,
-  "avg_time_2d": 0.06163697772555881,
-  "avg_time_3d": 0.00240840270505314,
-  "avg_fps": 15.613928643665723
+  "img_loading": 0.7245951409925494,
+  "avg_time_2d": 0.09512041047302602,
+  "avg_time_3d": 0.002043061786227756,
+  "avg_fps": 10.2919335502109
 }
 {
   "person_nums": {
     "total_frames": 181,
     "total_labels": 362,
-    "total_preds": 363,
+    "total_preds": 362,
     "considered_empty": 0,
     "valid_preds": 362,
-    "invalid_preds": 1,
+    "invalid_preds": 0,
     "missing": 0,
-    "invalid_fraction": 0.00275,
-    "precision": 0.99725,
+    "invalid_fraction": 0.0,
+    "precision": 1.0,
     "recall": 1.0,
-    "f1": 0.99862,
-    "non_empty": 363
+    "f1": 1.0,
+    "non_empty": 362
   },
   "mpjpe": {
     "count": 362,
-    "mean": 0.025628,
-    "median": 0.024117,
-    "std": 0.009588,
-    "sem": 0.000505,
-    "min": 0.013981,
-    "max": 0.131115,
-    "recall-0.025": 0.588398,
-    "recall-0.05": 0.980663,
+    "mean": 0.023644,
+    "median": 0.022255,
+    "std": 0.009435,
+    "sem": 0.000497,
+    "min": 0.012188,
+    "max": 0.126226,
+    "recall-0.025": 0.690608,
+    "recall-0.05": 0.983425,
     "recall-0.1": 0.997238,
     "recall-0.15": 1.0,
     "recall-0.25": 1.0,
     "recall-0.5": 1.0,
     "num_labels": 362,
-    "ap-0.025": 0.418845,
-    "ap-0.05": 0.978155,
-    "ap-0.1": 0.997184,
+    "ap-0.025": 0.572774,
+    "ap-0.05": 0.983156,
+    "ap-0.1": 0.997169,
     "ap-0.15": 1.0,
     "ap-0.25": 1.0,
     "ap-0.5": 1.0
   },
   "head": {
     "count": 362,
-    "mean": 0.038472,
-    "median": 0.037725,
-    "std": 0.014629,
-    "sem": 0.00077,
-    "min": 0.004845,
-    "max": 0.13214,
-    "recall-0.025": 0.168508,
-    "recall-0.05": 0.820442,
-    "recall-0.1": 0.997238,
+    "mean": 0.036626,
+    "median": 0.036248,
+    "std": 0.015805,
+    "sem": 0.000832,
+    "min": 0.002253,
+    "max": 0.120652,
+    "recall-0.025": 0.21547,
+    "recall-0.05": 0.856354,
+    "recall-0.1": 0.994475,
     "recall-0.15": 1.0,
     "recall-0.25": 1.0,
     "recall-0.5": 1.0,
@@ -6887,31 +6887,31 @@ Results of the model in various experiments on different datasets. \
   },
   "shoulder_left": {
     "count": 362,
-    "mean": 0.027359,
-    "median": 0.02335,
-    "std": 0.017146,
-    "sem": 0.000902,
-    "min": 0.003874,
-    "max": 0.205663,
-    "recall-0.025": 0.546961,
-    "recall-0.05": 0.925414,
-    "recall-0.1": 0.997238,
-    "recall-0.15": 0.997238,
+    "mean": 0.023775,
+    "median": 0.020648,
+    "std": 0.01403,
+    "sem": 0.000738,
+    "min": 0.002847,
+    "max": 0.088362,
+    "recall-0.025": 0.627072,
+    "recall-0.05": 0.947514,
+    "recall-0.1": 1.0,
+    "recall-0.15": 1.0,
     "recall-0.25": 1.0,
     "recall-0.5": 1.0,
     "num_labels": 362
   },
   "shoulder_right": {
     "count": 362,
-    "mean": 0.034435,
-    "median": 0.032006,
-    "std": 0.015656,
-    "sem": 0.000824,
-    "min": 0.006446,
-    "max": 0.14459,
-    "recall-0.025": 0.251381,
-    "recall-0.05": 0.872928,
-    "recall-0.1": 0.994475,
+    "mean": 0.02824,
+    "median": 0.02531,
+    "std": 0.015051,
+    "sem": 0.000792,
+    "min": 0.002271,
+    "max": 0.100955,
+    "recall-0.025": 0.486188,
+    "recall-0.05": 0.91989,
+    "recall-0.1": 0.997238,
     "recall-0.15": 1.0,
     "recall-0.25": 1.0,
     "recall-0.5": 1.0,
@@ -6919,15 +6919,15 @@ Results of the model in various experiments on different datasets. \
   },
   "elbow_left": {
     "count": 362,
-    "mean": 0.020715,
-    "median": 0.017045,
-    "std": 0.015227,
-    "sem": 0.000801,
-    "min": 0.001981,
-    "max": 0.129145,
-    "recall-0.025": 0.748619,
-    "recall-0.05": 0.950276,
-    "recall-0.1": 0.994475,
+    "mean": 0.020885,
+    "median": 0.016729,
+    "std": 0.013454,
+    "sem": 0.000708,
+    "min": 0.002948,
+    "max": 0.100452,
+    "recall-0.025": 0.729282,
+    "recall-0.05": 0.958564,
+    "recall-0.1": 0.997238,
     "recall-0.15": 1.0,
     "recall-0.25": 1.0,
     "recall-0.5": 1.0,
@@ -6935,31 +6935,31 @@ Results of the model in various experiments on different datasets. \
   },
   "elbow_right": {
     "count": 362,
-    "mean": 0.024957,
-    "median": 0.020567,
-    "std": 0.019737,
-    "sem": 0.001039,
-    "min": 0.002462,
-    "max": 0.168376,
-    "recall-0.025": 0.646409,
-    "recall-0.05": 0.939227,
-    "recall-0.1": 0.983425,
-    "recall-0.15": 0.997238,
+    "mean": 0.024025,
+    "median": 0.021397,
+    "std": 0.01553,
+    "sem": 0.000817,
+    "min": 0.0009,
+    "max": 0.149873,
+    "recall-0.025": 0.610497,
+    "recall-0.05": 0.955801,
+    "recall-0.1": 0.994475,
+    "recall-0.15": 1.0,
     "recall-0.25": 1.0,
     "recall-0.5": 1.0,
     "num_labels": 362
   },
   "wrist_left": {
     "count": 362,
-    "mean": 0.02462,
-    "median": 0.022084,
-    "std": 0.015491,
-    "sem": 0.000815,
-    "min": 0.002166,
-    "max": 0.129272,
-    "recall-0.025": 0.588398,
+    "mean": 0.02249,
+    "median": 0.018789,
+    "std": 0.014691,
+    "sem": 0.000773,
+    "min": 0.001059,
+    "max": 0.095284,
+    "recall-0.025": 0.665746,
     "recall-0.05": 0.944751,
-    "recall-0.1": 0.997238,
+    "recall-0.1": 1.0,
     "recall-0.15": 1.0,
     "recall-0.25": 1.0,
     "recall-0.5": 1.0,
@@ -6967,15 +6967,15 @@ Results of the model in various experiments on different datasets. \
   },
   "wrist_right": {
     "count": 362,
-    "mean": 0.030253,
-    "median": 0.023575,
-    "std": 0.028573,
-    "sem": 0.001504,
-    "min": 0.001456,
-    "max": 0.304691,
-    "recall-0.025": 0.535912,
-    "recall-0.05": 0.864641,
-    "recall-0.1": 0.983425,
+    "mean": 0.028614,
+    "median": 0.023014,
+    "std": 0.027617,
+    "sem": 0.001454,
+    "min": 0.000565,
+    "max": 0.309303,
+    "recall-0.025": 0.538674,
+    "recall-0.05": 0.89779,
+    "recall-0.1": 0.98895,
     "recall-0.15": 0.994475,
     "recall-0.25": 0.994475,
     "recall-0.5": 1.0,
@@ -6983,31 +6983,31 @@ Results of the model in various experiments on different datasets. \
   },
   "hip_left": {
     "count": 362,
-    "mean": 0.026354,
-    "median": 0.023315,
-    "std": 0.01518,
-    "sem": 0.000799,
-    "min": 0.002699,
-    "max": 0.15654,
-    "recall-0.025": 0.549724,
-    "recall-0.05": 0.953039,
+    "mean": 0.0255,
+    "median": 0.022948,
+    "std": 0.014585,
+    "sem": 0.000768,
+    "min": 0.002524,
+    "max": 0.140758,
+    "recall-0.025": 0.552486,
+    "recall-0.05": 0.944751,
     "recall-0.1": 0.997238,
-    "recall-0.15": 0.997238,
+    "recall-0.15": 1.0,
     "recall-0.25": 1.0,
     "recall-0.5": 1.0,
     "num_labels": 362
   },
   "hip_right": {
     "count": 362,
-    "mean": 0.028051,
-    "median": 0.025509,
-    "std": 0.016384,
-    "sem": 0.000862,
-    "min": 0.002476,
-    "max": 0.215051,
-    "recall-0.025": 0.461326,
-    "recall-0.05": 0.939227,
-    "recall-0.1": 0.997238,
+    "mean": 0.026037,
+    "median": 0.023594,
+    "std": 0.017228,
+    "sem": 0.000907,
+    "min": 0.001997,
+    "max": 0.188306,
+    "recall-0.025": 0.558011,
+    "recall-0.05": 0.936464,
+    "recall-0.1": 0.991713,
     "recall-0.15": 0.997238,
     "recall-0.25": 1.0,
     "recall-0.5": 1.0,
@@ -7015,14 +7015,14 @@ Results of the model in various experiments on different datasets. \
   },
   "knee_left": {
     "count": 362,
-    "mean": 0.014625,
-    "median": 0.012931,
-    "std": 0.007674,
-    "sem": 0.000404,
-    "min": 0.000793,
-    "max": 0.053751,
-    "recall-0.025": 0.903315,
-    "recall-0.05": 0.997238,
+    "mean": 0.015771,
+    "median": 0.014171,
+    "std": 0.008043,
+    "sem": 0.000423,
+    "min": 0.002213,
+    "max": 0.04907,
+    "recall-0.025": 0.864641,
+    "recall-0.05": 1.0,
     "recall-0.1": 1.0,
     "recall-0.15": 1.0,
     "recall-0.25": 1.0,
@@ -7031,13 +7031,13 @@ Results of the model in various experiments on different datasets. \
   },
   "knee_right": {
     "count": 362,
-    "mean": 0.016569,
-    "median": 0.013809,
-    "std": 0.017197,
-    "sem": 0.000905,
-    "min": 0.000436,
-    "max": 0.268298,
-    "recall-0.025": 0.889503,
+    "mean": 0.016923,
+    "median": 0.014611,
+    "std": 0.016323,
+    "sem": 0.000859,
+    "min": 0.001828,
+    "max": 0.259361,
+    "recall-0.025": 0.867403,
     "recall-0.05": 0.991713,
     "recall-0.1": 0.994475,
     "recall-0.15": 0.997238,
@@ -7047,15 +7047,15 @@ Results of the model in various experiments on different datasets. \
   },
   "ankle_left": {
     "count": 362,
-    "mean": 0.024424,
-    "median": 0.022628,
-    "std": 0.011403,
-    "sem": 0.0006,
-    "min": 0.004148,
-    "max": 0.108124,
-    "recall-0.025": 0.599448,
-    "recall-0.05": 0.972376,
-    "recall-0.1": 0.997238,
+    "mean": 0.019707,
+    "median": 0.017121,
+    "std": 0.011593,
+    "sem": 0.00061,
+    "min": 0.001651,
+    "max": 0.078642,
+    "recall-0.025": 0.767956,
+    "recall-0.05": 0.966851,
+    "recall-0.1": 1.0,
     "recall-0.15": 1.0,
     "recall-0.25": 1.0,
     "recall-0.5": 1.0,
@@ -7063,14 +7063,14 @@ Results of the model in various experiments on different datasets. \
   },
   "ankle_right": {
     "count": 362,
-    "mean": 0.022326,
-    "median": 0.018382,
-    "std": 0.027706,
-    "sem": 0.001458,
-    "min": 0.001387,
-    "max": 0.477011,
-    "recall-0.025": 0.740331,
-    "recall-0.05": 0.950276,
+    "mean": 0.018776,
+    "median": 0.014697,
+    "std": 0.025258,
+    "sem": 0.001329,
+    "min": 0.001777,
+    "max": 0.425641,
+    "recall-0.025": 0.850829,
+    "recall-0.05": 0.961326,
     "recall-0.1": 0.994475,
     "recall-0.15": 0.997238,
     "recall-0.25": 0.997238,
@@ -7079,17 +7079,17 @@ Results of the model in various experiments on different datasets. \
   },
   "joint_recalls": {
     "num_labels": 4706,
-    "recall-0.025": 0.58606,
-    "recall-0.05": 0.93136,
-    "recall-0.1": 0.9932,
-    "recall-0.15": 0.99809,
+    "recall-0.025": 0.64003,
+    "recall-0.05": 0.94369,
+    "recall-0.1": 0.99511,
+    "recall-0.15": 0.99873,
     "recall-0.25": 0.99894,
     "recall-0.5": 1.0
   }
 }
 {
   "total_parts": 5068,
-  "correct_parts": 5057,
-  "pcp": 0.99783
+  "correct_parts": 5064,
+  "pcp": 0.999211
 }
 ```
diff --git a/scripts/test_skelda_dataset.py b/scripts/test_skelda_dataset.py
index 126f8d1..3dd3967 100644
--- a/scripts/test_skelda_dataset.py
+++ b/scripts/test_skelda_dataset.py
@@ -61,7 +61,7 @@ datasets = {
     "human36m": {
         "path": "/datasets/human36m/skelda/pose_test.json",
         "take_interval": 5,
-        "min_match_score": 0.94,
+        "min_match_score": 0.95,
         "min_group_size": 1,
         "min_bbox_score": 0.4,
         "min_bbox_area": 0.1 * 0.1,
@@ -73,6 +73,7 @@ datasets = {
         # "cams": ["00_03", "00_06", "00_12"],
         # "cams": ["00_03", "00_06", "00_12", "00_13", "00_23", "00_15", "00_10", "00_21", "00_09", "00_01"],
         "take_interval": 3,
+        "min_match_score": 0.95,
         "use_scenes": ["160906_pizza1", "160422_haggling1", "160906_ian5"],
         "min_group_size": 1,
         # "min_group_size": 4,
@@ -88,6 +89,7 @@ datasets = {
     "campus": {
         "path": "/datasets/campus/skelda/test.json",
         "take_interval": 1,
+        "min_match_score": 0.90,
         "min_bbox_score": 0.5,
     },
     "shelf": {
@@ -109,6 +111,7 @@ datasets = {
     "tsinghua": {
         "path": "/datasets/tsinghua/skelda/test.json",
         "take_interval": 3,
+        "min_match_score": 0.95,
         "min_group_size": 2,
     },
     "human36m_wb": {
@@ -122,7 +125,7 @@ datasets = {
         "take_interval": 2,
         "subset": "tagging",
         "min_group_size": 2,
-        "min_bbox_score": 0.25,
+        "min_bbox_score": 0.2,
         "min_bbox_area": 0.05 * 0.05,
     },
     "egohumans_legoassemble": {
@@ -343,19 +346,32 @@ def main():
     # Print a dataset sample for debugging
     print(labels[0])
 
+    print("\nPrefetching images ...")
+    for label in tqdm.tqdm(labels):
+        # If the images are stored on a HDD, it sometimes takes a while to load them
+        # Prefetching them results in more stable timings of the following steps
+        # To prevent memory overflow, the code only loads the images, but does not store them
+        try:
+            for i in range(len(label["imgpaths"])):
+                imgpath = label["imgpaths"][i]
+                img = test_triangulate.load_image(imgpath)
+        except cv2.error:
+            print("One of the paths not found:", label["imgpaths"])
+            continue
+    time.sleep(3)
+
     print("\nCalculating 2D predictions ...")
     all_poses_2d = []
     times = []
     for label in tqdm.tqdm(labels):
         images_2d = []
 
+        start = time.time()
         try:
-            start = time.time()
             for i in range(len(label["imgpaths"])):
                 imgpath = label["imgpaths"][i]
                 img = test_triangulate.load_image(imgpath)
                 images_2d.append(img)
-            time_imgs = time.time() - start
         except cv2.error:
             print("One of the paths not found:", label["imgpaths"])
             continue
@@ -373,7 +389,16 @@ def main():
                     cam["K"][0][2] = cam["K"][0][2] * (1000 / ishape[1])
                     images_2d[i] = cv2.resize(img, (1000, 1000))
 
+        # Convert image format to Bayer encoding to simulate real camera input
+        # This also resulted in notably better MPJPE results in most cases, presumbly since the
+        # demosaicing algorithm from OpenCV is better than the default one from the cameras
+        for i in range(len(images_2d)):
+            images_2d[i] = test_triangulate.rgb2bayer(images_2d[i])
+        time_imgs = time.time() - start
+
         start = time.time()
+        for i in range(len(images_2d)):
+            images_2d[i] = test_triangulate.bayer2rgb(images_2d[i])
         poses_2d = utils_2d_pose.get_2d_pose(kpt_model, images_2d)
         poses_2d = test_triangulate.update_keypoints(poses_2d, joint_names_2d)
         time_2d = time.time() - start
diff --git a/scripts/test_triangulate.py b/scripts/test_triangulate.py
index 831a7e6..481e4b0 100644
--- a/scripts/test_triangulate.py
+++ b/scripts/test_triangulate.py
@@ -227,6 +227,23 @@ def load_image(path: str):
 # ==================================================================================================
 
 
+def rgb2bayer(img):
+    bayer = np.zeros((img.shape[0], img.shape[1]), dtype=img.dtype)
+    bayer[0::2, 0::2] = img[0::2, 0::2, 0]
+    bayer[0::2, 1::2] = img[0::2, 1::2, 1]
+    bayer[1::2, 0::2] = img[1::2, 0::2, 1]
+    bayer[1::2, 1::2] = img[1::2, 1::2, 2]
+    return bayer
+
+
+def bayer2rgb(bayer):
+    img = cv2.cvtColor(bayer, cv2.COLOR_BayerBG2RGB)
+    return img
+
+
+# ==================================================================================================
+
+
 def update_keypoints(poses_2d: list, joint_names: List[str]) -> list:
     new_views = []
     for view in poses_2d:
@@ -314,6 +331,8 @@ def main():
         for i in range(len(sample["cameras_color"])):
             imgpath = sample["imgpaths_color"][i]
             img = load_image(imgpath)
+            img = rgb2bayer(img)
+            img = bayer2rgb(img)
             images_2d.append(img)
 
         # Get 2D poses