diff --git a/media/RESULTS.md b/media/RESULTS.md
index 7b1b616..9f3d197 100644
--- a/media/RESULTS.md
+++ b/media/RESULTS.md
@@ -6,9 +6,9 @@ Results of the model in various experiments on different datasets.
 
 ```json
 {
-  "avg_time_2d": 0.010003441875263796,
-  "avg_time_3d": 0.0003245426436602059,
-  "avg_fps": 96.824312446218
+  "avg_time_2d": 0.009281002464940992,
+  "avg_time_3d": 0.00031142558081675384,
+  "avg_fps": 104.24889248371795
 }
 {
   "person_nums": {
@@ -27,149 +27,149 @@ Results of the model in various experiments on different datasets.
   },
   "mpjpe": {
     "count": 600,
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-    "median": 0.058987,
-    "std": 0.027958,
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-    "max": 0.189648,
+    "mean": 0.066564,
+    "median": 0.05836,
+    "std": 0.028051,
+    "sem": 0.001146,
+    "min": 0.043283,
+    "max": 0.190505,
     "recall-0.025": 0.0,
-    "recall-0.05": 0.061667,
-    "recall-0.1": 0.93,
+    "recall-0.05": 0.06,
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     "recall-0.15": 0.95,
     "recall-0.25": 1.0,
     "recall-0.5": 1.0,
     "num_labels": 600,
     "ap-0.025": 0.0,
-    "ap-0.05": 0.00503,
-    "ap-0.1": 0.887557,
-    "ap-0.15": 0.913732,
+    "ap-0.05": 0.005904,
+    "ap-0.1": 0.88884,
+    "ap-0.15": 0.912818,
     "ap-0.25": 1.0,
     "ap-0.5": 1.0
   },
   "nose": {
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-    "sem": 0.001806,
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+    "std": 0.041516,
+    "sem": 0.001696,
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     "recall-0.025": 0.0,
-    "recall-0.05": 0.015,
-    "recall-0.1": 0.52,
-    "recall-0.15": 0.816667,
-    "recall-0.25": 0.988333,
+    "recall-0.05": 0.01,
+    "recall-0.1": 0.521667,
+    "recall-0.15": 0.821667,
+    "recall-0.25": 0.993333,
     "recall-0.5": 1.0,
     "num_labels": 600
   },
   "shoulder_left": {
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-    "recall-0.05": 0.863333,
-    "recall-0.1": 0.941667,
-    "recall-0.15": 0.966667,
+    "mean": 0.034321,
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+    "sem": 0.001316,
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+    "recall-0.1": 0.945,
+    "recall-0.15": 0.965,
     "recall-0.25": 1.0,
     "recall-0.5": 1.0,
     "num_labels": 600
   },
   "shoulder_right": {
     "count": 600,
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-    "recall-0.1": 0.913333,
-    "recall-0.15": 0.945,
-    "recall-0.25": 0.998333,
+    "mean": 0.046987,
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+    "recall-0.05": 0.773333,
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+    "recall-0.25": 1.0,
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     "num_labels": 600
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   "elbow_left": {
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     "recall-0.05": 0.805,
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     "recall-0.25": 1.0,
     "recall-0.5": 1.0,
     "num_labels": 600
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   "elbow_right": {
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+    "recall-0.25": 0.998333,
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   "wrist_right": {
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+    "count": 597,
+    "mean": 0.046115,
+    "median": 0.027472,
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+    "sem": 0.002206,
+    "min": 0.002569,
+    "max": 0.482775,
+    "recall-0.025": 0.455,
+    "recall-0.05": 0.748333,
+    "recall-0.1": 0.888333,
+    "recall-0.15": 0.898333,
     "recall-0.25": 0.991667,
-    "recall-0.5": 0.998333,
+    "recall-0.5": 0.995,
     "num_labels": 600
   },
   "hip_left": {
     "count": 600,
-    "mean": 0.089504,
-    "median": 0.085316,
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-    "sem": 0.001345,
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-    "recall-0.025": 0.006667,
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-    "recall-0.1": 0.815,
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     "recall-0.15": 0.951667,
     "recall-0.25": 1.0,
     "recall-0.5": 1.0,
@@ -177,15 +177,15 @@ Results of the model in various experiments on different datasets.
   },
   "hip_right": {
     "count": 600,
-    "mean": 0.112947,
-    "median": 0.112279,
-    "std": 0.026967,
-    "sem": 0.001102,
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+    "mean": 0.113405,
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+    "recall-0.1": 0.236667,
     "recall-0.15": 0.946667,
     "recall-0.25": 1.0,
     "recall-0.5": 1.0,
@@ -193,82 +193,82 @@ Results of the model in various experiments on different datasets.
   },
   "knee_left": {
     "count": 600,
-    "mean": 0.061189,
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+    "recall-0.05": 0.593333,
+    "recall-0.1": 0.921667,
+    "recall-0.15": 0.93,
     "recall-0.25": 0.981667,
     "recall-0.5": 1.0,
     "num_labels": 600
   },
   "knee_right": {
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   },
   "ankle_left": {
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   "joint_recalls": {
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+    "recall-0.25": 0.99397,
     "recall-0.5": 0.99923
   }
 }
 {
   "total_parts": 8400,
-  "correct_parts": 8084,
-  "pcp": 0.962381
+  "correct_parts": 8094,
+  "pcp": 0.963571
 }
 ```
 
@@ -276,90 +276,90 @@ Results of the model in various experiments on different datasets.
 
 ```json
 {
-  "avg_time_2d": 0.08998977933143013,
-  "avg_time_3d": 0.0006411935865264578,
-  "avg_fps": 11.033755545195868
+  "avg_time_2d": 0.025720739692347155,
+  "avg_time_3d": 0.0007782072545736516,
+  "avg_fps": 37.737348657781304
 }
 {
   "person_nums": {
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+    "total_preds": 828,
     "considered_empty": 0,
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-    "invalid_preds": 337,
+    "invalid_preds": 351,
     "missing": 0,
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+    "invalid_fraction": 0.42391,
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     "recall": 1.0,
-    "f1": 0.73896,
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+    "f1": 0.73103,
+    "non_empty": 828
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+    "ap-0.25": 0.712076,
+    "ap-0.5": 0.712076
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   "head": {
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@@ -367,31 +367,31 @@ Results of the model in various experiments on different datasets.
   },
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+    "recall-0.1": 0.90566,
     "recall-0.15": 0.958071,
     "recall-0.25": 1.0,
     "recall-0.5": 1.0,
@@ -399,47 +399,47 @@ Results of the model in various experiments on different datasets.
   },
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-  "correct_parts": 4958,
-  "pcp": 0.941869
+  "correct_parts": 4973,
+  "pcp": 0.944719
 }
 ```
 
@@ -816,205 +816,205 @@ Results of the model in various experiments on different datasets.
 
 ```json
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+  "avg_time_3d": 0.0004397906086171386,
+  "avg_fps": 78.97827313868461
 }
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 {
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-  "correct_parts": 5850,
-  "pcp": 0.551367
+  "correct_parts": 6084,
+  "pcp": 0.573421
 }
 ```
 
@@ -1022,269 +1022,269 @@ Results of the model in various experiments on different datasets.
 
 ```json
 {
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 }
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   "total_parts": 20444,
-  "correct_parts": 19972,
-  "pcp": 0.976913
+  "correct_parts": 19987,
+  "pcp": 0.977646
 }
 ```
 
@@ -1292,269 +1292,269 @@ Results of the model in various experiments on different datasets.
 
 ```json
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+  "avg_fps": 50.90373948447781
 }
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 }
 ```
 
@@ -1562,57 +1562,57 @@ Results of the model in various experiments on different datasets.
 
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@@ -1621,14 +1621,14 @@ Results of the model in various experiments on different datasets.
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@@ -1637,14 +1637,14 @@ Results of the model in various experiments on different datasets.
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@@ -1653,15 +1653,15 @@ Results of the model in various experiments on different datasets.
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@@ -1669,63 +1669,63 @@ Results of the model in various experiments on different datasets.
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@@ -1733,98 +1733,98 @@ Results of the model in various experiments on different datasets.
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 }
 {
   "total_parts": 20444,
-  "correct_parts": 20225,
-  "pcp": 0.989288
+  "correct_parts": 20259,
+  "pcp": 0.990951
 }
 ```
 
@@ -1833,269 +1833,269 @@ Results of the model in various experiments on different datasets.
 (duration 00:01:45)
 ```json
 {
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-  "avg_time_3d": 0.00031231766316428114,
-  "avg_fps": 21.39832766737141
+  "avg_time_2d": 0.013423044290115585,
+  "avg_time_3d": 0.0004526440268640579,
+  "avg_fps": 72.06849686702047
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+  "correct_parts": 3712,
+  "pcp": 0.860654
 }
 ```
 
@@ -2103,269 +2103,269 @@ Results of the model in various experiments on different datasets.
 
 ```json
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+  "avg_fps": 64.94449504580058
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 }
 ```
 
@@ -2374,74 +2374,74 @@ Results of the model in various experiments on different datasets.
 (duration 00:01:51)
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@@ -2449,15 +2449,15 @@ Results of the model in various experiments on different datasets.
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@@ -2465,63 +2465,63 @@ Results of the model in various experiments on different datasets.
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@@ -2529,31 +2529,31 @@ Results of the model in various experiments on different datasets.
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@@ -2561,82 +2561,82 @@ Results of the model in various experiments on different datasets.
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 }
 ```
 
@@ -2646,9 +2646,9 @@ Results of the model in various experiments on different datasets.
 
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 }
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@@ -2721,14 +2721,14 @@ Results of the model in various experiments on different datasets.
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@@ -2737,30 +2737,30 @@ Results of the model in various experiments on different datasets.
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@@ -2769,30 +2769,30 @@ Results of the model in various experiments on different datasets.
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@@ -2801,14 +2801,14 @@ Results of the model in various experiments on different datasets.
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@@ -2817,14 +2817,14 @@ Results of the model in various experiments on different datasets.
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@@ -2833,45 +2833,45 @@ Results of the model in various experiments on different datasets.
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@@ -2881,13 +2881,13 @@ Results of the model in various experiments on different datasets.
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@@ -2897,18 +2897,18 @@ Results of the model in various experiments on different datasets.
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+  "correct_parts": 2792,
+  "pcp": 0.997143
 }
 ```
 
@@ -5188,269 +5188,269 @@ Results of the model in various experiments on different datasets.
 
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+  "avg_fps": 22.802308480320526
 }
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 }
 ```
 
@@ -5458,9 +5458,9 @@ Results of the model in various experiments on different datasets.
 
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@@ -5501,14 +5501,14 @@ Results of the model in various experiments on different datasets.
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@@ -5533,14 +5533,14 @@ Results of the model in various experiments on different datasets.
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@@ -5565,15 +5565,15 @@ Results of the model in various experiments on different datasets.
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@@ -5597,15 +5597,15 @@ Results of the model in various experiments on different datasets.
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@@ -5613,14 +5613,14 @@ Results of the model in various experiments on different datasets.
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@@ -5709,10 +5709,10 @@ Results of the model in various experiments on different datasets.
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@@ -5728,58 +5728,58 @@ Results of the model in various experiments on different datasets.
 
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@@ -5787,15 +5787,15 @@ Results of the model in various experiments on different datasets.
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@@ -5803,15 +5803,15 @@ 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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@@ -5899,14 +5899,14 @@ Results of the model in various experiments on different datasets.
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@@ -5915,14 +5915,14 @@ Results of the model in various experiments on different datasets.
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@@ -5931,15 +5931,15 @@ Results of the model in various experiments on different datasets.
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@@ -5947,14 +5947,14 @@ 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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@@ -5979,9 +5979,9 @@ Results of the model in various experiments on different datasets.
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@@ -5998,249 +5998,249 @@ Results of the model in various experiments on different datasets.
 
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@@ -6249,18 +6249,18 @@ Results of the model in various experiments on different datasets.
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-  "pcp": 0.990693
+  "correct_parts": 4587,
+  "pcp": 0.992857
 }
 ```
 
@@ -6268,269 +6268,269 @@ Results of the model in various experiments on different datasets.
 
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-  "pcp": 0.996901
+  "correct_parts": 6722,
+  "pcp": 0.992031
 }
 ```
 
@@ -6538,269 +6538,269 @@ Results of the model in various experiments on different datasets.
 
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   "joint_recalls": {
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-    "recall-0.25": 0.99884,
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+    "recall-0.025": 0.54214,
+    "recall-0.05": 0.91015,
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+    "recall-0.25": 0.9959,
+    "recall-0.5": 0.9959
   }
 }
 {
   "total_parts": 10248,
-  "correct_parts": 10233,
-  "pcp": 0.998536
+  "correct_parts": 10194,
+  "pcp": 0.994731
 }
 ```
 
@@ -6808,58 +6808,58 @@ Results of the model in various experiments on different datasets.
 
 ```json
 {
-  "avg_time_2d": 0.2932176687564069,
-  "avg_time_3d": 0.0019383890586986876,
-  "avg_fps": 3.3880382039335584
+  "avg_time_2d": 0.06536485716613413,
+  "avg_time_3d": 0.0028059914795278804,
+  "avg_fps": 14.669026715477658
 }
 {
   "person_nums": {
     "total_frames": 181,
     "total_labels": 362,
-    "total_preds": 362,
+    "total_preds": 363,
     "considered_empty": 0,
     "valid_preds": 362,
-    "invalid_preds": 0,
+    "invalid_preds": 1,
     "missing": 0,
-    "invalid_fraction": 0.0,
-    "precision": 1.0,
+    "invalid_fraction": 0.00275,
+    "precision": 0.99725,
     "recall": 1.0,
-    "f1": 1.0,
-    "non_empty": 362
+    "f1": 0.99862,
+    "non_empty": 363
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   "mpjpe": {
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+    "median": 0.023909,
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+    "sem": 0.000502,
+    "min": 0.014679,
+    "max": 0.132619,
+    "recall-0.025": 0.616022,
+    "recall-0.05": 0.977901,
     "recall-0.1": 0.997238,
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     "recall-0.25": 1.0,
     "recall-0.5": 1.0,
     "num_labels": 362,
-    "ap-0.025": 0.435297,
-    "ap-0.05": 0.985784,
-    "ap-0.1": 0.997131,
+    "ap-0.025": 0.455631,
+    "ap-0.05": 0.975503,
+    "ap-0.1": 0.997184,
     "ap-0.15": 1.0,
     "ap-0.25": 1.0,
     "ap-0.5": 1.0
   },
   "head": {
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-    "mean": 0.037086,
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-    "sem": 0.000775,
-    "min": 0.002558,
-    "max": 0.124266,
-    "recall-0.025": 0.190608,
+    "mean": 0.038431,
+    "median": 0.038664,
+    "std": 0.014854,
+    "sem": 0.000782,
+    "min": 0.005309,
+    "max": 0.137357,
+    "recall-0.025": 0.168508,
     "recall-0.05": 0.831492,
-    "recall-0.1": 0.991713,
+    "recall-0.1": 0.997238,
     "recall-0.15": 1.0,
     "recall-0.25": 1.0,
     "recall-0.5": 1.0,
@@ -6867,31 +6867,31 @@ Results of the model in various experiments on different datasets.
   },
   "shoulder_left": {
     "count": 362,
-    "mean": 0.024422,
-    "median": 0.021056,
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-    "min": 0.002657,
-    "max": 0.179202,
-    "recall-0.025": 0.60221,
-    "recall-0.05": 0.958564,
+    "mean": 0.026983,
+    "median": 0.02263,
+    "std": 0.015206,
+    "sem": 0.0008,
+    "min": 0.00387,
+    "max": 0.126938,
+    "recall-0.025": 0.58011,
+    "recall-0.05": 0.917127,
     "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
   },
   "shoulder_right": {
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-    "mean": 0.03084,
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-    "min": 0.005008,
-    "max": 0.118173,
-    "recall-0.025": 0.38674,
-    "recall-0.05": 0.900552,
-    "recall-0.1": 0.997238,
+    "mean": 0.034054,
+    "median": 0.031876,
+    "std": 0.015398,
+    "sem": 0.00081,
+    "min": 0.004182,
+    "max": 0.141592,
+    "recall-0.025": 0.273481,
+    "recall-0.05": 0.881215,
+    "recall-0.1": 0.994475,
     "recall-0.15": 1.0,
     "recall-0.25": 1.0,
     "recall-0.5": 1.0,
@@ -6899,46 +6899,46 @@ Results of the model in various experiments on different datasets.
   },
   "elbow_left": {
     "count": 362,
-    "mean": 0.019183,
-    "median": 0.015594,
-    "std": 0.013393,
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-    "max": 0.11991,
-    "recall-0.025": 0.779006,
-    "recall-0.05": 0.966851,
+    "mean": 0.020792,
+    "median": 0.016891,
+    "std": 0.015473,
+    "sem": 0.000814,
+    "min": 0.002343,
+    "max": 0.158617,
+    "recall-0.025": 0.751381,
+    "recall-0.05": 0.953039,
     "recall-0.1": 0.997238,
-    "recall-0.15": 1.0,
+    "recall-0.15": 0.997238,
     "recall-0.25": 1.0,
     "recall-0.5": 1.0,
     "num_labels": 362
   },
   "elbow_right": {
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-    "max": 0.154583,
-    "recall-0.025": 0.696133,
-    "recall-0.05": 0.972376,
+    "mean": 0.024753,
+    "median": 0.020827,
+    "std": 0.020256,
+    "sem": 0.001066,
+    "min": 0.002198,
+    "max": 0.20548,
+    "recall-0.025": 0.643646,
+    "recall-0.05": 0.950276,
     "recall-0.1": 0.986188,
-    "recall-0.15": 0.997238,
+    "recall-0.15": 0.994475,
     "recall-0.25": 1.0,
     "recall-0.5": 1.0,
     "num_labels": 362
   },
   "wrist_left": {
     "count": 362,
-    "mean": 0.024056,
-    "median": 0.021277,
-    "std": 0.014391,
-    "sem": 0.000757,
-    "min": 0.003255,
-    "max": 0.125608,
+    "mean": 0.024482,
+    "median": 0.021036,
+    "std": 0.015509,
+    "sem": 0.000816,
+    "min": 0.002882,
+    "max": 0.120801,
     "recall-0.025": 0.60221,
-    "recall-0.05": 0.953039,
+    "recall-0.05": 0.941989,
     "recall-0.1": 0.997238,
     "recall-0.15": 1.0,
     "recall-0.25": 1.0,
@@ -6947,47 +6947,47 @@ Results of the model in various experiments on different datasets.
   },
   "wrist_right": {
     "count": 362,
-    "mean": 0.028766,
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-    "std": 0.029596,
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-    "max": 0.317639,
-    "recall-0.025": 0.58011,
-    "recall-0.05": 0.883978,
+    "mean": 0.030548,
+    "median": 0.023797,
+    "std": 0.027228,
+    "sem": 0.001433,
+    "min": 0.003852,
+    "max": 0.29761,
+    "recall-0.025": 0.530387,
+    "recall-0.05": 0.867403,
     "recall-0.1": 0.986188,
-    "recall-0.15": 0.98895,
+    "recall-0.15": 0.994475,
     "recall-0.25": 0.994475,
     "recall-0.5": 1.0,
     "num_labels": 362
   },
   "hip_left": {
     "count": 362,
-    "mean": 0.025963,
-    "median": 0.023332,
-    "std": 0.014302,
-    "sem": 0.000753,
-    "min": 0.004395,
-    "max": 0.105448,
-    "recall-0.025": 0.549724,
-    "recall-0.05": 0.953039,
+    "mean": 0.026399,
+    "median": 0.023668,
+    "std": 0.015079,
+    "sem": 0.000794,
+    "min": 0.002841,
+    "max": 0.157722,
+    "recall-0.025": 0.524862,
+    "recall-0.05": 0.955801,
     "recall-0.1": 0.997238,
-    "recall-0.15": 1.0,
+    "recall-0.15": 0.997238,
     "recall-0.25": 1.0,
     "recall-0.5": 1.0,
     "num_labels": 362
   },
   "hip_right": {
     "count": 362,
-    "mean": 0.027134,
-    "median": 0.023912,
-    "std": 0.01681,
-    "sem": 0.000885,
-    "min": 0.001672,
-    "max": 0.201707,
-    "recall-0.025": 0.530387,
-    "recall-0.05": 0.947514,
-    "recall-0.1": 0.994475,
+    "mean": 0.028132,
+    "median": 0.026271,
+    "std": 0.0167,
+    "sem": 0.000879,
+    "min": 0.00458,
+    "max": 0.218165,
+    "recall-0.025": 0.453039,
+    "recall-0.05": 0.925414,
+    "recall-0.1": 0.997238,
     "recall-0.15": 0.997238,
     "recall-0.25": 1.0,
     "recall-0.5": 1.0,
@@ -6995,14 +6995,14 @@ Results of the model in various experiments on different datasets.
   },
   "knee_left": {
     "count": 362,
-    "mean": 0.014274,
-    "median": 0.012875,
-    "std": 0.007508,
+    "mean": 0.014559,
+    "median": 0.012687,
+    "std": 0.007514,
     "sem": 0.000395,
-    "min": 0.00133,
-    "max": 0.047529,
-    "recall-0.025": 0.91989,
-    "recall-0.05": 1.0,
+    "min": 0.000961,
+    "max": 0.053512,
+    "recall-0.025": 0.89779,
+    "recall-0.05": 0.997238,
     "recall-0.1": 1.0,
     "recall-0.15": 1.0,
     "recall-0.25": 1.0,
@@ -7011,14 +7011,14 @@ Results of the model in various experiments on different datasets.
   },
   "knee_right": {
     "count": 362,
-    "mean": 0.015968,
-    "median": 0.013439,
-    "std": 0.016304,
-    "sem": 0.000858,
-    "min": 0.002223,
-    "max": 0.259639,
-    "recall-0.025": 0.88674,
-    "recall-0.05": 0.986188,
+    "mean": 0.016592,
+    "median": 0.014442,
+    "std": 0.01703,
+    "sem": 0.000896,
+    "min": 0.001216,
+    "max": 0.268298,
+    "recall-0.025": 0.892265,
+    "recall-0.05": 0.991713,
     "recall-0.1": 0.994475,
     "recall-0.15": 0.997238,
     "recall-0.25": 0.997238,
@@ -7027,15 +7027,15 @@ Results of the model in various experiments on different datasets.
   },
   "ankle_left": {
     "count": 362,
-    "mean": 0.027315,
-    "median": 0.025773,
-    "std": 0.010493,
-    "sem": 0.000552,
-    "min": 0.006963,
-    "max": 0.072852,
-    "recall-0.025": 0.444751,
-    "recall-0.05": 0.961326,
-    "recall-0.1": 1.0,
+    "mean": 0.0244,
+    "median": 0.022601,
+    "std": 0.011282,
+    "sem": 0.000594,
+    "min": 0.004563,
+    "max": 0.104821,
+    "recall-0.025": 0.604972,
+    "recall-0.05": 0.969613,
+    "recall-0.1": 0.997238,
     "recall-0.15": 1.0,
     "recall-0.25": 1.0,
     "recall-0.5": 1.0,
@@ -7043,26 +7043,26 @@ Results of the model in various experiments on different datasets.
   },
   "ankle_right": {
     "count": 362,
-    "mean": 0.025082,
-    "median": 0.021269,
-    "std": 0.02841,
-    "sem": 0.001495,
-    "min": 0.001559,
-    "max": 0.492964,
-    "recall-0.025": 0.624309,
-    "recall-0.05": 0.964088,
+    "mean": 0.022289,
+    "median": 0.018322,
+    "std": 0.027775,
+    "sem": 0.001462,
+    "min": 0.000525,
+    "max": 0.481108,
+    "recall-0.025": 0.726519,
+    "recall-0.05": 0.961326,
     "recall-0.1": 0.994475,
-    "recall-0.15": 0.994475,
+    "recall-0.15": 0.997238,
     "recall-0.25": 0.997238,
     "recall-0.5": 1.0,
     "num_labels": 362
   },
   "joint_recalls": {
     "num_labels": 4706,
-    "recall-0.025": 0.59839,
-    "recall-0.05": 0.94411,
-    "recall-0.1": 0.99426,
-    "recall-0.15": 0.99745,
+    "recall-0.025": 0.5867,
+    "recall-0.05": 0.93264,
+    "recall-0.1": 0.99448,
+    "recall-0.15": 0.99766,
     "recall-0.25": 0.99894,
     "recall-0.5": 1.0
   }
diff --git a/scripts/test_skelda_dataset.py b/scripts/test_skelda_dataset.py
index ae7eb09..cc0b61c 100644
--- a/scripts/test_skelda_dataset.py
+++ b/scripts/test_skelda_dataset.py
@@ -17,8 +17,8 @@ import rpt
 
 # ==================================================================================================
 
-# dataset_use = "panoptic"
 dataset_use = "human36m"
+# dataset_use = "panoptic"
 # dataset_use = "mvor"
 # dataset_use = "shelf"
 # dataset_use = "campus"
@@ -26,8 +26,43 @@ dataset_use = "human36m"
 # dataset_use = "chi3d"
 # dataset_use = "tsinghua"
 # dataset_use = "human36m_wb"
-# dataset_use = "egohumans"
+# dataset_use = "egohumans_tagging"
+# dataset_use = "egohumans_legoassemble"
+# dataset_use = "egohumans_fencing"
+# dataset_use = "egohumans_basketball"
+# dataset_use = "egohumans_volleyball"
+# dataset_use = "egohumans_badminton"
+# dataset_use = "egohumans_tennis"
+# dataset_use = "ntu"
+# dataset_use = "koarob"
+
+
+# Describes the minimum area as fraction of the image size for a 2D bounding box to be considered
+# If the persons are small in the image, use a lower value
+default_min_bbox_area = 0.1 * 0.1
+
+# Describes how confident a 2D bounding box needs to be to be considered
+# If the persons are small in the image, or poorly recognizable, use a lower value
+default_min_bbox_score = 0.3
+
+# Describes how good two 2D poses need to match each other to create a valid triangulation
+# If the quality of the 2D detections is poor, use a lower value
+default_min_match_score = 0.94
+
+# Describes the minimum number of camera pairs that need to detect the same person
+# If the number of cameras is high, and the views are not occluded, use a higher value
+default_min_group_size = 1
+
+
 datasets = {
+    "human36m": {
+        "path": "/datasets/human36m/skelda/pose_test.json",
+        "take_interval": 5,
+        "min_match_score": 0.94,
+        "min_group_size": 1,
+        "min_bbox_score": 0.4,
+        "min_bbox_area": 0.1 * 0.1,
+    },
     "panoptic": {
         "path": "/datasets/panoptic/skelda/test.json",
         "cams": ["00_03", "00_06", "00_12", "00_13", "00_23"],
@@ -35,27 +70,33 @@ datasets = {
         # "cams": ["00_03", "00_06", "00_12", "00_13", "00_23", "00_15", "00_10", "00_21", "00_09", "00_01"],
         "take_interval": 3,
         "use_scenes": ["160906_pizza1", "160422_haggling1", "160906_ian5"],
-    },
-    "human36m": {
-        "path": "/datasets/human36m/skelda/pose_test.json",
-        "take_interval": 5,
+        "min_group_size": 1,
+        # "min_group_size": 4,
+        "min_bbox_area": 0.05 * 0.05,
     },
     "mvor": {
         "path": "/datasets/mvor/skelda/all.json",
         "take_interval": 1,
         "with_depth": False,
+        "min_match_score": 0.85,
+        "min_bbox_score": 0.25,
     },
     "campus": {
         "path": "/datasets/campus/skelda/test.json",
         "take_interval": 1,
+        "min_bbox_score": 0.5,
     },
     "shelf": {
         "path": "/datasets/shelf/skelda/test.json",
         "take_interval": 1,
+        "min_match_score": 0.96,
+        "min_group_size": 2,
     },
     "ikeaasm": {
         "path": "/datasets/ikeaasm/skelda/test.json",
         "take_interval": 2,
+        "min_match_score": 0.92,
+        "min_bbox_score": 0.20,
     },
     "chi3d": {
         "path": "/datasets/chi3d/skelda/all.json",
@@ -64,21 +105,65 @@ datasets = {
     "tsinghua": {
         "path": "/datasets/tsinghua/skelda/test.json",
         "take_interval": 3,
+        "min_group_size": 2,
     },
     "human36m_wb": {
         "path": "/datasets/human36m/skelda/wb/test.json",
         "take_interval": 100,
+        "min_bbox_score": 0.4,
     },
-    "egohumans": {
+    "egohumans_tagging": {
+        "path": "/datasets/egohumans/skelda/all.json",
+        "take_interval": 2,
+        "subset": "tagging",
+        "min_group_size": 2,
+        "min_bbox_score": 0.25,
+        "min_bbox_area": 0.05 * 0.05,
+    },
+    "egohumans_legoassemble": {
         "path": "/datasets/egohumans/skelda/all.json",
         "take_interval": 2,
-        # "subset": "tagging",
         "subset": "legoassemble",
-        # "subset": "fencing",
-        # "subset": "basketball",
-        # "subset": "volleyball",
-        # "subset": "badminton",
-        # "subset": "tennis",
+        "min_group_size": 2,
+    },
+    "egohumans_fencing": {
+        "path": "/datasets/egohumans/skelda/all.json",
+        "take_interval": 2,
+        "subset": "fencing",
+        "min_group_size": 7,
+        "min_bbox_score": 0.5,
+        "min_bbox_area": 0.05 * 0.05,
+    },
+    "egohumans_basketball": {
+        "path": "/datasets/egohumans/skelda/all.json",
+        "take_interval": 2,
+        "subset": "basketball",
+        "min_group_size": 7,
+        "min_bbox_score": 0.25,
+        "min_bbox_area": 0.025 * 0.025,
+    },
+    "egohumans_volleyball": {
+        "path": "/datasets/egohumans/skelda/all.json",
+        "take_interval": 2,
+        "subset": "volleyball",
+        "min_group_size": 11,
+        "min_bbox_score": 0.25,
+        "min_bbox_area": 0.05 * 0.05,
+    },
+    "egohumans_badminton": {
+        "path": "/datasets/egohumans/skelda/all.json",
+        "take_interval": 2,
+        "subset": "badminton",
+        "min_group_size": 7,
+        "min_bbox_score": 0.25,
+        "min_bbox_area": 0.05 * 0.05,
+    },
+    "egohumans_tennis": {
+        "path": "/datasets/egohumans/skelda/all.json",
+        "take_interval": 2,
+        "subset": "tennis",
+        "min_group_size": 11,
+        "min_bbox_area": 0.025 * 0.025,
     },
 }
 
@@ -102,8 +187,10 @@ eval_joints = [
 if dataset_use in ["human36m", "panoptic"]:
     eval_joints[eval_joints.index("head")] = "nose"
 if dataset_use.endswith("_wb"):
-    # eval_joints[eval_joints.index("head")] = "nose"
-    eval_joints = list(joint_names_2d)
+    if any((test_triangulate.whole_body.values())):
+        eval_joints = list(joint_names_2d)
+    else:
+        eval_joints[eval_joints.index("head")] = "nose"
 
 # output_dir = "/RapidPoseTriangulation/data/testoutput/"
 output_dir = ""
@@ -191,11 +278,11 @@ def load_labels(dataset: dict):
     elif "human36m_wb" in dataset:
         labels = load_json(dataset["human36m_wb"]["path"])
 
-    elif "egohumans" in dataset:
-        labels = load_json(dataset["egohumans"]["path"])
+    elif any(("egohumans" in key for key in dataset)):
+        labels = load_json(dataset[dataset_use]["path"])
         labels = [lb for lb in labels if "test" in lb["splits"]]
-        labels = [lb for lb in labels if dataset["egohumans"]["subset"] in lb["seq"]]
-        if dataset["egohumans"]["subset"] in ["volleyball", "tennis"]:
+        labels = [lb for lb in labels if dataset[dataset_use]["subset"] in lb["seq"]]
+        if dataset[dataset_use]["subset"] in ["volleyball", "tennis"]:
             labels = [lb for i, lb in enumerate(labels) if i % 150 < 60]
 
     else:
@@ -216,11 +303,20 @@ def load_labels(dataset: dict):
 def main():
     global joint_names_3d, eval_joints
 
+    # Load dataset specific parameters
+    min_match_score = datasets[dataset_use].get(
+        "min_match_score", default_min_match_score
+    )
+    min_group_size = datasets[dataset_use].get("min_group_size", default_min_group_size)
+    min_bbox_score = datasets[dataset_use].get("min_bbox_score", default_min_bbox_score)
+    min_bbox_area = datasets[dataset_use].get("min_bbox_area", default_min_bbox_area)
+
+    # Load 2D pose model
     whole_body = test_triangulate.whole_body
     if any((whole_body[k] for k in whole_body)):
         kpt_model = utils_2d_pose.load_wb_model()
     else:
-        kpt_model = utils_2d_pose.load_model()
+        kpt_model = utils_2d_pose.load_model(min_bbox_score, min_bbox_area)
 
     # Manually set matplotlib backend
     try:
@@ -239,50 +335,12 @@ def main():
     # Print a dataset sample for debugging
     print(labels[0])
 
-    minscores = {
-        # Describes how good two 2D poses need to match each other to create a valid triangulation
-        # If the quality of the 2D detections is poor, use a lower value
-        "panoptic": 0.94,
-        "human36m": 0.94,
-        "mvor": 0.86,
-        "campus": 0.96,
-        "shelf": 0.96,
-        "ikeaasm": 0.89,
-        "chi3d": 0.94,
-        "tsinghua": 0.96,
-        "egohumans": 0.95,
-        "human36m_wb": 0.94,
-    }
-    minscore = minscores.get(dataset_use, 0.95)
-    min_group_sizes = {
-        # Describes the minimum number of camera pairs that need to detect the same person
-        # If the number of cameras is high, and the views are not occluded, use a higher value
-        "panoptic": 1,
-        "shelf": 2,
-        "chi3d": 1,
-        "tsinghua": 2,
-        "egohumans": 4,
-    }
-    min_group_size = min_group_sizes.get(dataset_use, 1)
-    if dataset_use == "panoptic" and len(datasets["panoptic"]["cams"]) == 10:
-        min_group_size = 4
-    if dataset_use == "egohumans" and (
-        "lego" in labels[0]["seq"] or "tagging" in labels[0]["seq"]
-    ):
-        min_group_size = 2
-    if dataset_use == "egohumans" and (
-        "volleyball" in labels[0]["seq"] or "badminton" in labels[0]["seq"]
-    ):
-        min_group_size = 7
-    if dataset_use == "egohumans" and "tennis" in labels[0]["seq"]:
-        min_group_size = 11
-
     print("\nRunning predictions ...")
     all_poses = []
     all_ids = []
     times = []
     triangulator = rpt.Triangulator(
-        min_match_score=minscore, min_group_size=min_group_size
+        min_match_score=min_match_score, min_group_size=min_group_size
     )
     old_scene = ""
     old_index = -1