diff --git a/extras/mmdeploy/configs/detection_onnxruntime_static-320x320.py b/extras/mmdeploy/configs/detection_onnxruntime_static-320x320.py
index b9a9c4c..3918d65 100644
--- a/extras/mmdeploy/configs/detection_onnxruntime_static-320x320.py
+++ b/extras/mmdeploy/configs/detection_onnxruntime_static-320x320.py
@@ -7,12 +7,12 @@ onnx_config = dict(
 codebase_config = dict(
     # For later TensorRT inference, the number of output boxes needs to be as stable as possible, 
     # because a drop in the box count leads to a re-optimization which takes a lot of time,
-    # therefore sort out low confidence boxes outside the model and reduce the maximum number
-    # of output boxes to the smallest usable value.
+    # therefore reduce the maximum number of output boxes to the smallest usable value and sort out
+    # low confidence boxes outside the model.
     post_processing=dict(
         score_threshold=0.0,
         confidence_threshold=0.0,
-        iou_threshold=0.3,
+        iou_threshold=0.5,
         max_output_boxes_per_class=10,
     ),
 )
diff --git a/extras/mmdeploy/configs/detection_onnxruntime_static-320x320_fp16.py b/extras/mmdeploy/configs/detection_onnxruntime_static-320x320_fp16.py
index a5478be..a724f53 100644
--- a/extras/mmdeploy/configs/detection_onnxruntime_static-320x320_fp16.py
+++ b/extras/mmdeploy/configs/detection_onnxruntime_static-320x320_fp16.py
@@ -12,7 +12,7 @@ codebase_config = dict(
     post_processing=dict(
         score_threshold=0.0,
         confidence_threshold=0.0,
-        iou_threshold=0.3,
+        iou_threshold=0.5,
         max_output_boxes_per_class=10,
     ),
 )
diff --git a/media/RESULTS.md b/media/RESULTS.md
index 9c13741..7b1b616 100644
--- a/media/RESULTS.md
+++ b/media/RESULTS.md
@@ -6,74 +6,74 @@ Results of the model in various experiments on different datasets.
 
 ```json
 {
-  "avg_time_2d": 0.010846347323918747,
-  "avg_time_3d": 0.0003320467674126059,
-  "avg_fps": 89.45828817893282
+  "avg_time_2d": 0.010003441875263796,
+  "avg_time_3d": 0.0003245426436602059,
+  "avg_fps": 96.824312446218
 }
 {
   "person_nums": {
     "total_frames": 600,
     "total_labels": 600,
-    "total_preds": 601,
+    "total_preds": 600,
     "considered_empty": 0,
     "valid_preds": 600,
-    "invalid_preds": 1,
+    "invalid_preds": 0,
     "missing": 0,
-    "invalid_fraction": 0.00166,
-    "precision": 0.99834,
+    "invalid_fraction": 0.0,
+    "precision": 1.0,
     "recall": 1.0,
-    "f1": 0.99917,
-    "non_empty": 601
+    "f1": 1.0,
+    "non_empty": 600
   },
   "mpjpe": {
     "count": 600,
-    "mean": 0.066093,
-    "median": 0.058635,
-    "std": 0.027815,
-    "sem": 0.001136,
-    "min": 0.040333,
-    "max": 0.189198,
+    "mean": 0.067074,
+    "median": 0.058987,
+    "std": 0.027958,
+    "sem": 0.001142,
+    "min": 0.042414,
+    "max": 0.189648,
     "recall-0.025": 0.0,
-    "recall-0.05": 0.101667,
-    "recall-0.1": 0.938333,
+    "recall-0.05": 0.061667,
+    "recall-0.1": 0.93,
     "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": 0.023002,
-    "ap-0.1": 0.897991,
-    "ap-0.15": 0.914985,
+    "ap-0.05": 0.00503,
+    "ap-0.1": 0.887557,
+    "ap-0.15": 0.913732,
     "ap-0.25": 1.0,
     "ap-0.5": 1.0
   },
   "nose": {
     "count": 600,
-    "mean": 0.114181,
-    "median": 0.099121,
-    "std": 0.042396,
-    "sem": 0.001732,
-    "min": 0.029365,
-    "max": 0.287428,
+    "mean": 0.114519,
+    "median": 0.097973,
+    "std": 0.044206,
+    "sem": 0.001806,
+    "min": 0.025858,
+    "max": 0.292026,
     "recall-0.025": 0.0,
-    "recall-0.05": 0.011667,
-    "recall-0.1": 0.508333,
-    "recall-0.15": 0.801667,
-    "recall-0.25": 0.991667,
+    "recall-0.05": 0.015,
+    "recall-0.1": 0.52,
+    "recall-0.15": 0.816667,
+    "recall-0.25": 0.988333,
     "recall-0.5": 1.0,
     "num_labels": 600
   },
   "shoulder_left": {
     "count": 600,
-    "mean": 0.03478,
-    "median": 0.026496,
-    "std": 0.031647,
-    "sem": 0.001293,
-    "min": 0.003155,
-    "max": 0.183779,
-    "recall-0.025": 0.455,
-    "recall-0.05": 0.853333,
-    "recall-0.1": 0.95,
+    "mean": 0.034466,
+    "median": 0.025369,
+    "std": 0.032528,
+    "sem": 0.001329,
+    "min": 0.002782,
+    "max": 0.182086,
+    "recall-0.025": 0.483333,
+    "recall-0.05": 0.863333,
+    "recall-0.1": 0.941667,
     "recall-0.15": 0.966667,
     "recall-0.25": 1.0,
     "recall-0.5": 1.0,
@@ -81,95 +81,95 @@ Results of the model in various experiments on different datasets.
   },
   "shoulder_right": {
     "count": 600,
-    "mean": 0.047867,
-    "median": 0.034293,
-    "std": 0.039619,
-    "sem": 0.001619,
-    "min": 0.005688,
-    "max": 0.254393,
-    "recall-0.025": 0.218333,
+    "mean": 0.048171,
+    "median": 0.03483,
+    "std": 0.040889,
+    "sem": 0.001671,
+    "min": 0.003841,
+    "max": 0.258489,
+    "recall-0.025": 0.221667,
     "recall-0.05": 0.751667,
     "recall-0.1": 0.913333,
-    "recall-0.15": 0.95,
+    "recall-0.15": 0.945,
     "recall-0.25": 0.998333,
     "recall-0.5": 1.0,
     "num_labels": 600
   },
   "elbow_left": {
     "count": 600,
-    "mean": 0.044022,
-    "median": 0.035159,
-    "std": 0.034701,
-    "sem": 0.001418,
-    "min": 0.002814,
-    "max": 0.194526,
-    "recall-0.025": 0.233333,
-    "recall-0.05": 0.771667,
-    "recall-0.1": 0.943333,
-    "recall-0.15": 0.958333,
+    "mean": 0.043039,
+    "median": 0.03493,
+    "std": 0.034865,
+    "sem": 0.001425,
+    "min": 0.002006,
+    "max": 0.197281,
+    "recall-0.025": 0.248333,
+    "recall-0.05": 0.805,
+    "recall-0.1": 0.941667,
+    "recall-0.15": 0.955,
     "recall-0.25": 1.0,
     "recall-0.5": 1.0,
     "num_labels": 600
   },
   "elbow_right": {
     "count": 600,
-    "mean": 0.04408,
-    "median": 0.033951,
-    "std": 0.036319,
-    "sem": 0.001484,
-    "min": 0.008171,
-    "max": 0.360134,
-    "recall-0.025": 0.265,
-    "recall-0.05": 0.78,
-    "recall-0.1": 0.933333,
-    "recall-0.15": 0.946667,
-    "recall-0.25": 0.998333,
+    "mean": 0.044694,
+    "median": 0.032396,
+    "std": 0.03821,
+    "sem": 0.001561,
+    "min": 0.005657,
+    "max": 0.367138,
+    "recall-0.025": 0.24,
+    "recall-0.05": 0.791667,
+    "recall-0.1": 0.928333,
+    "recall-0.15": 0.943333,
+    "recall-0.25": 0.996667,
     "recall-0.5": 1.0,
     "num_labels": 600
   },
   "wrist_left": {
     "count": 600,
-    "mean": 0.043753,
-    "median": 0.027211,
-    "std": 0.044668,
-    "sem": 0.001825,
-    "min": 0.002715,
-    "max": 0.190751,
-    "recall-0.025": 0.46,
-    "recall-0.05": 0.74,
-    "recall-0.1": 0.891667,
-    "recall-0.15": 0.925,
-    "recall-0.25": 1.0,
+    "mean": 0.043228,
+    "median": 0.024022,
+    "std": 0.047501,
+    "sem": 0.001941,
+    "min": 0.002332,
+    "max": 0.283113,
+    "recall-0.025": 0.52,
+    "recall-0.05": 0.746667,
+    "recall-0.1": 0.885,
+    "recall-0.15": 0.92,
+    "recall-0.25": 0.996667,
     "recall-0.5": 1.0,
     "num_labels": 600
   },
   "wrist_right": {
-    "count": 600,
-    "mean": 0.046553,
-    "median": 0.026979,
-    "std": 0.050263,
-    "sem": 0.002054,
-    "min": 0.003364,
-    "max": 0.244861,
-    "recall-0.025": 0.46,
-    "recall-0.05": 0.733333,
-    "recall-0.1": 0.87,
+    "count": 599,
+    "mean": 0.047526,
+    "median": 0.027369,
+    "std": 0.055131,
+    "sem": 0.002254,
+    "min": 0.001,
+    "max": 0.492857,
+    "recall-0.025": 0.451667,
+    "recall-0.05": 0.74,
+    "recall-0.1": 0.873333,
     "recall-0.15": 0.906667,
-    "recall-0.25": 1.0,
-    "recall-0.5": 1.0,
+    "recall-0.25": 0.991667,
+    "recall-0.5": 0.998333,
     "num_labels": 600
   },
   "hip_left": {
     "count": 600,
-    "mean": 0.08362,
-    "median": 0.077619,
-    "std": 0.032967,
-    "sem": 0.001347,
-    "min": 0.018157,
-    "max": 0.240771,
-    "recall-0.025": 0.005,
-    "recall-0.05": 0.055,
-    "recall-0.1": 0.848333,
+    "mean": 0.089504,
+    "median": 0.085316,
+    "std": 0.032919,
+    "sem": 0.001345,
+    "min": 0.011484,
+    "max": 0.236463,
+    "recall-0.025": 0.006667,
+    "recall-0.05": 0.031667,
+    "recall-0.1": 0.815,
     "recall-0.15": 0.951667,
     "recall-0.25": 1.0,
     "recall-0.5": 1.0,
@@ -177,98 +177,98 @@ Results of the model in various experiments on different datasets.
   },
   "hip_right": {
     "count": 600,
-    "mean": 0.106567,
-    "median": 0.104243,
-    "std": 0.026243,
-    "sem": 0.001072,
-    "min": 0.035565,
-    "max": 0.245341,
+    "mean": 0.112947,
+    "median": 0.112279,
+    "std": 0.026967,
+    "sem": 0.001102,
+    "min": 0.041373,
+    "max": 0.235641,
     "recall-0.025": 0.0,
-    "recall-0.05": 0.003333,
-    "recall-0.1": 0.415,
+    "recall-0.05": 0.01,
+    "recall-0.1": 0.245,
     "recall-0.15": 0.946667,
     "recall-0.25": 1.0,
     "recall-0.5": 1.0,
     "num_labels": 600
   },
   "knee_left": {
-    "count": 599,
-    "mean": 0.063278,
-    "median": 0.047513,
-    "std": 0.056978,
-    "sem": 0.00233,
-    "min": 0.017587,
-    "max": 0.4004,
-    "recall-0.025": 0.038333,
-    "recall-0.05": 0.546667,
-    "recall-0.1": 0.883333,
-    "recall-0.15": 0.925,
-    "recall-0.25": 0.978333,
-    "recall-0.5": 0.998333,
+    "count": 600,
+    "mean": 0.061189,
+    "median": 0.045843,
+    "std": 0.0566,
+    "sem": 0.002313,
+    "min": 0.012587,
+    "max": 0.400213,
+    "recall-0.025": 0.05,
+    "recall-0.05": 0.58,
+    "recall-0.1": 0.91,
+    "recall-0.15": 0.926667,
+    "recall-0.25": 0.981667,
+    "recall-0.5": 1.0,
     "num_labels": 600
   },
   "knee_right": {
     "count": 600,
-    "mean": 0.050742,
-    "median": 0.041408,
-    "std": 0.037974,
-    "sem": 0.001552,
-    "min": 0.01394,
-    "max": 0.279839,
-    "recall-0.025": 0.053333,
-    "recall-0.05": 0.75,
-    "recall-0.1": 0.941667,
-    "recall-0.15": 0.941667,
-    "recall-0.25": 0.996667,
+    "mean": 0.052612,
+    "median": 0.04423,
+    "std": 0.037278,
+    "sem": 0.001523,
+    "min": 0.01118,
+    "max": 0.249994,
+    "recall-0.025": 0.038333,
+    "recall-0.05": 0.736667,
+    "recall-0.1": 0.936667,
+    "recall-0.15": 0.94,
+    "recall-0.25": 1.0,
     "recall-0.5": 1.0,
     "num_labels": 600
   },
   "ankle_left": {
-    "count": 600,
-    "mean": 0.096717,
-    "median": 0.085484,
-    "std": 0.043279,
-    "sem": 0.001768,
-    "min": 0.050765,
-    "max": 0.49651,
+    "count": 598,
+    "mean": 0.095824,
+    "median": 0.084767,
+    "std": 0.048441,
+    "sem": 0.001983,
+    "min": 0.045599,
+    "max": 0.496625,
     "recall-0.025": 0.0,
-    "recall-0.05": 0.0,
-    "recall-0.1": 0.825,
-    "recall-0.15": 0.935,
-    "recall-0.25": 0.988333,
-    "recall-0.5": 1.0,
+    "recall-0.05": 0.003333,
+    "recall-0.1": 0.843333,
+    "recall-0.15": 0.94,
+    "recall-0.25": 0.981667,
+    "recall-0.5": 0.996667,
     "num_labels": 600
   },
   "ankle_right": {
-    "count": 600,
-    "mean": 0.08227,
-    "median": 0.068786,
-    "std": 0.049929,
-    "sem": 0.00204,
-    "min": 0.028705,
-    "max": 0.486848,
+    "count": 598,
+    "mean": 0.080368,
+    "median": 0.067762,
+    "std": 0.045136,
+    "sem": 0.001847,
+    "min": 0.031319,
+    "max": 0.490733,
     "recall-0.025": 0.0,
-    "recall-0.05": 0.033333,
-    "recall-0.1": 0.896667,
-    "recall-0.15": 0.916667,
-    "recall-0.25": 0.985,
-    "recall-0.5": 1.0,
+    "recall-0.05": 0.028333,
+    "recall-0.1": 0.89,
+    "recall-0.15": 0.913333,
+    "recall-0.25": 0.983333,
+    "recall-0.5": 0.996667,
     "num_labels": 600
   },
   "joint_recalls": {
     "num_labels": 7800,
-    "recall-0.025": 0.16782,
-    "recall-0.05": 0.46333,
-    "recall-0.1": 0.83154,
-    "recall-0.15": 0.92846,
-    "recall-0.25": 0.99462,
-    "recall-0.5": 0.99974
+    "recall-0.025": 0.17346,
+    "recall-0.05": 0.4691,
+    "recall-0.1": 0.81808,
+    "recall-0.15": 0.92833,
+    "recall-0.25": 0.99333,
+    "recall-0.5": 0.99923
   }
 }
 {
   "total_parts": 8400,
-  "correct_parts": 8111,
-  "pcp": 0.965595
+  "correct_parts": 8084,
+  "pcp": 0.962381
 }
 ```
 
diff --git a/scripts/utils_2d_pose_ort.py b/scripts/utils_2d_pose_ort.py
index 88787d9..0cbc875 100644
--- a/scripts/utils_2d_pose_ort.py
+++ b/scripts/utils_2d_pose_ort.py
@@ -148,7 +148,7 @@ class LetterBox:
         resized_img = cv2.resize(
             image,
             (new_w, new_h),
-            interpolation=cv2.INTER_LINEAR,
+            interpolation=cv2.INTER_NEAREST,
         )
 
         # Optionally pad the image
@@ -273,7 +273,7 @@ class BoxCrop:
         resized_img = cv2.resize(
             cropped_img,
             (new_w, new_h),
-            interpolation=cv2.INTER_LINEAR,
+            interpolation=cv2.INTER_NEAREST,
         )
 
         # Optionally pad the image
@@ -309,6 +309,10 @@ class RTMDet(BaseModel):
         self.conf_threshold = conf_threshold
         self.letterbox = LetterBox(self.target_size, fill_value=114)
 
+        min_area_scale = 0.025 * 0.025
+        img_area = self.target_size[0] * self.target_size[1]
+        self.min_area = img_area * min_area_scale
+
     def preprocess(self, image: np.ndarray):
         image = self.letterbox.resize_image(image)
         tensor = np.asarray(image).astype(self.input_types[0], copy=False)
@@ -326,6 +330,11 @@ class RTMDet(BaseModel):
         keep = boxes[:, 4] > self.conf_threshold
         boxes = boxes[keep]
 
+        # Drop boxes with too small area
+        areas = (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1])
+        keep = areas >= self.min_area
+        boxes = boxes[keep]
+
         paddings, scale, _ = self.letterbox.calc_params(image.shape)
 
         boxes[:, 0] -= paddings[0]