diff --git a/depthai_nodes/ml/parsers/image_output.py b/depthai_nodes/ml/parsers/image_output.py
index e60bb09..be25053 100644
--- a/depthai_nodes/ml/parsers/image_output.py
+++ b/depthai_nodes/ml/parsers/image_output.py
@@ -58,7 +58,7 @@ def run(self):
                 raise ValueError(
                     f"Expected 1 output layer, got {len(output_layer_names)}."
                 )
-            output = output.getTensor(output_layer_names[0])
+            output = output.getTensor(output_layer_names[0], dequantize=True)
 
             if len(output.shape) == 4:
                 image = output[0]
diff --git a/depthai_nodes/ml/parsers/keypoints.py b/depthai_nodes/ml/parsers/keypoints.py
index a501ba1..07d93f9 100644
--- a/depthai_nodes/ml/parsers/keypoints.py
+++ b/depthai_nodes/ml/parsers/keypoints.py
@@ -87,7 +87,9 @@ def run(self):
                     f"Expected 1 output layer, got {len(output_layer_names)}."
                 )
 
-            keypoints = output.getTensor(output_layer_names[0])
+            keypoints = output.getTensor(output_layer_names[0], dequantize=True).astype(
+                np.float32
+            )
             num_coords = int(np.prod(keypoints.shape) / self.num_keypoints)
 
             if num_coords not in [2, 3]:
diff --git a/depthai_nodes/ml/parsers/mediapipe_hand_landmarker.py b/depthai_nodes/ml/parsers/mediapipe_hand_landmarker.py
index effbc0f..346b881 100644
--- a/depthai_nodes/ml/parsers/mediapipe_hand_landmarker.py
+++ b/depthai_nodes/ml/parsers/mediapipe_hand_landmarker.py
@@ -71,9 +71,21 @@ def run(self):
             except dai.MessageQueue.QueueException:
                 break  # Pipeline was stopped
 
-            landmarks = output.getTensor("Identity").reshape(21, 3).astype(np.float32)
-            hand_score = output.getTensor("Identity_1").reshape(-1).astype(np.float32)
-            handedness = output.getTensor("Identity_2").reshape(-1).astype(np.float32)
+            landmarks = (
+                output.getTensor("Identity", dequantize=True)
+                .reshape(21, 3)
+                .astype(np.float32)
+            )
+            hand_score = (
+                output.getTensor("Identity_1", dequantize=True)
+                .reshape(-1)
+                .astype(np.float32)
+            )
+            handedness = (
+                output.getTensor("Identity_2", dequantize=True)
+                .reshape(-1)
+                .astype(np.float32)
+            )
             hand_score = hand_score[0]
             handedness = handedness[0]
 
diff --git a/depthai_nodes/ml/parsers/mediapipe_palm_detection.py b/depthai_nodes/ml/parsers/mediapipe_palm_detection.py
index aeacd17..2f297d7 100644
--- a/depthai_nodes/ml/parsers/mediapipe_palm_detection.py
+++ b/depthai_nodes/ml/parsers/mediapipe_palm_detection.py
@@ -85,8 +85,16 @@ def run(self):
             except dai.MessageQueue.QueueException:
                 break  # Pipeline was stopped
 
-            bboxes = output.getTensor("Identity").reshape(2016, 18).astype(np.float32)
-            scores = output.getTensor("Identity_1").reshape(2016).astype(np.float32)
+            bboxes = (
+                output.getTensor("Identity", dequantize=True)
+                .reshape(2016, 18)
+                .astype(np.float32)
+            )
+            scores = (
+                output.getTensor("Identity_1", dequantize=True)
+                .reshape(2016)
+                .astype(np.float32)
+            )
 
             decoded_bboxes = generate_anchors_and_decode(
                 bboxes=bboxes, scores=scores, threshold=self.score_threshold, scale=192
diff --git a/depthai_nodes/ml/parsers/segmentation.py b/depthai_nodes/ml/parsers/segmentation.py
index d4f9e0b..e141c3a 100644
--- a/depthai_nodes/ml/parsers/segmentation.py
+++ b/depthai_nodes/ml/parsers/segmentation.py
@@ -62,9 +62,11 @@ def run(self):
                     f"Expected 1 output layer, got {len(output_layer_names)}."
                 )
 
-            segmentation_mask = output.getTensor(output_layer_names[0])[
-                0
-            ]  # num_clases x H x W
+            segmentation_mask = output.getTensor(output_layer_names[0], dequantize=True)
+            if len(segmentation_mask.shape) == 4:
+                segmentation_mask = segmentation_mask[0]
+            else:
+                segmentation_mask = segmentation_mask.transpose(2, 0, 1)
 
             if len(segmentation_mask.shape) != 3:
                 raise ValueError(
diff --git a/depthai_nodes/ml/parsers/superanimal_landmarker.py b/depthai_nodes/ml/parsers/superanimal_landmarker.py
index 9b0bdee..31ed393 100644
--- a/depthai_nodes/ml/parsers/superanimal_landmarker.py
+++ b/depthai_nodes/ml/parsers/superanimal_landmarker.py
@@ -68,7 +68,10 @@ def run(self):
             except dai.MessageQueue.QueueException:
                 break  # Pipeline was stopped
 
-            heatmaps = output.getTensor("heatmaps").astype(np.float32)
+            heatmaps = output.getTensor("heatmaps", dequantize=True).astype(np.float32)
+
+            if len(heatmaps.shape) == 3:
+                heatmaps = heatmaps.reshape((1,) + heatmaps.shape)
 
             heatmaps_scale_factor = (
                 self.scale_factor / heatmaps.shape[1],
diff --git a/depthai_nodes/ml/parsers/xfeat.py b/depthai_nodes/ml/parsers/xfeat.py
index f7ecf63..6d4a451 100644
--- a/depthai_nodes/ml/parsers/xfeat.py
+++ b/depthai_nodes/ml/parsers/xfeat.py
@@ -82,8 +82,17 @@ def run(self):
             except dai.MessageQueue.QueueException:
                 break  # Pipeline was stopped
 
-            feats = output.getTensor("feats").astype(np.float32)
-            keypoints = output.getTensor("keypoints").astype(np.float32)
+            feats = output.getTensor("feats", dequantize=True).astype(np.float32)
+            keypoints = output.getTensor("keypoints", dequantize=True).astype(
+                np.float32
+            )
+
+            if len(feats.shape) == 3:
+                feats = feats.reshape((1,) + feats.shape).transpose(0, 3, 1, 2)
+            if len(keypoints.shape) == 3:
+                keypoints = keypoints.reshape((1,) + keypoints.shape).transpose(
+                    0, 3, 1, 2
+                )
 
             result = detect_and_compute(
                 feats, keypoints, resize_rate_w, resize_rate_h, self.input_size