diff --git a/luxonis_train/attached_modules/losses/README.md b/luxonis_train/attached_modules/losses/README.md
index 38f8b42f..aa1b9ca6 100644
--- a/luxonis_train/attached_modules/losses/README.md
+++ b/luxonis_train/attached_modules/losses/README.md
@@ -118,6 +118,6 @@ Adapted from [here](https://arxiv.org/abs/2108.07610).
 
 **Parameters:**
 
-| Key             | Type    | Default value | Description                                     |
-| --------------- | ------- | ------------- | ----------------------------------------------- |
-| `object_weight` | `float` | `1000`        | Weight for the objects in the loss calculation. |
+| Key             | Type    | Default value | Description                                                                                                                                                                          |
+| --------------- | ------- | ------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| `object_weight` | `float` | `1000`        | Weight for the objects in the loss calculation. Training with a larger `object_weight` in the loss parameters may result in more false positives (FP), but it will improve accuracy. |
diff --git a/luxonis_train/config/predefined_models/README.md b/luxonis_train/config/predefined_models/README.md
index 9c6727fb..629b17a5 100644
--- a/luxonis_train/config/predefined_models/README.md
+++ b/luxonis_train/config/predefined_models/README.md
@@ -160,6 +160,8 @@ The `FOMOModel` allows for both `"light"` and `"heavy"` variants, where the `"he
 
 See an example configuration file using this predefined model [here](../../../configs/detection_fomo_light_model.yaml) for the `"light"` variant, and [here](../../../configs/detection_fomo_heavy_model.yaml) for the `"heavy"` variant.
 
+There is a trade-off in this simple model: training with a larger `object_weight` in the loss parameters may result in more false positives (FP), but it will improve accuracy. You can also use `use_nms: True` in the `head_params` to enable NMS which can reduce FP, but it will also reduce TP for close neighbors.
+
 ### **Components**
 
 | Name                                                                                            | Alias               | Function                                                                       |
diff --git a/luxonis_train/nodes/README.md b/luxonis_train/nodes/README.md
index 31f1f6c2..18beb17a 100644
--- a/luxonis_train/nodes/README.md
+++ b/luxonis_train/nodes/README.md
@@ -268,7 +268,8 @@ Adapted from [here](https://arxiv.org/abs/2108.07610).
 
 **Parameters:**
 
-| Key               | Type  | Default value | Description                                             |
-| ----------------- | ----- | ------------- | ------------------------------------------------------- |
-| `num_conv_layers` | `int` | `3`           | Number of convolutional layers to use in the model.     |
-| `conv_channels`   | `int` | `16`          | Number of output channels for each convolutional layer. |
+| Key               | Type   | Default value | Description                                                                              |
+| ----------------- | ------ | ------------- | ---------------------------------------------------------------------------------------- |
+| `num_conv_layers` | `int`  | `3`           | Number of convolutional layers to use in the model.                                      |
+| `conv_channels`   | `int`  | `16`          | Number of output channels for each convolutional layer.                                  |
+| `use_nms`         | `bool` | `False`       | If True, enable NMS. This can reduce FP, but it will also reduce TP for close neighbors. |
diff --git a/luxonis_train/nodes/heads/fomo_head.py b/luxonis_train/nodes/heads/fomo_head.py
index cc9ea1a0..ce63f975 100644
--- a/luxonis_train/nodes/heads/fomo_head.py
+++ b/luxonis_train/nodes/heads/fomo_head.py
@@ -21,6 +21,7 @@ def __init__(
         self,
         num_conv_layers: int = 3,
         conv_channels: int = 16,
+        use_nms: bool = False,
         **kwargs: Any,
     ):
         """FOMO Head for object detection using heatmaps.
@@ -37,6 +38,7 @@ def __init__(
         self.original_img_size = self.original_in_shape[1:]
         self.num_conv_layers = num_conv_layers
         self.conv_channels = conv_channels
+        self.use_nms = use_nms
 
         current_channels = self.in_channels
 
@@ -76,7 +78,11 @@ def wrap(self, heatmap: Tensor) -> Packet[Tensor]:
 
     def _heatmap_to_kpts(self, heatmap: Tensor) -> List[Tensor]:
         """Convert heatmap to keypoint pairs using local-max NMS so that
-        only the strongest local peak in a neighborhood is retained."""
+        only the strongest local peak in a neighborhood is retained.
+
+        @type heatmap: Tensor
+        @param heatmap: Heatmap to convert to keypoints.
+        """
         device = heatmap.device
         batch_size, num_classes, height, width = heatmap.shape
 
@@ -87,19 +93,25 @@ def _heatmap_to_kpts(self, heatmap: Tensor) -> List[Tensor]:
             for c in range(num_classes):
                 prob_map = torch.sigmoid(heatmap[batch_idx, c, :, :])
 
-                pooled_map = (
-                    F.max_pool2d(
-                        prob_map.unsqueeze(0).unsqueeze(0),  # shape [1,1,H,W]
-                        kernel_size=3,
-                        stride=1,
-                        padding=1,
-                    )
-                    .squeeze(0)
-                    .squeeze(0)
-                )  # back to [H,W]
-
-                threshold = 0.5
-                keep = (prob_map == pooled_map) & (prob_map > threshold)
+                if self.use_nms:
+                    pooled_map = (
+                        F.max_pool2d(
+                            prob_map.unsqueeze(0).unsqueeze(
+                                0
+                            ),  # shape [1,1,H,W]
+                            kernel_size=3,
+                            stride=1,
+                            padding=1,
+                        )
+                        .squeeze(0)
+                        .squeeze(0)
+                    )  # back to [H,W]
+
+                    threshold = 0.5
+                    keep = (prob_map == pooled_map) & (prob_map > threshold)
+                else:
+                    threshold = 0.5
+                    keep = prob_map > threshold
 
                 y_indices, x_indices = torch.where(keep)
                 kpts = []