diff --git a/frigate/util/model.py b/frigate/util/model.py
index 119274b0b..6ef83aa51 100644
--- a/frigate/util/model.py
+++ b/frigate/util/model.py
@@ -230,6 +230,66 @@ def post_process_yolo(output: list[np.ndarray], width: int, height: int) -> np.n
         return __post_process_nms_yolo(output[0], width, height)
 
 
+def post_process_yolox(predictions: np.ndarray, width: int, height: int) -> np.ndarray:
+    grids = []
+    expanded_strides = []
+
+    # decode and orient predictions
+    strides = [8, 16, 32]
+    hsizes = [height // stride for stride in strides]
+    wsizes = [width // stride for stride in strides]
+
+    for hsize, wsize, stride in zip(hsizes, wsizes, strides):
+        xv, yv = np.meshgrid(np.arange(wsize), np.arange(hsize))
+        grid = np.stack((xv, yv), 2).reshape(1, -1, 2)
+        grids.append(grid)
+        shape = grid.shape[:2]
+        expanded_strides.append(np.full((*shape, 1), stride))
+
+    grids = np.concatenate(grids, 1)
+    expanded_strides = np.concatenate(expanded_strides, 1)
+    predictions[..., :2] = (predictions[..., :2] + grids) * expanded_strides
+    predictions[..., 2:4] = np.exp(predictions[..., 2:4]) * expanded_strides
+
+    # process organized predictions
+    predictions = predictions[0]
+    boxes = predictions[:, :4]
+    scores = predictions[:, 4:5] * predictions[:, 5:]
+
+    boxes_xyxy = np.ones_like(boxes)
+    boxes_xyxy[:, 0] = boxes[:, 0] - boxes[:, 2] / 2.0
+    boxes_xyxy[:, 1] = boxes[:, 1] - boxes[:, 3] / 2.0
+    boxes_xyxy[:, 2] = boxes[:, 0] + boxes[:, 2] / 2.0
+    boxes_xyxy[:, 3] = boxes[:, 1] + boxes[:, 3] / 2.0
+
+    cls_inds = scores.argmax(1)
+    scores = scores[np.arange(len(cls_inds)), cls_inds]
+
+    indices = cv2.dnn.NMSBoxes(
+        boxes_xyxy, scores, score_threshold=0.4, nms_threshold=0.4
+    )
+
+    detections = np.zeros((20, 6), np.float32)
+    for i, (bbox, confidence, class_id) in enumerate(
+        zip(boxes_xyxy[indices], scores[indices], cls_inds[indices])
+    ):
+        if i == 20:
+            break
+
+        detections[i] = [
+            class_id,
+            0.75,
+            bbox[1] / height,
+            bbox[0] / width,
+            bbox[3] / height,
+            bbox[2] / width,
+        ]
+
+        # print(f"raw det is {detections[i]}")
+
+    return detections
+
+
 ### ONNX Utilities