diff --git a/plugins/ckn_inference_daemon/models/mega_detector.py b/plugins/ckn_inference_daemon/models/mega_detector.py
new file mode 100644
index 0000000..4c0a8db
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+++ b/plugins/ckn_inference_daemon/models/mega_detector.py
@@ -0,0 +1,106 @@
+import os
+import sys
+sys.path.append("yolov5")
+
+from utils.general import non_max_suppression
+from utils.augmentations import letterbox
+try:
+    from utils.general import scale_coords  # YOLOv5 < v7
+except ImportError:
+    from utils.general import scale_boxes as scale_coords  # YOLOv5 v7+
+
+import cv2
+import numpy as np
+import torch
+from huggingface_hub import hf_hub_download
+from base_model import BaseModel
+
+class MegaDetectorModel(BaseModel):
+    """
+    A model class that loads the MegaDetector V5 checkpoint from Hugging Face,
+    preprocesses an input image, runs inference, and returns detections (label/conf).
+    """
+
+    def __init__(self, device='cpu', conf_thres=0.25, iou_thres=0.45, labels_path=None):
+        """
+        :param device: 'cpu' or 'cuda' (if GPU is available)
+        :param conf_thres: Confidence threshold for non-max suppression
+        :param iou_thres: IoU threshold for non-max suppression
+        :param labels_path: Optional path to a labels file (if you want class names).
+                            For MegaDetector, we often have 1=animal, 2=person, 3=vehicle, etc.
+        """
+        self.device = torch.device(device)
+        self.conf_thres = conf_thres
+        self.iou_thres = iou_thres
+
+        # Optionally load a label file if you have one (otherwise default to None)
+        self.labels = None
+        if labels_path and os.path.exists(labels_path):
+            with open(labels_path, "r") as f:
+                self.labels = [line.strip() for line in f.readlines()]
+
+        # Download the MegaDetector V5 checkpoint from Hugging Face
+        model_path = hf_hub_download(repo_id="nkarthikeyan/MegaDetectorV5", filename="md_v5a.0.0.pt")
+
+        # Load the YOLO model
+        checkpoint = torch.load(model_path, map_location=self.device)
+        self.model = checkpoint['model'].float().fuse().eval()
+
+        if self.device.type != 'cpu':
+            self.model.to(self.device)
+
+    def pre_process(self, filename):
+        image_bgr = cv2.imread(filename)
+        if image_bgr is None:
+            raise ValueError(f"Could not load image from path: {filename}")
+
+        image_rgb = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2RGB)
+
+        # 1. Detect the model's stride
+        model_stride = int(self.model.stride.max())  # e.g., 32 or 64
+
+        # 2. Letterbox with auto=False, passing the model stride
+        processed = letterbox(
+            image_rgb, new_shape=640, stride=model_stride, auto=False
+        )[0]
+
+        processed = processed.transpose(2, 0, 1)
+        processed = np.ascontiguousarray(processed, dtype=np.float32) / 255.0
+        input_tensor = torch.from_numpy(processed).unsqueeze(0).to(self.device)
+
+        return (input_tensor, image_rgb)
+
+    def predict(self, input_data):
+        """
+        Run the model on the pre-processed input.
+        Returns a list of (label, probability) or (label_str, probability) if labels are provided.
+        """
+        # input_data is a tuple of (processed_tensor, original_rgb)
+        processed_tensor, original_rgb = input_data
+
+        with torch.no_grad():
+            prediction = self.model(processed_tensor)[0]
+
+        # Apply Non-Max Suppression
+        detections = non_max_suppression(prediction,
+                                         conf_thres=self.conf_thres,
+                                         iou_thres=self.iou_thres)
+
+        results = []
+        if len(detections) > 0 and detections[0] is not None:
+            # Rescale detections from [640x640] back to original image size
+            det = detections[0]
+            det[:, :4] = scale_coords(processed_tensor.shape[2:], det[:, :4], original_rgb.shape).round()
+
+            # Each detection: [x1, y1, x2, y2, conf, class]
+            for *xyxy, conf, cls_idx in det.tolist():
+                label_idx = int(cls_idx)
+                confidence = float(conf)
+                # If we have a label file, map the index to a label string
+                if self.labels and 0 <= label_idx < len(self.labels):
+                    results.append((self.labels[label_idx], confidence))
+                else:
+                    # Just return numeric class index
+                    results.append((label_idx, confidence))
+
+        return results