diff --git a/multi_series_dongle_manager.py b/multi_series_dongle_manager.py
index ce5fdff..cad34a0 100644
--- a/multi_series_dongle_manager.py
+++ b/multi_series_dongle_manager.py
@@ -26,8 +26,8 @@ class InferenceResult:
     timestamp: float
 
 class DongleSeriesSpec:
-    KL520_GOPS = 345
-    KL720_GOPS = 1425
+    KL520_GOPS = 3
+    KL720_GOPS = 28
     
     SERIES_SPECS = {
         0x100: {"name": "KL520", "gops": KL520_GOPS},
@@ -140,10 +140,8 @@ class MultiSeriesDongleManager:
                     )
                     self.model_descriptors[product_id] = model_descriptor
                     
-                    # Prepare inference descriptor
-                    self.inference_descriptors[product_id] = kp.GenericImageInferenceDescriptor(
-                        model_id=model_descriptor.models[0].id
-                    )
+                    # Store model descriptor for later use
+                    # Note: inference descriptors will be created per task
                     print(f"[{series_name}] Model upload success")
                 
                 # Create result queue for this dongle
@@ -244,6 +242,7 @@ class MultiSeriesDongleManager:
         # Process image input
         if isinstance(image, str):
             image_data = cv2.imread(image)
+            image_data = cv2.cvtColor(image_data, cv2.COLOR_RGB2BGR565)
             if image_data is None:
                 raise FileNotFoundError(f"Image file not found: {image}")
         elif isinstance(image, np.ndarray):
@@ -254,6 +253,8 @@ class MultiSeriesDongleManager:
         # Convert format string to enum
         format_mapping = {
             'BGR565': kp.ImageFormat.KP_IMAGE_FORMAT_RGB565,
+            # 'RGB888': kp.ImageFormat.KP_IMAGE_FORMAT_RGB888,
+            # 'BGR888': kp.ImageFormat.KP_IMAGE_FORMAT_RGB888,  # OpenCV uses BGR by default
             'RGB8888': kp.ImageFormat.KP_IMAGE_FORMAT_RGBA8888,
             'YUYV': kp.ImageFormat.KP_IMAGE_FORMAT_YUYV,
             'RAW8': kp.ImageFormat.KP_IMAGE_FORMAT_RAW8
@@ -341,7 +342,6 @@ class MultiSeriesDongleManager:
         print(f"Send worker started for {series_name}")
         
         device_group = self.dongle_groups[product_id]
-        inference_descriptor = self.inference_descriptors[product_id]
         result_queue = self.result_queues[product_id]
         
         while not self.stop_event.is_set():
@@ -350,22 +350,33 @@ class MultiSeriesDongleManager:
                 if task is None:
                     continue
                 
-                # Configure inference descriptor
-                inference_descriptor.inference_number = task.sequence_id
-                inference_descriptor.input_node_image_list = [kp.GenericInputNodeImage(
-                    image=task.image_data,
-                    image_format=task.image_format,
-                    resize_mode=kp.ResizeMode.KP_RESIZE_ENABLE,
-                    padding_mode=kp.PaddingMode.KP_PADDING_CORNER,
-                    normalize_mode=kp.NormalizeMode.KP_NORMALIZE_KNERON
-                )]
+                # print(f"Sending task {task.sequence_id} to {series_name}")
                 
+                inference_descriptor = kp.GenericImageInferenceDescriptor(
+                    model_id=self.model_descriptors[product_id].models[0].id,
+                )
+                # print(f"Using model ID: {inference_descriptor.model_id}")
+                inference_descriptor.inference_number = task.sequence_id
+                # print(f"Task sequence ID: {task.sequence_id}")
+                # Create new inference descriptor for each task to avoid state issues
+                # print ("image data: ", task.image_data, ", format: ", task.image_format)
+                # print(device_group)
+                inference_descriptor.input_node_image_list = [
+                    kp.GenericInputNodeImage(
+                        image=task.image_data,
+                        image_format=task.image_format,
+                        resize_mode=kp.ResizeMode.KP_RESIZE_ENABLE,
+                        padding_mode=kp.PaddingMode.KP_PADDING_CORNER,
+                        normalize_mode=kp.NormalizeMode.KP_NORMALIZE_KNERON
+                    )
+                ]
+                # print(f"Task image shape: {task.image_data.shape}, format: {task.image_format}")
                 # Send inference
                 kp.inference.generic_image_inference_send(
                     device_group=device_group,
                     generic_inference_input_descriptor=inference_descriptor
                 )
-                
+                print(f"Task {task.sequence_id} sent successfully to {series_name}")
                 self.stats['dongle_stats'][product_id]['sent'] += 1
                 
             except queue.Empty: