Is Roboflow support deploying yolov8 on OAK-1 LITE?

I have trained a YOLOv3 model using Roboflow and I am currently attempting to deploy the model on an OAK-1 LITE device. However, I am facing an issue where I cannot see any bounding boxes in the output images, despite achieving 100% precision during the training process.

Below is the code I am using:

from roboflowoak import RoboflowOak
import cv2
import time
import numpy as np

if name == ‘main’:
# instantiating an object (rf) with the RoboflowOak module
rf = RoboflowOak(model=“****“, confidence=0.05, overlap=0.5,
”, api_key=“*****”, rgb=True,
depth=False, device=None, blocking=True)
# Running our model and displaying the video output with detections
while True:
t0 = time.time()
# The rf.detect() function runs the model inference
result, frame, raw_frame, depth = rf.detect()
predictions = result[“predictions”]
# predictions:
# [ {
# x: (middle),
# y:(middle),
# width:
# height:
# depth: ###->
# confidence:
# class:
# mask: {
# ]
#frame - frame after preprocs, with predictions
#raw_frame - original frame from your OAK
#depth - depth map for raw_frame, center-rectified to the center camera

    # timing: for benchmarking purposes
    t = time.time()-t0
    print("FPS ", 1/t)
    print("PREDICTIONS ", [p.json() for p in predictions])

    # setting parameters for depth calculation
    # comment out the following 2 lines out if you're using an OAK without Depth
    # displaying the video feed as successive frames
    cv2.imshow("frame", frame)

    # how to close the OAK inference window / stop inference: CTRL+q or CTRL+c
    if cv2.waitKey(1) == ord('q'):

I would greatly appreciate any insights or suggestions on how to resolve this issue.

Thank you in advance for your assistance!