# Is Roboflow support deploying yolov8 on OAK-1 LITE?

**URL:** <https://discuss.roboflow.com/t/is-roboflow-support-deploying-yolov8-on-oak-1-lite/2679>\
**Category:** 🤝  Community Help\
**Created:** [July 17, 2023, 12:38pm UTC](https://discuss.roboflow.com/t/is-roboflow-support-deploying-yolov8-on-oak-1-lite/2679 "2023-07-17T12:38:32Z")\
**Posts on this page:** 1\
**Page:** 1

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**Author:** ![Morad\_Laglil](https://yyz1.discourse-cdn.com/flex029/user_avatar/discuss.roboflow.com/morad_laglil/32/2051_2.png) [@Morad\_Laglil](https://discuss.roboflow.com/u/Morad_Laglil)\
**Post date:** [July 17, 2023, 12:38pm UTC](https://discuss.roboflow.com/t/is-roboflow-support-deploying-yolov8-on-oak-1-lite/2679/1 "2023-07-17T12:38:33Z")

</div>

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,  
version=”**_”, 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'):
        break

```

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

## Thank you in advance for your assistance!