YOLO Blob Produces No Detections in DepthAI v3

yishu_corpex

Hi,

I’m trying to run a YOLO model on a OAK-D S2 PoE camera using DepthAI v3 (Python 3.1.0 bindings).
I can successfully load my YOLO .blob file and run inference with the DetectionNetwork node, but:

inDet.detections is always empty
The network outputs tensors, but no decoded bounding boxes
There is no error, just 0 detections every frame

Below is my code for you reference:

import cv2
import depthai as dai
import numpy as np

BLOB_PATH = r"C:/EMVS_Development_Camera_Scripts/Box & Label Detection/best_ckpt_openvino_2022.1_5shave.blob"
W, H = 416, 320   # model input size (width, height)

with dai.Pipeline() as pipeline:
    # Camera 
    cam = pipeline.create(dai.node.Camera).build()
    
    # Request NN-sized frames from the camera
    cam_out = cam.requestOutput(
        size=(W, H),
        type=dai.ImgFrame.Type.BGR888p,
        resizeMode=dai.ImgResizeMode.LETTERBOX,
        fps=10,
    )

    # DetectionNetwork – load local blob from disk
    det = pipeline.create(dai.node.DetectionNetwork)
    det.setBlobPath(BLOB_PATH)
    det.setConfidenceThreshold(0.5)
    det.setNumInferenceThreads(2)

    # Link camera to NN
    cam_out.link(det.input)

    # Output queues
    qDet = det.out.createOutputQueue(maxSize=4, blocking=False)
    qRgb = det.passthrough.createOutputQueue(maxSize=4, blocking=False)

    # Start pipeline
    pipeline.start()

    def frameNorm(frame, bbox):
        normVals = np.full(len(bbox), frame.shape[0])
        normVals[::2] = frame.shape[1]
        return (np.clip(np.array(bbox), 0, 1) * normVals).astype(int)

    while pipeline.isRunning():
        inFrame = qRgb.tryGet()
        inDet   = qDet.tryGet()

        if inFrame is not None:
            frame = inFrame.getCvFrame()

            # Draw detections if we also have dets
            print("InDet: ", inDet)
            if inDet is not None:
                for d in inDet.detections:
                    print("Detections Inside: ", d)
                    b = frameNorm(frame, (d.xmin, d.ymin, d.xmax, d.ymax))
                    cv2.rectangle(frame, (b[0], b[1]), (b[2], b[3]), (0, 255, 0), 2)
                    cv2.putText(frame, f"{int(d.confidence * 100)}%",
                                (b[0], b[1] - 5),
                                cv2.FONT_HERSHEY_TRIPLEX, 0.5, (0, 255, 0))

            cv2.imshow("YOLO", frame)

        if cv2.waitKey(1) == ord("q"):
            break

I did read here that the DetectionNetwork node replaces the YoloDetectionNetwork from v2. However, I didn't find any examples that shows how we can use a blob file of our yolo model tuned on custom data (most examples make use of yolo models from the Model Zoo) and how do we set the parameters below that were available in v2 to be set using the YoloDetectionNetwork node:

setNumClasses
setCoordinateSize
setAnchors
setAnchorMasks
setIouThreshold (for NMS algorithm)

Your help and response is highly appreciated as I am trying to resolve this issue asap.

Thanks,
Yishu

KlemenSkrlj

Hi @yishu_corpex ,
could you share a bit more information about this YOLO model (version, how you exported it - though HubAI?). And additionally could you also share a DepthAIv2 script that works for you with this exact model blob so we can check side by side that the update to DAIv3 preserved the same pipeline?
Thanks,
Klemen