@glenn-jocher it's quite simple actually.
First, you read the model weights and configuration to construct the network
net = cv2.dnn.readNetFromDarknet(configPath, weightsPath)

then, we infer an input

blob = cv2.dnn.blobFromImage(frame, 1 / 255.0, (416, 416), swapRB=True, crop=False)
net.setInput(blob)
detections = net.forward(ln)

And finally we run thresholds for filtration with a code like this

boxes = []
confidences = []
classIDs = []
for output in detections:
    for detection in output:
        scores = detection[5:]
        classID = np.argmax(scores)
        confidence = scores[classID]
        if confidence > args["confidence"]:
            # W, H are the dimensions of the input image
            box = detection[0:4] * np.array([W, H, W, H])
            (centerX, centerY, width, height) = box.astype("int")
            x = int(centerX - (width / 2))
            y = int(centerY - (height / 2))
            boxes.append([x, y, int(width), int(height)])
            confidences.append(float(confidence))
            classIDs.append(classID)
idxs = cv2.dnn.NMSBoxes(boxes, confidences, confidence, threshold)

Uh oh!

Is there any way I can use yolov5 with opencv dnn #239

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