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У меня есть этот код на python(yolo) для обнаружения объектов на картинке :

 import numpy as np
import time
import cv2


INPUT_FILE='images/1.jpeg'
OUTPUT_FILE='predicted.jpg'
LABELS_FILE='yolo/voc.names'
CONFIG_FILE='yolo/yolov2-tiny-voc.cfg'
WEIGHTS_FILE='yolo/yolov2-tiny-voc.weights'
CONFIDENCE_THRESHOLD=0.3

            
LABELS = open(LABELS_FILE).read().strip().split("n")

np.random.seed(4)
COLORS = np.random.randint(0, 255, size=(len(LABELS), 3),
    dtype="uint8")


net = cv2.dnn.readNetFromDarknet(CONFIG_FILE, WEIGHTS_FILE)

image = cv2.imread(INPUT_FILE)
(H, W) = image.shape[:2]

# determine only the *output* layer names that we need from YOLO
ln = net.getLayerNames()
ln = [ln[i[0] - 1] for i in net.getUnconnectedOutLayers()]


blob = cv2.dnn.blobFromImage(image, 1 / 255.0, (416, 416),
    swapRB=True, crop=False)
net.setInput(blob)
start = time.time()
layerOutputs = net.forward(ln)
end = time.time()


print("[INFO] YOLO took {:.6f} seconds".format(end - start))


# initialize our lists of detected bounding boxes, confidences, and
# class IDs, respectively
boxes = []
confidences = []
classIDs = []

# loop over each of the layer outputs
for output in layerOutputs:
    # loop over each of the detections
    for detection in output:
        # extract the class ID and confidence (i.e., probability) of
        # the current object detection
        scores = detection[5:]
        classID = np.argmax(scores)
        confidence = scores[classID]

        # filter out weak predictions by ensuring the detected
        # probability is greater than the minimum probability
        if confidence > CONFIDENCE_THRESHOLD:
            # scale the bounding box coordinates back relative to the
            # size of the image, keeping in mind that YOLO actually
            # returns the center (x, y)-coordinates of the bounding
            # box followed by the boxes' width and height
            box = detection[0:4] * np.array([W, H, W, H])
            (centerX, centerY, width, height) = box.astype("int")

            # use the center (x, y)-coordinates to derive the top and
            # and left corner of the bounding box
            x = int(centerX - (width / 2))
            y = int(centerY - (height / 2))

            # update our list of bounding box coordinates, confidences,
            # and class IDs
            boxes.append([x, y, int(width), int(height)])
            confidences.append(float(confidence))
            classIDs.append(classID)

# apply non-maxima suppression to suppress weak, overlapping bounding
# boxes
idxs = cv2.dnn.NMSBoxes(boxes, confidences, CONFIDENCE_THRESHOLD,
    CONFIDENCE_THRESHOLD)

# ensure at least one detection exists
if len(idxs) > 0:
    # loop over the indexes we are keeping
    for i in idxs.flatten():
        # extract the bounding box coordinates
        (x, y) = (boxes[i][0], boxes[i][1])
        (w, h) = (boxes[i][2], boxes[i][3])

        color = [int(c) for c in COLORS[classIDs[i]]]

        cv2.rectangle(image, (x, y), (x   w, y   h), color, 2)
        crop_img = image[y:y h, x:x w]
        

        text = "{}: {:.4f}".format(LABELS[classIDs[i]], confidences[i])
        cv2.putText(image, text, (x, y - 5), cv2.FONT_HERSHEY_SIMPLEX,
            0.5, color, 2)

# show the output image
cv2.imwrite("example.png", image)
 

Как вы видите, я использовал крошечные веса и конфигурацию, но в конечном результате возникла проблема, и все объекты не обнаружены .почему?