title: SSL: CERTIFICATE_VERIFY_FAILED following online tutorial
body: <p>After following the tutorial on this website <a href="https://www.learnopencv.com/faster-r-cnn-object-detection-with-pytorch/" rel="nofollow noreferrer">https://www.learnopencv.com/faster-r-cnn-object-detection-with-pytorch/</a>, when I run the code I get the following error message (see picture). I followed the tutorial exactly how it is, and I have no idea what the error could be.</p>
  <p><a href="https://i.stack.imgur.com/ZZxyp.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/ZZxyp.png" alt="Error message:" /></a></p>
  <p>Thank you so much!</p>
  <pre><code>import cv2
  import matplotlib.pyplot as plt
  from PIL import Image
  from torchvision import transforms as T
  
  model = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=True)
  model.eval()
  
  COCO_INSTANCE_CATEGORY_NAMES = [
      '__background__', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus',
      'train', 'truck', 'boat', 'traffic light', 'fire hydrant', 'N/A', 'stop sign',
      'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',
      'elephant', 'bear', 'zebra', 'giraffe', 'N/A', 'backpack', 'umbrella', 'N/A', 'N/A',
      'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball',
      'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', 'tennis racket',
      'bottle', 'N/A', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl',
      'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza',
      'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed', 'N/A', 'dining table',
      'N/A', 'N/A', 'toilet', 'N/A', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone',
      'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'N/A', 'book',
      'clock', 'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush'
  ]
  
  
  def get_prediction(img_path, threshold):
      img = Image.open(img_path)  # Load the image
      transform = T.Compose([T.ToTensor()])  # Defing PyTorch Transform
      img = transform(img)  # Apply the transform to the image
      pred = model([img])  # Pass the image to the model
      pred_class = [COCO_INSTANCE_CATEGORY_NAMES[i] for i in list(pred[0]['labels'].numpy())]  # Get the Prediction Score
      pred_boxes = [[(i[0], i[2]), (i[2], i[3])] for i in list(pred[0]['boxes'].detach().numpy())]  # Bounding boxes
      pred_score = list(pred[0]['scores'].detach().numpy())
      pred_t = [pred_score.index(x) for x in pred_score if x &gt; threshold][
          -1]  # Get list of index with score greater than threshold.
      pred_boxes = pred_boxes[:pred_t + 1]
      pred_class = pred_class[:pred_t + 1]
      return pred_boxes, pred_class
  
  
  def object_detection_api(img_path, threshold=0.5, rect_th=3, text_size=3, text_th=3):
      boxes, pred_cls = get_prediction(img_path, threshold)  # Get predictions
      img = cv2.imread(img_path)  # Read image with cv2
      img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)  # Convert to RGB
      for i in range(len(boxes)):
          cv2.rectangle(img, boxes[i][0], boxes[i][2], color=(0, 255, 0),
                        thickness=rect_th)  # Draw Rectangle with the coordinates
          cv2.putText(img, pred_cls[i], boxes[i][0], cv2.FONT_HERSHEY_SIMPLEX, text_size, (0, 255, 0),
                      thickness=text_th)  # Write the prediction class
      plt.figure(figsize=(20, 30))  # display the output image
      plt.imshow(img)
      plt.xticks([])
      plt.yticks([])
      plt.show()
  
  
  object_detection_api('./people.jpg', threshold=0.8)
  </code></pre>