Here’s the code that performs the classification task.
def classify_face(image):
    device = torch.device("cpu")
    img = process_image(image)
    print('Image processed')
    # img = image.unsqueeze_(0)
    # img = image.float()
    
    pred = model(img)[0]
    
    # Apply NMS
    pred = non_max_suppression(pred, 0.4, 0.5, classes = [0, 1, 2], agnostic = None )
    if classify:
        pred = apply_classifier(pred, modelc, img, im0s)
    #print(pred)
    
    model.eval()
    model.cpu()
    
    print(pred)
        
    # output = non_max_suppression(output, 0.4, 0.5, classes = class_names, agnostic = False)
        
    #_, predicted = torch.max(output[0], 1)
    #print(predicted.data[0], "predicted")

    classification = torch.cat(pred)[:, -1]
    index = int(classification)
    print(names[index])
    return names[index]

During prediction, pred consists of x1, y1, x2, y2, conf, and class.
Eg: pred = [tensor([[176.64380, 193.86154, 273.84702, 306.30405,   0.83492,   2.00000]])]
If there are no predictions made by the model then pred is simply empty.
Eg: pred = [tensor([], size=(0, 6))]
Presently my program stops prediction if it receives an empty tensor and throws an error:
Traceback (most recent call last):
  File "WEBCAM_DETECT.py", line 168, in <module>
    label = classify_face(frame)
  File "WEBCAM_DETECT.py", line 150, in classify_face
    index = int(classification)
ValueError: only one element tensors can be converted to Python scalars

How do I make my program sort of just ignore if there are no predictions made at a certain frame and continue onto the next frame?
My goal is to access the class from the tensor stored in pred, in integer.
Thank you for the help!