text: Title: GPU performance issues of flask framework
  Body: When I loaded an ultralytics YOLO model outside the framework, I read the image from the client in a request processing function and performed inference. I found that the inference speed was 10 times slower than normal.
  I don’t have this problem when I use fastapi.
  
  ```python
  from flask import Flask, request
  
  from ultralytics import YOLO
  import cv2
  import numpy
  import base64
  model = YOLO("/workspace/yolov8s.pt")
  def b64_cv(frame_b64):
      return cv2.imdecode(numpy.frombuffer(base64.b64decode(frame_b64), numpy.uint8), cv2.IMREAD_COLOR)
  
  app = Flask(__name__)
  
  @app.route('/frame', methods=['POST'])
  def read_item():
      data = request.json
      frame = data.get('frame', None)
      
      results = model(source=b64_cv(frame))
  
      return {}
  
  if __name__ == '__main__':
      app.run(host='0.0.0.0', port=8000)
  ```
  
  ```python
  import requests
  import cv2
  import numpy
  import base64
  
  def cv_b64(frame_cv):
      return str(base64.b64encode(cv2.imencode(".jpg", frame_cv)[1]))[2:-1]
  
  while True:
      stream = cv2.VideoCapture("/workspace/road_30_1920x1080.mp4")
      while stream.isOpened():
          ret, frame = stream.read()
          if not ret: break
  
          data = {"frame": cv_b64(frame)}
          response = requests.post(url="http://127.0.0.1:8000/frame", json=data)
  
  ```
  
  Environment:
  
  - Python version: 3.10.12
  - Flask version: 3.0.0
  