code: def test_perform_inference_without_mask_output_with_automodel(self):
      from sahi import AutoDetectionModel
  
      # init model
      download_mmdet_yolox_tiny_model()
  
      mmdet_detection_model = AutoDetectionModel.from_pretrained(
          model_type="mmdet",
          model_path=MmdetTestConstants.MMDET_YOLOX_TINY_MODEL_PATH,
          config_path=MmdetTestConstants.MMDET_YOLOX_TINY_CONFIG_PATH,
          confidence_threshold=CONFIDENCE_THRESHOLD,
          category_remapping=None,
          load_at_init=True,
          image_size=IMAGE_SIZE,
      )
  
      # prepare image
      image = read_image(IMAGE_PATH)
  
      # perform inference
      mmdet_detection_model.perform_inference(image)
      original_predictions = mmdet_detection_model.original_predictions
  
      pred = original_predictions[0]
      n_preds = len(pred["bboxes"])
      self.assertTrue(len(pred["bboxes"]) == n_preds)
      self.assertTrue(len(pred["labels"]) == n_preds)
      self.assertTrue(len(pred["scores"]) == n_preds)
      boxes = np.array(pred["bboxes"])
      labels = np.array(pred["labels"])
      scores = np.array(pred["scores"])
  
      # find box of first car detection with conf greater than 0.5
      idx = np.where((scores >= 0.5) & (labels == 2))[0][0]
  
      # compare
      self.assertEqual(
          boxes[idx].astype(int).tolist(), [320, 323, 380, 365]
      )
  
file_path: sahi/tests/test_mmdetectionmodel.py
imports: import unittest
  import numpy
  from sahi.utils.cv import read_image
  from sahi.utils.file import download_from_url
  from sahi.utils.mmdet import MmdetTestConstants, download_mmdet_cascade_mask_rcnn_model, download_mmdet_yolox_tiny_model
label_desc: Use image node_id 6 for calling the TestMmdetDetectionModel obj's underlying member method code with example usage: obj.test_perform_inference_without_mask_output_with_automodel() without return types