class_docstr: Class for loading the COCO dataset meant for the DETR ResNet50 model`.
  
  Implement the necessary methods to load the images, labels and generate model predictions in a format comprehensible
   by deepchecks.
class_docstr_tok: ["Class","for","loading","the","COCO","dataset","meant","for","the","DETR","ResNet50","model","`",".","Implement","the","necessary","methods","to","load","the","images",",","labels","and","generate","model","predictions","in","a","format","comprehensible","by","deepchecks","."]
code: def __init__(self):
      # Create a transform to pre-process the images into a format acceptable by the DETR model.
      self.transforms = T.Compose(
          [
              T.Resize(800),
              T.ToTensor(),
              T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
          ]
      )
  
      # Build a dict translating the classes DETR was trained on to the classes YOLO was trained on.
      # DETR classes, listed in DETR_CLASSES, include 'N/A' classes which didn't exist in the YOLO version of COCO
      # data.
      self.label_translation = {}
      detr_shift = 0
      for i in range(len(self.DETR_CLASSES)):
          if self.DETR_CLASSES[i] == "N/A":
              detr_shift += 1
          self.label_translation[i] = i - detr_shift
  
file_path: deepchecks/examples/integrations/hugging_face/deepchecks_hugging_face_tutorial.py
imports: import torch
  from transformers import DetrForObjectDetection
  from typing import Union, List, Iterable
  import numpy
  from deepchecks.vision import VisionData
  import torchvision.transforms
  from deepchecks.vision.datasets.detection import coco_torch
  from deepchecks.vision.datasets.detection import coco_utils
  from deepchecks.vision.vision_data import BatchOutputFormat
  from deepchecks.vision.checks import MeanAveragePrecisionReport
label_desc: Use image node_id 1 to create a new COCODETRData object  with example: obj = COCODETRData()