code: def evaluate_coco(generator, model, threshold=0.05):
      """Use the pycocotools to evaluate a COCO model on a dataset.
  
      Args
          generator : The generator for generating the evaluation data.
          model     : The model to evaluate.
          threshold : The score threshold to use.
      """
      # start collecting results
      results = []
      image_ids = []
      for index in progressbar.progressbar(
          range(generator.size()), prefix="COCO evaluation: "
      ):
          image = generator.load_image(index)
          image = generator.preprocess_image(image)
          image, scale = generator.resize_image(image)
  
          if keras.backend.image_data_format() == "channels_first":
              image = image.transpose((2, 0, 1))
  
          # run network
          boxes, scores, labels = model.predict_on_batch(
              np.expand_dims(image, axis=0)
          )
  
          # correct boxes for image scale
          boxes /= scale
  
          # change to (x, y, w, h) (MS COCO standard)
          boxes[:, :, 2] -= boxes[:, :, 0]
          boxes[:, :, 3] -= boxes[:, :, 1]
  
          # compute predicted labels and scores
          for box, score, label in zip(boxes[0], scores[0], labels[0]):
              # scores are sorted, so we can break
              if score < threshold:
                  break
  
              # append detection for each positively labeled class
              image_result = {
                  "image_id": generator.image_ids[index],
                  "category_id": generator.label_to_coco_label(label),
                  "score": float(score),
                  "bbox": box.tolist(),
              }
  
              # append detection to results
              results.append(image_result)
  
          # append image to list of processed images
          image_ids.append(generator.image_ids[index])
  
      if not len(results):
          return
  
      # write output
      json.dump(
          results,
          open("{}_bbox_results.json".format(generator.set_name), "w"),
          indent=4,
      )
      json.dump(
          image_ids,
          open(
              "{}_processed_image_ids.json".format(generator.set_name),
              "w",
          ),
          indent=4,
      )
  
      # load results in COCO evaluation tool
      coco_true = generator.coco
      coco_pred = coco_true.loadRes(
          "{}_bbox_results.json".format(generator.set_name)
      )
  
      # run COCO evaluation
      coco_eval = COCOeval(coco_true, coco_pred, "bbox")
      coco_eval.params.imgIds = image_ids
      coco_eval.evaluate()
      coco_eval.accumulate()
      coco_eval.summarize()
      return coco_eval.stats
  
imports: from pycocotools.cocoeval import COCOeval
  from tensorflow import keras
  import numpy
  import json
  import progressbar
label_desc: Use image node_id 1 for calling a global function with example usage: evaluate_coco(generator, model, threshold) and returns: coco_eval