Is torchmetrics mean average precision behaving as expected? 2 images assessed individually score higher than the result together

I'm trying to evaluate the performance of an object detection model using torchmetrics mean average precision. However, I'm getting this odd result:

When I evaluate the metric for image A, I get 'map': 0.7891, 'map_50': 1, 'map_75': 1

When I evaluate the metric for image B, I get 'map': 0.7611, 'map_50': 1, 'map_75': 1

When I run the metric for a list containing information about both image A and image B, I expect the performance to be somewhere between 0.7891 and 0.7611. Instead, I get 'map':0.7197, 'map_50': 0.9293,'map_75': 0.9293.

Is my reasoning about how mean average precision for multiple images works flawed, or is something going wrong here?

Here are the specific values I'm using:

import torch
from torchmetrics.detection.mean_ap import MeanAveragePrecision

include_A = True
include_B = True

truth_eval = []
pred_eval = []

if include_A:
  truth_eval.append({'boxes': torch.tensor([[562., 180., 694., 227.],
          [322., 189., 467., 251.],
          [770., 167., 954., 240.]], dtype=torch.float64),
  'labels': torch.tensor([1., 1., 1.], dtype=torch.float64)})
  pred_eval.append({'boxes': torch.tensor([[ 769.4067,  163.4746,  949.1498,  240.5295],
          [ 574.9888,  178.3064,  697.7145,  226.6653],
          [ 333.8886,  188.9244,  471.6525,  250.9485],
          [1037.9913,   20.9808, 1110.0175,  118.0723]]),
  'scores': torch.tensor([1., 1., 1., 1.]),
  'labels': torch.tensor([1., 1., 1., 1.])})

if include_B:
  truth_eval.append({'boxes': torch.tensor([[554., 180., 688., 228.],
          [309., 190., 457., 253.],
          [810., 166., 999., 239.]], dtype=torch.float64),
  'labels': torch.tensor([1., 1., 1.], dtype=torch.float64)})
  pred_eval.append({'boxes': torch.tensor([[ 567.4130,  177.8370,  691.1442,  227.1231],
          [ 810.1462,  163.8314,  994.3475,  241.7314],
          [ 321.8360,  190.0476,  464.3474,  251.8763],
          [1046.0631,   18.7516, 1111.9734,  104.2167],
          [ 540.7168,   76.8780,  564.6191,  133.8904]]),
  'scores': torch.tensor([1., 1., 1., 1., 1.]),
  'labels': torch.tensor([1., 1., 1., 1., 1.])})

metric = MeanAveragePrecision()
metric.update(pred_eval , truth_eval )
metric.compute()