如何解释对象检测模型的评估结果？

Question

我正在TensorFlow对象检测API中运行model_main.py，得到以下结果作为评估。以下是获得的结果

Evaluate annotation type *bbox*
DONE (t=0.85s).
Accumulating evaluation results...
DONE (t=0.18s).
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.688
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.934
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.846
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.688
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.727
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.806
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.810
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.810

任何人都可以解释如何解释此结果吗？ 有关更多信息，请在评论中告诉我。