Please, how can I calculate the MAP for these results?

INFO:tensorflow:Restoring parameters from test_image1/model.ckpt-50000
INFO:tensorflow:Restoring parameters from test_image1/model.ckpt-50000
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Performing evaluation on 4 images.
INFO:tensorflow:Performing evaluation on 4 images.
creating index...
index created!
INFO:tensorflow:Loading and preparing annotation results...
INFO:tensorflow:Loading and preparing annotation results...
INFO:tensorflow:DONE (t=0.00s)
INFO:tensorflow:DONE (t=0.00s)
creating index...
index created!
Running per image evaluation...
Evaluate annotation type bbox
DONE (t=0.06s).
Accumulating evaluation results...
DONE (t=0.01s).
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.211
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.380
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.187
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.003
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.233
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.485
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.067
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.241
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.261
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.020
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.283
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.570
INFO:tensorflow:Finished evaluation at 2019-10-28-14:06:58
INFO:tensorflow:Finished evaluation at 2019-10-28-14:06:58
INFO:tensorflow:Saving dict for global step 50000: DetectionBoxes_Precision/mAP = 0.21072435, DetectionBoxes_Precision/mAP (large) = 0.485231, DetectionBoxes_Precision/mAP (medium) = 0.23299623, DetectionBoxes_Precision/mAP (small) = 0.0032204273, DetectionBoxes_Precision/mAP@.50IOU = 0.38036388, DetectionBoxes_Precision/mAP@.75IOU = 0.1866721, DetectionBoxes_Recall/AR@1 = 0.06658986, DetectionBoxes_Recall/AR@10 = 0.24055299, DetectionBoxes_Recall/AR@100 = 0.26059908, DetectionBoxes_Recall/AR@100 (large) = 0.57045454, DetectionBoxes_Recall/AR@100 (medium) = 0.2825, DetectionBoxes_Recall/AR@100 (small) = 0.02, Loss/BoxClassifierLoss/classification_loss = 0.5046802, Loss/BoxClassifierLoss/localization_loss = 0.3415953, Loss/RPNLoss/localization_loss = 0.54075974, Loss/RPNLoss/objectness_loss = 0.46926486, Loss/total_loss = 1.8563001, global_step = 50000, learning_rate = 0.0002, loss = 1.8563001
INFO:tensorflow:Saving dict for global step 50000: DetectionBoxes_Precision/mAP = 0.21072435, DetectionBoxes_Precision/mAP (large) = 0.485231, DetectionBoxes_Precision/mAP (medium) = 0.23299623, DetectionBoxes_Precision/mAP (small) = 0.0032204273, DetectionBoxes_Precision/mAP@.50IOU = 0.38036388, DetectionBoxes_Precision/mAP@.75IOU = 0.1866721, DetectionBoxes_Recall/AR@1 = 0.06658986, DetectionBoxes_Recall/AR@10 = 0.24055299, DetectionBoxes_Recall/AR@100 = 0.26059908, DetectionBoxes_Recall/AR@100 (large) = 0.57045454, DetectionBoxes_Recall/AR@100 (medium) = 0.2825, DetectionBoxes_Recall/AR@100 (small) = 0.02, Loss/BoxClassifierLoss/classification_loss = 0.5046802, Loss/BoxClassifierLoss/localization_loss = 0.3415953, Loss/RPNLoss/localization_loss = 0.54075974, Loss/RPNLoss/objectness_loss = 0.46926486, Loss/total_loss = 1.8563001, global_step = 50000, learning_rate = 0.0002, loss = 1.8563001
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 50000: test_image1/model.ckpt-50000
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 50000: test_image1/model.ckpt-50000
INFO:tensorflow:Performing the final export in the end of training.
INFO:tensorflow:Performing the final export in the end of training.