Evaluation details (#705)

* Update documentation for testing and calculating mAP for each class

* Update documentation for testing and calculating mAP for each class

README.md

@@ -152,6 +152,14 @@ python -m torch.distributed.launch --nproc_per_node=$NGPUS /path_to_maskrcnn_ben
 ```
 Note we should set `MODEL.RPN.FPN_POST_NMS_TOP_N_TRAIN` follow the rule in Single-GPU training.
 
+## Evaluation
+You can test your model directly on single or multiple gpus. Here is an example for Mask R-CNN R-50 FPN with the 1x schedule on 8 GPUS:
+```bash
+export NGPUS=8
+python -m torch.distributed.launch --nproc_per_node=$NGPUS /path_to_maskrcnn_benchmark/tools/test_net.py --config-file "configs/e2e_mask_rcnn_R_50_FPN_1x.yaml" TEST.IMS_PER_BATCH 16
+```
+To calculate mAP for each class, you can simply modify a few lines in [coco_eval.py](https://github.com/facebookresearch/maskrcnn-benchmark/blob/master/maskrcnn_benchmark/data/datasets/evaluation/coco/coco_eval.py). See [#524](https://github.com/facebookresearch/maskrcnn-benchmark/issues/524#issuecomment-475118810) for more details.
+
 ## Abstractions
 For more information on some of the main abstractions in our implementation, see [ABSTRACTIONS.md](ABSTRACTIONS.md).