Update README.md

instancesegmentation/README.md

@@ -61,7 +61,8 @@ For each instance mask prediction, we only count it when its IoU with ground tru
 
 You can refer to COCO evaluation page for more explanation: http://mscoco.org/dataset/#detections-eval
 
-For everyone's reference, Mask-RCNN with ResNet-FPN-50 backbone achieves 20.0 mAP on the validation set. (Only backbone is pretrained on ImageNet; RPN+Bbox+Mask are jointly trained; single-scale training and testing.)
+For everyone's reference, Mask-RCNN with ResNet-FPN-50 backbone achieves 20.0 mAP on the validation set.
+- Only backbone is pretrained on ImageNet; RPN+Bbox+Mask are jointly trained; single-scale training and testing.
 
 This was achieved by Hang Zhao when he was an intern at Facebook. We thank Kaiming He and Ross Girshick for the code pointers and suggestions.
 
@@ -71,5 +72,5 @@ To run the evaluation demo:
 1. Convert annotations of validation set (*.png) into RLE format (.json), by running convert_anns_to_json_dataset.py
 2. Install COCO API: https://github.com/pdollar/coco
 3. Prepare your results in the submission format (.json)
-4. python eval_main.py --dataset_json [DATASET_JSON] --preds_json [PREDS_JSON]
+4. python eval_main.py --dataset_json DATASET_JSON --preds_json PREDS_JSON