Fix typo.

README.md

@@ -28,7 +28,7 @@ Approximate *baseline* [setup](https://github.com/endernewton/tf-faster-rcnn/blo
 
 **Note**:
   - Due to the randomness in GPU training with Tensorflow especially for VOC, the best numbers are reported (with 2-3 attempts) here. According to my experience, for COCO you can almost always get a very close number (within ~0.2%) despite the randomness.
-  - The numbers are obtained with the **default** testing scheme which selects region proposals using non-maximal suppression (TEST.MODE nms), the alternative testing scheme (TEST.MODE nms) will likely result in slightly better performance (see [report](https://arxiv.org/pdf/1702.02138.pdf), for COCO it drops 0.X AP).
+  - The numbers are obtained with the **default** testing scheme which selects region proposals using non-maximal suppression (TEST.MODE nms), the alternative testing scheme (TEST.MODE nms) will likely result in slightly better performance (see [report](https://arxiv.org/pdf/1702.02138.pdf), for COCO it boosts 0.X AP).
   - Since we keep the small proposals (\< 16 pixels width/height), our performance is especially good for small objects.
   - We do not set a threshold (instead of 0.05) for a detection to be included in the final result, which increases recall.
   - Weight decay is set to 1e-4.