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README.md

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 # Attention-guided Context Feature Pyramid Network for Object Detection
 
-This repository is written by Junxu Cao at UC San Diego, on the base of [Detectron](https://github.com/facebookresearch/Detectron) @ [e8942c8](https://github.com/facebookresearch/Detectron/tree/e8942c882abf6e28fe68a626ec55028c9bdfe1cf).
+This repository is written by Junxu Cao, on the base of [Detectron](https://github.com/facebookresearch/Detectron) @ [e8942c8](https://github.com/facebookresearch/Detectron/tree/e8942c882abf6e28fe68a626ec55028c9bdfe1cf).
 
 ## Introduction
 
 This repository re-implements AC-FPN on the base of [Detectron](https://github.com/facebookresearch/Detectron) and [Detectron-Cascade-RCNN](https://github.com/zhaoweicai/Detectron-Cascade-RCNN).
 
 Please follow [Detectron](https://github.com/facebookresearch/Detectron) on how to install and use Detectron-Cascade-RCNN.
 
+## AC-FPN
+
+Because of the proposed architecture, We have better performance on bigger objectWe have better performance on bigger object 
+![architecture](pics/architecture.jpg)
+
+Object detection result
+![detection](pics/detection_samples.png) 
+
+Instance segmentation result
+![segmentation](pics/instance_samples.png) 
+
+More detail in paper.
+
+## Benchmarking
+
+AC-FPN can be readily plugged into existing FPN-based models and improve performance.
+![segmentation](pics/paper_result.png) 
+
+This repo has released CEM module without AM module, but we can get higher performance than the implementation of pytorch in paper.
+Also, thanks to the power of detectron, this repo is faster in training and inference.
+
+The result of coco test-dev(team Neptune)
+![rank](pics/rank.png) 
+### Mask R-CNN with Bells & Whistles
+
+<table><tbody>
+<!-- START BELLS TABLE -->
+<!-- TABLE HEADER -->
+<!-- Info: we use wrap text in <sup><sub></sub><sup> to make is small -->
+<th valign="bottom"><sup><sub>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;backbone&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;</sub></sup></th>
+<th valign="bottom"><sup><sub>type</sub></sup></th>
+<th valign="bottom"><sup><sub>lr<br/>schd</sub></sup></th>
+<th valign="bottom"><sup><sub>im/<br/>gpu</sub></sup></th>
+<th valign="bottom"><sup><sub>box<br/>AP</sub></sup></th>
+<th valign="bottom"><sup><sub>box<br/>AP50</sub></sup></th>
+<th valign="bottom"><sup><sub>box<br/>AP75</sub></sup></th>
+<!-- TABLE BODY -->
+<tr>
+<td align="left"><sup><sub>X-152-32x8d-FPN-IN5k-baseline</sub></sup></td>
+<td align="left"><sup><sub>Mask</sub></sup></td>
+<td align="left"><sup><sub>s1x</sub></sup></td>
+<td align="right"><sup><sub>1</sub></sup></td>
+<td align="right"><sup><sub>48.1</sub></sup></td>
+<td align="right"><sup><sub>68.3</sub></sup></td>
+<td align="right"><sup><sub>52.9</sub></sup></td>
+</tr>
+<tr>
+<td align="left"><sup><sub>X-152-32x8d-FPN-IN5k-cascade</sub></sup></td>
+<td align="left"><sup><sub>Mask</sub></sup></td>
+<td align="left"><sup><sub>s1x</sub></sup></td>
+<td align="right"><sup><sub>1</sub></sup></td>
+<td align="right"><sup><sub>50.2</sub></sup></td>
+<td align="right"><sup><sub>68.2</sub></sup></td>
+<td align="right"><sup><sub>55.0</sub></sup></td>
+</tr>
+<tr>
+<td align="left"><sup><sub>X-152-32x8d-FPN-IN5k-acfpn(just CEM)</sub></sup></td>
+<td align="left"><sup><sub>Mask</sub></sup></td>
+<td align="left"><sup><sub>s1x</sub></sup></td>
+<td align="right"><sup><sub>1</sub></sup></td>
+<td align="right"><sup><sub>51.9</sub></sup></td>
+<td align="right"><sup><sub>70.4</sub></sup></td>
+<td align="right"><sup><sub>57.0</sub></sup></td>
+</tr>
+<!-- END BELLS TABLE -->
+</tbody></table>
+
+
+
 ## Citation
 
 If you use our code/model/data, please site our paper: