update readme

Author: lzx1413

README.md

@@ -5,38 +5,38 @@
 * RFB-SSD[Receptive Field Block Net for Accurate and Fast Object Detection]
 
 ### VOC2007 Test
-| System |  *mAP* | **FPS** (Titan X Maxwell) |
-|:-------|:-----:|:-------:|
-| [Faster R-CNN (VGG16)](https://github.com/ShaoqingRen/faster_rcnn) | 73.2 | 7 | 
-| [YOLOv2 (Darknet-19)](http://pjreddie.com/darknet/yolo/) | 78.6 | 40 | 
-| [R-FCN (ResNet-101)](https://github.com/daijifeng001/R-FCN)| 80.5| 9 |
-| [SSD300* (VGG16)](https://github.com/weiliu89/caffe/tree/ssd) | 77.2 | 46 |
-| [SSD512* (VGG16)](https://github.com/weiliu89/caffe/tree/ssd) | 79.8 | 19 |
-| RFBNet300 (VGG16) | **80.5** |83 |
-| RFBNet512 (VGG16) | **82.2** | 38 |
-| SSD300 (VGG) |77.8|**150 (1080Ti)**|
-| FSSD300 (VGG)|78.8|120 (1080Ti)|
+| System                                   |  *mAP*   | **FPS** (Titan X Maxwell) |
+| :--------------------------------------- | :------: | :-----------------------: |
+| [Faster R-CNN (VGG16)](https://github.com/ShaoqingRen/faster_rcnn) |   73.2   |             7             |
+| [YOLOv2 (Darknet-19)](http://pjreddie.com/darknet/yolo/) |   78.6   |            40             |
+| [R-FCN (ResNet-101)](https://github.com/daijifeng001/R-FCN) |   80.5   |             9             |
+| [SSD300* (VGG16)](https://github.com/weiliu89/caffe/tree/ssd) |   77.2   |            46             |
+| [SSD512* (VGG16)](https://github.com/weiliu89/caffe/tree/ssd) |   79.8   |            19             |
+| RFBNet300 (VGG16)                        | **80.5** |            83             |
+| RFBNet512 (VGG16)                        | **82.2** |            38             |
+| SSD300 (VGG)                             |   77.8   |     **150 (1080Ti)**      |
+| FSSD300 (VGG)                            |   78.8   |       120 (1080Ti)        |
 
 ### COCO 
-| System |  *test-dev mAP* | **Time** (Titan X Maxwell) |
-|:-------|:-----:|:-------:|
-| [Faster R-CNN++ (ResNet-101)](https://github.com/KaimingHe/deep-residual-networks) | 34.9 | 3.36s | 
-| [YOLOv2 (Darknet-19)](http://pjreddie.com/darknet/yolo/) | 21.6 | 25ms| 
-| [SSD300* (VGG16)](https://github.com/weiliu89/caffe/tree/ssd) | 25.1 | 22ms |
-| [SSD512* (VGG16)](https://github.com/weiliu89/caffe/tree/ssd) | 28.8 | 53ms |
-| [RetinaNet500 (ResNet-101-FPN)](https://arxiv.org/pdf/1708.02002.pdf) | 34.4| 90ms|
-| RFBNet300 (VGG16) | **29.9** |**15ms\*** | 
-| RFBNet512 (VGG16) | **33.8** | **30ms\*** |
-| RFBNet512-E (VGG16) | **34.4** | **33ms\*** |  
+| System                                   | *test-dev mAP* | **Time** (Titan X Maxwell) |
+| :--------------------------------------- | :------------: | :------------------------: |
+| [Faster R-CNN++ (ResNet-101)](https://github.com/KaimingHe/deep-residual-networks) |      34.9      |           3.36s            |
+| [YOLOv2 (Darknet-19)](http://pjreddie.com/darknet/yolo/) |      21.6      |            25ms            |
+| [SSD300* (VGG16)](https://github.com/weiliu89/caffe/tree/ssd) |      25.1      |            22ms            |
+| [SSD512* (VGG16)](https://github.com/weiliu89/caffe/tree/ssd) |      28.8      |            53ms            |
+| [RetinaNet500 (ResNet-101-FPN)](https://arxiv.org/pdf/1708.02002.pdf) |      34.4      |            90ms            |
+| RFBNet300 (VGG16)                        |    **29.9**    |         **15ms\***         |
+| RFBNet512 (VGG16)                        |    **33.8**    |         **30ms\***         |
+| RFBNet512-E (VGG16)                      |    **34.4**    |         **33ms\***         |
 
 
 *Note*: **\*** The speed here is tested on the newest pytorch and cudnn version (0.2.0 and cudnnV6), which is obviously faster than the speed reported in the paper (using pytorch-0.1.12 and cudnnV5).
 
 ### MobileNet
-|System |COCO *minival mAP*| **\#parameters**|
-|:-------|:-----:|:-------:|
-|[SSD MobileNet](https://arxiv.org/abs/1704.04861)| 19.3| 6.8M|
-|RFB MobileNet| 20.7\* | 7.4M|
+| System                                   | COCO *minival mAP* | **\#parameters** |
+| :--------------------------------------- | :----------------: | :--------------: |
+| [SSD MobileNet](https://arxiv.org/abs/1704.04861) |        19.3        |       6.8M       |
+| RFB MobileNet                            |       20.7\*       |       7.4M       |
 
 \*: slightly better than the original ones in the paper (20.5).
 
@@ -48,14 +48,15 @@
 5. [Models](#models)
 
 ## Installation
-- Install [PyTorch-0.2.0](http://pytorch.org/) by selecting your environment on the website and running the appropriate command.
-- Clone this repository. This repository is mainly based on [ssd.pytorch](https://github.com/amdegroot/ssd.pytorch) and [Chainer-ssd](https://github.com/Hakuyume/chainer-ssd), a huge thank to them.
+- Install [PyTorch-0.2.0+](http://pytorch.org/) by selecting your environment on the website and running the appropriate command.
+- Clone this repository. This repository is mainly based on[RFBNet](https://github.com/ruinmessi/RFBNet), [ssd.pytorch](https://github.com/amdegroot/ssd.pytorch) and [Chainer-ssd](https://github.com/Hakuyume/chainer-ssd), a huge thank to them.
   * Note: We currently only support Python 3+.
 - Compile the nms and coco tools:
 ```Shell
 ./make.sh
 ```
-*Note*: Check you GPU architecture support in utils/build.py, line 131. Default is:
+Note*: Check you GPU architecture support in utils/build.py, line 131. Default is:
+
 ``` 
 'nvcc': ['-arch=sm_52',
 ```
@@ -101,7 +102,7 @@ $COCO/images/val2014/
 
 ## Training
 - First download the fc-reduced [VGG-16](https://arxiv.org/abs/1409.1556) PyTorch base network weights at:    https://s3.amazonaws.com/amdegroot-models/vgg16_reducedfc.pth
-or from our [BaiduYun Driver](https://pan.baidu.com/s/1jIP86jW) 
+  or from our [BaiduYun Driver](https://pan.baidu.com/s/1jIP86jW) 
 - MobileNet pre-trained basenet is ported from [MobileNet-Caffe](https://github.com/shicai/MobileNet-Caffe), which achieves slightly better accuracy rates than the original one reported in the [paper](https://arxiv.org/abs/1704.04861), weight file is available at: https://drive.google.com/open?id=13aZSApybBDjzfGIdqN1INBlPsddxCK14 or [BaiduYun Driver](https://pan.baidu.com/s/1dFKZhdv).
 
 - By default, we assume you have downloaded the file in the `RFBNet/weights` dir:
@@ -120,7 +121,7 @@ python train_RFB.py -d VOC -v RFB_vgg -s 300
   * -v: choose backbone version, RFB_VGG, RFB_E_VGG or RFB_mobile.
   * -s: image size, 300 or 512.
   * You can pick-up training from a checkpoint by specifying the path as one of the training parameters (again, see `train_RFB.py` for options)
-  
+
 ## Evaluation
 To evaluate a trained network:
 
@@ -131,7 +132,7 @@ By default, it will directly output the mAP results on VOC2007 *test* or COCO *m
 
 ## Models
 
-* 07+12 [RFB_Net300](https://drive.google.com/open?id=1V3DjLw1ob89G8XOuUn7Jmg_o-8k_WM3L), [BaiduYun Driver](https://pan.baidu.com/s/1bplRosf)
+* 07+12 [RFB_Net300](https://drive.google.com/open?id=1V3DjLw1ob89G8XOuUn7Jmg_o-8k_WM3L), [BaiduYun Driver](https://pan.baidu.com/s/1bplRosf),[FSSD300](https://drive.google.com/open?id=1xhgdxCF_HuC3SP6ALhhTeC5RTmuoLzgC),[SSD300](https://drive.google.com/open?id=10sM_yWSN8vRZdh6Sf0CILyMfcoJiCNtn)
 * COCO [RFB_Net512_E](https://drive.google.com/open?id=1pHDc6Xg9im3affOr7xaimXaRNOHtbaPM), [BaiduYun Driver](https://pan.baidu.com/s/1o8dxrom)
 * COCO [RFB_Mobile Net300](https://drive.google.com/open?id=1vmbTWWgeMN_qKVWOeDfl1EN9c7yHPmOe), [BaiduYun Driver](https://pan.baidu.com/s/1bp4ik1L)