update D7X description on readme

Author: zylo117

readme.md

@@ -22,35 +22,6 @@ The speed/FPS test includes the time of post-processing with no jit/data precisi
 | D7 | [efficientdet-d7.pth](https://github.com/zylo117/Yet-Another-Efficient-Pytorch/releases/download/1.2/efficientdet-d7.pth) | 3819 | 3.73 | - | 52.7 | 53.7
 | D7X | [efficientdet-d8.pth](https://github.com/zylo117/Yet-Another-Efficient-Pytorch/releases/download/1.2/efficientdet-d8.pth) | 3983 | 2.39 | - | 53.9 | 55.1
 
-## Speed Test
-
-This pure-pytorch implement is up to 2 times faster than the official Tensorflow version without any trick.
-
-Recorded on 2020-04-26,
-
-official git version: <https://github.com/google/automl/commit/006668f2af1744de0357ca3d400527feaa73c122>
-
-| coefficient | FPS(this repo, tested on RTX2080Ti) | FPS(official, tested on T4) |  Ratio |
-| :------: | :------: | :------: | :-----: |
-| D0 | 36.20 | 42.1 | 0.86X |
-| D1 | 29.69 | 27.7 | 1.07X |
-| D2 | 26.50 | 19.7 | 1.35X |
-| D3 | 22.73 | 11.8 | 1.93X |
-| D4 | 14.75 | 7.1 | 2.08X |
-| D5 | 7.11 | 3.6 | 1.98X |
-| D6 | 5.30 | 2.6 | 2.03X |
-| D7 | 3.73 | - | - |
-
-Test method (this repo):
-
-Run this test on 2080Ti, Ubuntu 19.10 x64.
-
-1. Prepare a image tensor with the same content, size (1,3,512,512)-pytorch.
-2. Initiate everything by inferring once.
-3. Run 10 times with batchsize 1 and calculate the average time, including post-processing and visualization, to make the test more practical.
-
-___
-
 ## Update Log
 
 [2020-07-23] supports efficientdet-d7x, mAP 53.9, using efficientnet-b7 as its backbone and an extra deeper pyramid level of BiFPN. For the sake of simplicity, let's call it efficientdet-d8.
@@ -235,8 +206,9 @@ Check out this [tutorial](tutorial/train_shape.ipynb) if you are new to this. Yo
 - [X] adapt anchor strategies
 - [X] mAP tests
 - [X] training-scripts
-- [X] efficientdet D6 supports
-- [X] efficientdet D7 supports
+- [X] efficientdet D6 support
+- [X] efficientdet D7 support
+- [X] efficientdet D7x support
 
 ## FAQ