update readme

Author: wang-xinyu

README.md

@@ -10,6 +10,7 @@ All the models are implemented in pytorch/mxnet/tensorflown first, and export a
 
 ## News
 
+- `24 Jan 2021`. IBN-Net added by [TCHeish](https://github.com/TCHeish), PSENet optimized, YOLOv5 v4.0 INT8, etc.
 - `8 Jan 2021`. YOLOv5 s/m/l/x updated to v4.0.
 - `27 Dec 2020`. HRNet-Semantic-Segmentation added by [BaofengZan](https://github.com/BaofengZan).
 - `4 Dec 2020`. DBNet dynamic input shape support by [BaofengZan](https://github.com/BaofengZan), YOLOv3 int8, PSENet(tensorflow) text detection by [upczww](https://github.com/upczww).
@@ -18,13 +19,10 @@ All the models are implemented in pytorch/mxnet/tensorflown first, and export a
 - `7 Nov 2020`. All models migrated to trt7 API, and clean up the master branch.
 - `29 Oct 2020`. First INT8 quantization implementation! Please check retinaface.
 - `23 Oct 2020`. Add a .wts model zoo for quick evaluation.
-- `21 Sep 2020`. [BaofengZan](https://github.com/BaofengZan) added hrnet classification and step by step tutorial(Chinese).
 - `16 Sep 2020`. [hwh-hit](https://github.com/hwh-hit) added ufld(Ultra-Fast-Lane-Detection, ECCV2020).
 - `13 Sep 2020`. Add crnn, and got 1000fps on GTX1080.
 - `28 Aug 2020`. [BaofengZan](https://github.com/BaofengZan) added a tutorial for compiling and running tensorrtx on windows.
 - `16 Aug 2020`. [upczww](https://github.com/upczww) added a python wrapper for yolov5.
-- `14 Aug 2020`. Update yolov5 to v3.0 release.
-- `3 Aug 2020`. [BaofengZan](https://github.com/BaofengZan) implemented yolov5 s/m/l/x (yolov5 v2.0 release).
 - `28 May 2020`. arcface LResNet50E-IR model from [deepinsight/insightface](https://github.com/deepinsight/insightface) implemented. We got 333fps on GTX1080.
 - `22 May 2020`. A new branch [trt4](https://github.com/wang-xinyu/tensorrtx/tree/trt4) created, which is using TensorRT 4 API. Now the master branch is using TensorRT 7 API. But only `yolov4` has been migrated to TensorRT 7 API for now. The rest will be migrated soon. And a tutorial for `migarating from TensorRT 4 to 7` provided.
 

arcface/README.md

@@ -10,21 +10,30 @@ The two input images used in this project are joey0.ppm and joey1.ppm, download
 <img src="https://user-images.githubusercontent.com/15235574/83122953-f45f8d80-a106-11ea-84b0-4f6ff91b5924.jpg">
 </p>
 
+## Config
+
+- FP16/FP32 can be selected by the macro `USE_FP16` in arcface-r50.cpp
+- GPU id can be selected by the macro `DEVICE` in arcface-r50.cpp
+
 ## Run
 
-```
 1. generate arcface-r50.wts from mxnet implementation with LResNet50E-IR,ArcFace@ms1m-refine-v1 pretrained model
 
+```
 git clone https://github.com/deepinsight/insightface
-cd insightface/deploy
+cd insightface
+git checkout 3866cd77a6896c934b51ed39e9651b791d78bb57
+cd deploy
 // copy tensorrtx/arcface/gen_wts.py to here(insightface/deploy)
 // download model-r50-am-lfw.zip and unzip here(insightface/deploy)
 python gen_wts.py
 // a file 'arcface-r50.wts' will be generated.
 // the master branch of insightface should work, if not, you can checkout 94ad870abb3203d6f31b049b70dd080dc8f33fca
+```
 
 2. put arcface-r50.wts into tensorrtx/arcface, build and run
 
+```
 cd tensorrtx/arcface
 // download joey0.ppm and joey1.ppm, and put here(tensorrtx/arcface)
 mkdir build
@@ -33,14 +42,9 @@ cmake ..
 make
 sudo ./arcface-r50 -s    // serialize model to plan file i.e. 'arcface-r50.engine'
 sudo ./arcface-r50 -d    // deserialize plan file and run inference
-
-3. check the output log, latency and similarity score.
 ```
 
-## Config
-
-- FP16/FP32 can be selected by the macro `USE_FP16` in arcface-r50.cpp
-- GPU id can be selected by the macro `DEVICE` in arcface-r50.cpp
+3. check the output log, latency and similarity score.
 
 ## More Information
 

yolov5/README.md

@@ -66,7 +66,7 @@ python yolov5_trt.py
 
 2. unzip it in yolov5/build
 
-3. set the macro `USE_INT8` in yolov3.cpp and make
+3. set the macro `USE_INT8` in yolov5.cpp and make
 
 4. serialize the model and test