update yolov5 readme

Author: wang-xinyu

README.md

@@ -10,6 +10,7 @@ All the models are implemented in pytorch/mxnet/tensorflown first, and export a
 
 ## News
 
+- `18 Oct 2021`. [xupengao](https://github.com/xupengao): YOLOv5 updated to v6.0, supporting n/s/m/l/x/n6/s6/m6/l6/x6.
 - `31 Aug 2021`. [FamousDirector](https://github.com/FamousDirector): update retinaface to support TensorRT 8.0.
 - `27 Aug 2021`. [HaiyangPeng](https://github.com/HaiyangPeng): add a python wrapper for hrnet segmentation.
 - `1 Jul 2021`. [freedenS](https://github.com/freedenS): DE⫶TR: End-to-End Object Detection with Transformers. First Transformer model!
@@ -24,7 +25,6 @@ All the models are implemented in pytorch/mxnet/tensorflown first, and export a
 - `23 Apr 2021`. [irvingzhang0512](https://github.com/irvingzhang0512) add TSM: Temporal Shift Module for Efficient Video Understanding, ICCV2019.
 - `23 Apr 2021`. [freedenS](https://github.com/freedenS) implement MaskRCNN, till now the MOST complicated model in this repo.
 - `16 Apr 2021`. [irvingzhang0512](https://github.com/irvingzhang0512) implement lenet and resnet50 with Python API, [freedenS](https://github.com/freedenS) implement FasterRCNN with five plugins, cheers!
-- `2 Apr 2021`. [mingyu6yang](https://github.com/mingyu6yang) added a python wrapper for retinaface, [makaveli10](https://github.com/makaveli10) added DenseNet-121.
 
 ## Tutorials
 
@@ -68,7 +68,7 @@ Following models are implemented.
 |[yolov3](./yolov3)| darknet-53, weights and pytorch implementation from [ultralytics/yolov3](https://github.com/ultralytics/yolov3) |
 |[yolov3-spp](./yolov3-spp)| darknet-53, weights and pytorch implementation from [ultralytics/yolov3](https://github.com/ultralytics/yolov3) |
 |[yolov4](./yolov4)| CSPDarknet53, weights from [AlexeyAB/darknet](https://github.com/AlexeyAB/darknet#pre-trained-models), pytorch implementation from [ultralytics/yolov3](https://github.com/ultralytics/yolov3) |
-|[yolov5](./yolov5)| yolov5 v1.0-v5.0, pytorch implementation from [ultralytics/yolov5](https://github.com/ultralytics/yolov5) |
+|[yolov5](./yolov5)| yolov5 v1.0-v6.0, pytorch implementation from [ultralytics/yolov5](https://github.com/ultralytics/yolov5) |
 |[retinaface](./retinaface)| resnet50 and mobilnet0.25, weights from [biubug6/Pytorch_Retinaface](https://github.com/biubug6/Pytorch_Retinaface) |
 |[arcface](./arcface)| LResNet50E-IR, LResNet100E-IR and MobileFaceNet, weights from [deepinsight/insightface](https://github.com/deepinsight/insightface) |
 |[retinafaceAntiCov](./retinafaceAntiCov)| mobilenet0.25, weights from [deepinsight/insightface](https://github.com/deepinsight/insightface), retinaface anti-COVID-19, detect face and mask attribute |

yolov5/README.md

@@ -4,8 +4,9 @@ The Pytorch implementation is [ultralytics/yolov5](https://github.com/ultralytic
 
 ## Different versions of yolov5
 
-Currently, we support yolov5 v1.0(yolov5s only), v2.0, v3.0, v3.1, v4.0 and v5.0.
+Currently, we support yolov5 v1.0, v2.0, v3.0, v3.1, v4.0, v5.0 and v6.0.
 
+- For yolov5 v6.0, download .pt from [yolov5 release v6.0](https://github.com/ultralytics/yolov5/releases/tag/v6.0), `git clone -b v6.0 https://github.com/ultralytics/yolov5.git` and `git clone https://github.com/wang-xinyu/tensorrtx.git`, then follow how-to-run in current page.
 - For yolov5 v5.0, download .pt from [yolov5 release v5.0](https://github.com/ultralytics/yolov5/releases/tag/v5.0), `git clone -b v5.0 https://github.com/ultralytics/yolov5.git` and `git clone -b yolov5-v5.0 https://github.com/wang-xinyu/tensorrtx.git`, then follow how-to-run in [tensorrtx/yolov5-v5.0](https://github.com/wang-xinyu/tensorrtx/tree/yolov5-v5.0/yolov5).
 - For yolov5 v4.0, download .pt from [yolov5 release v4.0](https://github.com/ultralytics/yolov5/releases/tag/v4.0), `git clone -b v4.0 https://github.com/ultralytics/yolov5.git` and `git clone -b yolov5-v4.0 https://github.com/wang-xinyu/tensorrtx.git`, then follow how-to-run in [tensorrtx/yolov5-v4.0](https://github.com/wang-xinyu/tensorrtx/tree/yolov5-v4.0/yolov5).
 - For yolov5 v3.1, download .pt from [yolov5 release v3.1](https://github.com/ultralytics/yolov5/releases/tag/v3.1), `git clone -b v3.1 https://github.com/ultralytics/yolov5.git` and `git clone -b yolov5-v3.1 https://github.com/wang-xinyu/tensorrtx.git`, then follow how-to-run in [tensorrtx/yolov5-v3.1](https://github.com/wang-xinyu/tensorrtx/tree/yolov5-v3.1/yolov5).
@@ -15,7 +16,7 @@ Currently, we support yolov5 v1.0(yolov5s only), v2.0, v3.0, v3.1, v4.0 and v5.0
 
 ## Config
 
-- Choose the model s/m/l/x/s6/m6/l6/x6 from command line arguments.
+- Choose the model n/s/m/l/x/n6/s6/m6/l6/x6 from command line arguments.
 - Input shape defined in yololayer.h
 - Number of classes defined in yololayer.h, **DO NOT FORGET TO ADAPT THIS, If using your own model**
 - INT8/FP16/FP32 can be selected by the macro in yolov5.cpp, **INT8 need more steps, pls follow `How to Run` first and then go the `INT8 Quantization` below**
@@ -29,9 +30,8 @@ Currently, we support yolov5 v1.0(yolov5s only), v2.0, v3.0, v3.1, v4.0 and v5.0
 1. generate .wts from pytorch with .pt, or download .wts from model zoo
 
 ```
-git clone -b v5.0 https://github.com/ultralytics/yolov5.git
-git clone https://github.com/wang-xinyu/tensorrtx.git
-// download https://github.com/ultralytics/yolov5/releases/download/v5.0/yolov5s.pt
+// clone code according to above #Different versions of yolov5
+// download https://github.com/ultralytics/yolov5/releases/download/v6.0/yolov5s.pt
 cp {tensorrtx}/yolov5/gen_wts.py {ultralytics}/yolov5
 cd {ultralytics}/yolov5
 python gen_wts.py -w yolov5s.pt -o yolov5s.wts
@@ -48,7 +48,7 @@ cd build
 cp {ultralytics}/yolov5/yolov5s.wts {tensorrtx}/yolov5/build
 cmake ..
 make
-sudo ./yolov5 -s [.wts] [.engine] [s/m/l/x/s6/m6/l6/x6 or c/c6 gd gw]  // serialize model to plan file
+sudo ./yolov5 -s [.wts] [.engine] [n/s/m/l/x/n6/s6/m6/l6/x6 or c/c6 gd gw]  // serialize model to plan file
 sudo ./yolov5 -d [.engine] [image folder]  // deserialize and run inference, the images in [image folder] will be processed.
 // For example yolov5s
 sudo ./yolov5 -s yolov5s.wts yolov5s.engine s

yolov5/yolov5.cpp

@@ -7,7 +7,7 @@
 #include "utils.h"
 #include "calibrator.h"
 
-#define USE_FP32  // set USE_INT8 or USE_FP16 or USE_FP32
+#define USE_FP16  // set USE_INT8 or USE_FP16 or USE_FP32
 #define DEVICE 0  // GPU id
 #define NMS_THRESH 0.4
 #define CONF_THRESH 0.5