Update README.md

README.md

@@ -17,9 +17,10 @@ M. Verucchi, L. Bartoli, F. Bagni, F. Gatti, P. Burgio and M. Bertogna, "Real-Ti
     - [2)Export weights for DLA34 and ResNet101](#2export-weights-for-dla34-and-resnet101)
     - [3)Export weights for CenterNet](#3export-weights-for-centernet)
     - [4)Export weights for MobileNetSSD](#4export-weights-for-mobilenetssd)
-  - [Run the demo](#run-the-demo)
+  - [How to convert weights](#how-to-convert-weights)
     - [FP16 inference](#fp16-inference)
     - [INT8 inference](#int8-inference)
+  - [Run the demo](#run-the-demo)
   - [mAP demo](#map-demo)
   - [Existing tests and supported networks](#existing-tests-and-supported-networks)
   - [References](#references)
@@ -113,26 +114,8 @@ cd pytorch-ssd
 conda env create -f env_mobv2ssd.yml
 python run_ssd_live_demo.py mb2-ssd-lite <pth-model-fil> <labels-file>
 ```
-## Run the demo
 
-To run the an object detection demo follow these steps (example with yolov3):
-```
-rm yolo3_fp32.rt        # be sure to delete(or move) old tensorRT files
-./test_yolo3            # run the yolo test (is slow)
-./demo yolo3_fp32.rt ../demo/yolo_test.mp4 y
-```
-In general the demo program takes 4 parameters:
-```
-./demo <network-rt-file> <path-to-video> <kind-of-network> <number-of-classes>
-```
-where
-*  ```<network-rt-file>``` is the rt file generated by a test
-*  ```<<path-to-video>``` is the path to a video file or a camera input  
-*  ```<kind-of-network>``` is the type of network. Thee types are currently supported: ```y``` (YOLO family), ```c``` (CenterNet family) and ```m``` (MobileNet-SSD family)
-*  ```<number-of-classes>```is the number of classes the network is trained on
-N.b. By default it is used FP32 inference
-
-![demo](https://user-images.githubusercontent.com/11562617/72547657-540e7800-388d-11ea-83c6-49dfea2a0607.gif)
+## How to convert weights
 
 ### FP16 inference
 
@@ -187,6 +170,29 @@ rm yolo3_fp32.rt                   # be sure to delete(or move) old tensorRT fil
 ./test_rtinference yolo3_fp32.rt 4 # test with a batch size of 4
 ```
 
+## Run the demo
+
+To run the an object detection demo follow these steps (example with yolov3):
+```
+rm yolo3_fp32.rt        # be sure to delete(or move) old tensorRT files
+./test_yolo3            # run the yolo test (is slow)
+./demo yolo3_fp32.rt ../demo/yolo_test.mp4 y # add parameter y for yolo. c for CNET & m for Mobilenet.
+./demo yolo3_fp32.rt ../demo/yolo_test.mp4 y 1 #if number of classes not 80. Add class number parameter.
+```
+In general the demo program takes 5 parameters:
+```
+./demo <network-rt-file> <path-to-video> <kind-of-network> <number-of-classes> <output-type>
+```
+where
+*  ```<network-rt-file>``` is the rt file generated by a test
+*  ```<<path-to-video>``` is the path to a video file or a camera input  
+*  ```<kind-of-network>``` is the type of network. Thee types are currently supported: ```y``` (YOLO family), ```c``` (CenterNet family) and ```m``` (MobileNet-SSD family)
+*  ```<number-of-classes>```is the number of classes the network is trained on
+N.b. By default it is used FP32 inference
+* ```<output-type>``` benchmark or save_result. Adding benchmark will not show opencv detection video allowing demo to be run from command line and providing performance results without showing the video results. Adding save_result will save output of detection to results.mp4.
+
+![demo](https://user-images.githubusercontent.com/11562617/72547657-540e7800-388d-11ea-83c6-49dfea2a0607.gif)
+
 ## mAP demo
 
 To compute mAP, precision, recall and f1score, run the map_demo.