Adapt detection classes to use batches, adapt demos, update README

Signed-off-by: Micaela Verucchi <micaelaverucchi@gmail.com>

README.md

@@ -123,13 +123,16 @@ rm yolo3_fp32.rt        # be sure to delete(or move) old tensorRT files
 ```
 In general the demo program takes 4 parameters:
 ```
-./demo <network-rt-file> <path-to-video> <kind-of-network> <number-of-classes>
+./demo <network-rt-file> <path-to-video> <kind-of-network> <number-of-classes> <n-batches> <show-flag>
 ```
 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-batches>``` number of batches to use in inference (N.B. you should first export TKDNN_BATCHSIZE to the required n_batches and create again the rt file for the network).
+*  ```<show-flag>``` if set to 0 the demo will not show the visualization but save the video into result.mp4 (if n-batches ==1)
+
 N.b. By default it is used FP32 inference
 
 ![demo](https://user-images.githubusercontent.com/11562617/72547657-540e7800-388d-11ea-83c6-49dfea2a0607.gif)
@@ -218,6 +221,8 @@ cd build
 ./map_demo dla34_cnet_FP32.rt c ../demo/COCO_val2017/all_labels.txt ../demo/config.yaml
 ```
 
+This demo also creates a json file named ```net_name_COCO_res.json``` containing all the detections computed. The detections are in COCO format, the correct format to subit the results to [CodaLab COCO detection challenge](https://competitions.codalab.org/competitions/20794#participate).
+
 ## Existing tests and supported networks
 
 | Test Name         | Network                                       | Dataset                                                       | N Classes | Input size    | Weights                                                                   |