From c06c9fcf239ebbaf0591abbc159c5ffb4f8f4bd7 Mon Sep 17 00:00:00 2001
From: Davide Sapienza <sapienza.dav@gmail.com>
Date: Mon, 30 Mar 2020 19:02:55 +0200
Subject: [PATCH] Update README.md

Signed-off-by: Davide Sapienza <sapienza.dav@gmail.com>
---
 README.md | 40 +++++++++++++++++++++++++++++++++++-----
 1 file changed, 35 insertions(+), 5 deletions(-)

diff --git a/README.md b/README.md
index 8ed4ede..ee8bb1e 100644
--- a/README.md
+++ b/README.md
@@ -101,10 +101,9 @@ python run_ssd_live_demo.py mb2-ssd-lite <pth-model-fil> <labels-file>
 
 To run the an object detection demo follow these steps (example with yolov3):
 ```
-export TKDNN_MODE=FP16  # set the half floating point optimization
-rm yolo3.rt             # be sure to delete(or move) old tensorRT files
+rm yolo3_FP32.rt        # be sure to delete(or move) old tensorRT files
 ./test_yolo3            # run the yolo test (is slow)
-./demo yolo3.rt ../demo/yolo_test.mp4 y
+./demo yolo3_FP32.rt ../demo/yolo_test.mp4 y
 ```
 In general the demo program takes 3 parameters:
 ```
@@ -114,11 +113,42 @@ 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)
-N.b. Using FP16 inference will lead to some errors in the results (first or second decimal). 
+N.b. By default it is used FP32 inference
 
 ![demo](https://user-images.githubusercontent.com/11562617/72547657-540e7800-388d-11ea-83c6-49dfea2a0607.gif)
 
+### FP16 inference
+
+To run the an object detection demo with FP16 inference follow these steps (example with yolov3):
+```
+export TKDNN_MODE=FP16  # set the half floating point optimization
+rm yolo3_FP16.rt        # be sure to delete(or move) old tensorRT files
+./test_yolo3            # run the yolo test (is slow)
+./demo yolo3_FP16.rt ../demo/yolo_test.mp4 y
+```
+N.b. Using FP16 inference will lead to some errors in the results (first or second decimal). 
+
+### INT8 inference
+
+To run the an object detection demo with INT8 inference follow these steps (example with yolov3):
+```
+export TKDNN_MODE=INT8  # set the 8-bit integer optimization
+
+# image_list.txt contains the list of the absolute paths to the calibration images
+export TKDNN_CALIB_IMG_PATH=/path/to/calibration/image_list.txt
+
+# label_list.txt contains the list of the absolute paths to the calibration labels
+export TKDNN_CALIB_LABEL_PATH=/path/to/calibration/label_list.txt
+rm yolo3_INT8.rt        # be sure to delete(or move) old tensorRT files
+./test_yolo3            # run the yolo test (is slow)
+./demo yolo3_INT8.rt ../demo/yolo_test.mp4 y
+```
+N.b. Using INT8 inference will lead to some errors in the results (first or second decimal). 
+
+N.b. The network test script is very slow: it call the INT8 calibrator. 
+
 ## mAP demo
+
 To compute mAP, precision, recall and f1score, run the map_demo.
 
 A validation set is needed. To download COCO_val2017 run (form the root folder): 
@@ -139,7 +169,7 @@ Example:
 
 ```
 cd build
-./map_demo dla34_cnet.rt c ../demo/COCO_val2017/all_labels.txt ../demo/config.yaml
+./map_demo dla34_cnet_FP32.rt c ../demo/COCO_val2017/all_labels.txt ../demo/config.yaml
 ```
 
 ## Existing tests and supported networks