yolo3 berkeley ok

README.md

@@ -1,11 +1,11 @@
 # tkDNN
-tkDNN is a Deep Neural Network library built with cuDNN primitives specifically thought to work on NVIDIA TK1 board.<br>
+tkDNN is a Deep Neural Network library built with cuDNN primitives specifically thought to work on NVIDIA TK1(and all successive) board.<br>
 The main scope is to do high performance inference on already trained models.
 
 this branch actually work on every NVIDIA GPU that support the dependencies:
-* CUDA 8
-* CUDNN 6
-* TENSORRT 2
+* CUDA 9
+* CUDNN 7.105
+* TENSORRT 4.02
 
 ## Workflow
 The recommended workflow follow these step:
@@ -32,18 +32,20 @@ Assumiung you have correctly builded the library these are the test ready to exe
 * test_mnistRT: the mnist network hardcoded in using tensorRT apis (TENSORRT only)
 * test_yolo: YOLO detection network (CUDNN and TENSORRT)
 * test_yolo_tiny: smaller version of YOLO (CUDNN and TENSRRT)
+* test_yolo3_berkeley: our yolo3 version trained with BDD100K dateset 
 
-## Live detection
+## yolo3 berkeley demo detection
 For the live detection you need to precompile the tensorRT file by luncing the desidered network test, this is the recommended process:
 ```
 export TKDNN_MODE=FP16   # set the half floating point optimization
-rm yolo.rt		 # be sure to delete(or move) old tensorRT files
-./test_yolo              # run the yolo test (is slow)
+rm yolo3_berkeley.rt		 # be sure to delete(or move) old tensorRT files
+./test_yolo3_berkeley              # run the yolo test (is slow)
 # with f16 inference the result will be a bit incorrect
 ```
-this will genereate a yolo.rt file that can be used for live detection:
+this will genereate a yolo3_berkeley.rt file that can be used for live detection:
 ```
-./live yolo.rt 1 -s -t0.3    # launch detection on device 1 with 0.3 thresh
+./demo             # launch detection on a demo video
+./demo /dev/video0 # launch detection on device 0
 ```