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readme update

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Francesco Gatti
2020-04-09 20:15:37 +02:00
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@@ -3,15 +3,23 @@ tkDNN is a Deep Neural Network library built with cuDNN and tensorRT primitives,
The main goal of this project is to exploit NVIDIA boards as much as possible to obtain the best inference performance. It does not allow training. The main goal of this project is to exploit NVIDIA boards as much as possible to obtain the best inference performance. It does not allow training.
## Index ## Index
* [Dependencies](#dependencies) - [tkDNN](#tkdnn)
* [About OpenCV](#about-opencv) - [Index](#index)
* [How to compile this repo](#how-to-compile-this-repo) - [Dependencies](#dependencies)
* [Workflow](#workflow) - [About OpenCV](#about-opencv)
* [How to export weights](#how-to-export-weights) - [How to compile this repo](#how-to-compile-this-repo)
* [Run the demo](#run-the-demo) - [Workflow](#workflow)
* [mAP demo](#map-demo) - [How to export weights](#how-to-export-weights)
* [Existing tests and supported networks](#existing-tests-and-supported-networks) - [1)Export weights from darknet](#1export-weights-from-darknet)
* [References](#references) - [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)
- [FP16 inference](#fp16-inference)
- [INT8 inference](#int8-inference)
- [mAP demo](#map-demo)
- [Existing tests and supported networks](#existing-tests-and-supported-networks)
- [References](#references)
@@ -32,17 +40,15 @@ bash scripts/install_OpenCV4.sh
When using openCV not compiled with contrib, comment the definition of OPENCV_CUDACONTRIBCONTRIB in include/tkDNN/DetectionNN.h. When commented, the preprocessing of the networks is computed on the CPU, otherwise on the GPU. In the latter case some milliseconds are saved in the end-to-end latency. When using openCV not compiled with contrib, comment the definition of OPENCV_CUDACONTRIBCONTRIB in include/tkDNN/DetectionNN.h. When commented, the preprocessing of the networks is computed on the CPU, otherwise on the GPU. In the latter case some milliseconds are saved in the end-to-end latency.
## How to compile this repo ## How to compile this repo
Build with cmake. If using Ubuntu 18.04 a new version of cmake is needed (1.15 or above). Build with cmake. If using Ubuntu 18.04 a new version of cmake is needed (3.15 or above).
``` ```
git clone https://github.com/ceccocats/tkDNN git clone https://github.com/ceccocats/tkDNN
cd tkDNN cd tkDNN
git checkout cnet
mkdir build mkdir build
cd build cd build
cmake .. # use -DTEST_DATA=False to skip dataset download cmake ..
make make
``` ```
If TEST_DATA is not set to False, weights needed to run some tests will be automatically downloaded.
## Workflow ## Workflow
Steps needed to do inference on tkDNN with a custom neural network. Steps needed to do inference on tkDNN with a custom neural network.