From 8e7a355fab7de8fc6c4c8d8221c1f529e8a86d9d Mon Sep 17 00:00:00 2001 From: Francesco Gatti Date: Mon, 1 Jun 2020 19:04:39 +0200 Subject: [PATCH] readme update --- README.md | 29 ++++++++++++++++++++++++++++- 1 file changed, 28 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 0593bcb..3a73bec 100644 --- a/README.md +++ b/README.md @@ -104,7 +104,6 @@ python demo.py --input_res 512 --arch resdcn_101 ctdet --demo /path/to/image/or/ python demo.py --input_res 512 --arch dla_34 ctdet --demo /path/to/image/or/folder/or/video/or/webcam --load_model ../models/ctdet_coco_dla_2x.pth --exp_wo --exp_wo_dim 512 ``` ### 4)Export weights for MobileNetSSD - To get the weights needed to run Mobilenet tests use [this](https://github.com/mive93/pytorch-ssd) fork of a Pytorch implementation of SSD network. ``` @@ -113,6 +112,34 @@ cd pytorch-ssd conda env create -f env_mobv2ssd.yml python run_ssd_live_demo.py mb2-ssd-lite ``` + +## Darknet Parser +tkDNN implement and easy parser for darknet cfg files, a network can be converted with *tk::dnn::darknetParser*: +``` +// example of parsing yolo4 +tk::dnn::Network *net = tk::dnn::darknetParser("yolov4.cfg", "yolov4/layers", "coco.names"); +net->print(); +``` +All models from darknet are now parsed directly from cfg, you still need to export the weights with the descripted tools in the previus section. +
+ Supported layers + convolutional + maxpool + avgpool + shortcut + upsample + route + reorg + region + yolo +
+
+ Supported activations + relu + leaky + mish +
+ ## Run the demo To run the an object detection demo follow these steps (example with yolov3):