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readme update

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Francesco Gatti
2020-06-01 19:04:39 +02:00
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@@ -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 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 ### 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. 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 conda env create -f env_mobv2ssd.yml
python run_ssd_live_demo.py mb2-ssd-lite <pth-model-fil> <labels-file> python run_ssd_live_demo.py mb2-ssd-lite <pth-model-fil> <labels-file>
``` ```
## 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.
<details>
<summary>Supported layers</summary>
convolutional
maxpool
avgpool
shortcut
upsample
route
reorg
region
yolo
</details>
<details>
<summary>Supported activations</summary>
relu
leaky
mish
</details>
## Run the demo ## Run the demo
To run the an object detection demo follow these steps (example with yolov3): To run the an object detection demo follow these steps (example with yolov3):