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Modify weights download link for mobilenetv2ssd512 and yolo3_tiny512

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Signed-off-by: Micaela Verucchi <micaelaverucchi@gmail.com>
This commit is contained in:
Micaela Verucchi
2020-04-02 09:41:24 +02:00
parent af13e7c954
commit 79a05a8159
2 changed files with 7 additions and 7 deletions
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README.md
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@@ -1,5 +1,5 @@
# tkDNN
tkDNN is a Deep Neural Network library built with cuDNN and tensorRT primitives, specifically thought to work on NVIDIA Embedded Boards. It has been tested on TK1, TX1, TX2, AGX Xavier and several discrete GPU.
tkDNN is a Deep Neural Network library built with cuDNN and tensorRT primitives, specifically thought to work on NVIDIA Jetson Boards. It has been tested on TK1(branch cudnn2), TX1, TX2, AGX Xavier and several discrete GPU.
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
@@ -27,7 +27,7 @@ This branch works on every NVIDIA GPU that supports the dependencies:
Build with cmake. If using Ubuntu 18.04 a new version of cmake is needed (1.15 or above).
```
git clone https://github.com/ceccocats/tkDNN
git cd tkDNN
cd tkDNN
git checkout cnet
mkdir build
cd build
@@ -60,7 +60,7 @@ To export weights for NNs that are defined in darknet framework, use [this](http
```
git clone https://github.com/ceccocats/darknet
git cd darknet
cd darknet
make
mkdir layers debug
./darknet export <path-to-cfg-file> <path-to-weights> layers
@@ -187,16 +187,16 @@ cd build
| yolo3_coco4 | YOLO v3<sup>2</sup> | [COCO 2014](http://cocodataset.org/) | 4 | 416x416 | [weights](https://cloud.hipert.unimore.it/s/o27NDzSAartbyc4/download) |
| yolo3_flir | YOLO v3<sup>2</sup> | [FREE FLIR](https://www.flir.com/oem/adas/adas-dataset-form/) | 3 | 320x544 | [weights](https://cloud.hipert.unimore.it/s/62DECncmF6bMMiH/download) |
| yolo3_tiny | YOLO v3 tiny<sup>2</sup> | [COCO 2014](http://cocodataset.org/) | 80 | 416x416 | [weights](https://cloud.hipert.unimore.it/s/LMcSHtWaLeps8yN/download) |
| yolo3_tiny512 | YOLO v3 tiny<sup>2</sup> | [COCO 2017](http://cocodataset.org/) | 80 | 512x512 | [weights](https://cloud.hipert.unimore.it/s/njnYACnQfWQFKrn/download) |
| yolo3_tiny512 | YOLO v3 tiny<sup>2</sup> | [COCO 2017](http://cocodataset.org/) | 80 | 512x512 | [weights](https://cloud.hipert.unimore.it/s/wRW9nmkibSe5HoS/download) |
| dla34 | Deep Leayer Aggreagtion (DLA) 34<sup>3</sup> | [COCO 2014](http://cocodataset.org/) | 80 | 224x224 | weights |
| dla34_cnet | Centernet (DLA34 backend)<sup>4</sup> | [COCO 2017](http://cocodataset.org/) | 80 | 512x512 | [weights](https://cloud.hipert.unimore.it/s/8AjXdgCeRzCa5AF/download) |
| mobilenetv2ssd | Mobilnet v2 SSD Lite<sup>5</sup> | [VOC ](http://host.robots.ox.ac.uk/pascal/VOC/) | 21 | 300x300 | [weights](https://cloud.hipert.unimore.it/s/x4ZfxBKN23zAJQp/download) |
| mobilenetv2ssd512 | Mobilnet v2 SSD Lite<sup>5</sup> | [COCO 2017](http://cocodataset.org/) | 81 | 512x512 | [weights](https://cloud.hipert.unimore.it/s/x4ZfxBKN23zAJQp/download) |
| resnet101 | Resnet 101<sup>6</sup> | [COCO 2014](http://cocodataset.org/) | 80 | 224x224 | weights |
| resnet101_cnet | Centernet (Resnet101 backend)<sup>4</sup> | [COCO 2017](http://cocodataset.org/) | 80 | 512x512 | [weights](https://cloud.hipert.unimore.it/s/B6mj33k7beECXsY/download) |
| resnet101_cnet | Centernet (Resnet101 backend)<sup>4</sup> | [COCO 2017](http://cocodataset.org/) | 80 | 512x512 | [weights](https://cloud.hipert.unimore.it/s/Ye8f5JJPRo9AxCi/download) |
| csresnext50-panet-spp | Cross Stage Partial Network <sup>7</sup> | [COCO 2014](http://cocodataset.org/) | 80 | 416x416 | [weights](https://cloud.hipert.unimore.it/s/Kcs4xBozwY4wFx8/download) |
## References
1. Redmon, Joseph, and Ali Farhadi. "YOLO9000: better, faster, stronger." Proceedings of the IEEE conference on computer vision and pattern recognition. 2017.
tests/mobilenetv2ssd512/mobilenetv2ssd512.cpp
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@@ -134,7 +134,7 @@ const char *regression_header5 = "../tests/mobilenetv2ssd512/layers/regression_h
int main()
{
// downloadWeightsifDoNotExist(input_bin, "./tests/mobilenetv2ssd512", "https://cloud.hipert.unimore.it/s//download");
downloadWeightsifDoNotExist(input_bin, "../tests/mobilenetv2ssd512", "https://cloud.hipert.unimore.it/s/Ye8f5JJPRo9AxCi/download");
int classes = 81;