From 9e08f4125abea1458df69601f698bb673ea0647f Mon Sep 17 00:00:00 2001 From: Micaela Verucchi Date: Thu, 2 Apr 2020 09:41:24 +0200 Subject: [PATCH] Modify weights download link for mobilenetv2ssd512 and yolo3_tiny512 Signed-off-by: Micaela Verucchi --- README.md | 12 ++++++------ tests/mobilenetv2ssd512/mobilenetv2ssd512.cpp | 2 +- 2 files changed, 7 insertions(+), 7 deletions(-) diff --git a/README.md b/README.md index ee8bb1e..d308dc7 100644 --- a/README.md +++ b/README.md @@ -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 layers @@ -187,16 +187,16 @@ cd build | yolo3_coco4 | YOLO v32 | [COCO 2014](http://cocodataset.org/) | 4 | 416x416 | [weights](https://cloud.hipert.unimore.it/s/o27NDzSAartbyc4/download) | | yolo3_flir | YOLO v32 | [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 tiny2 | [COCO 2014](http://cocodataset.org/) | 80 | 416x416 | [weights](https://cloud.hipert.unimore.it/s/LMcSHtWaLeps8yN/download) | -| yolo3_tiny512 | YOLO v3 tiny2 | [COCO 2017](http://cocodataset.org/) | 80 | 512x512 | [weights](https://cloud.hipert.unimore.it/s/njnYACnQfWQFKrn/download) | +| yolo3_tiny512 | YOLO v3 tiny2 | [COCO 2017](http://cocodataset.org/) | 80 | 512x512 | [weights](https://cloud.hipert.unimore.it/s/wRW9nmkibSe5HoS/download) | | dla34 | Deep Leayer Aggreagtion (DLA) 343 | [COCO 2014](http://cocodataset.org/) | 80 | 224x224 | weights | | dla34_cnet | Centernet (DLA34 backend)4 | [COCO 2017](http://cocodataset.org/) | 80 | 512x512 | [weights](https://cloud.hipert.unimore.it/s/8AjXdgCeRzCa5AF/download) | | mobilenetv2ssd | Mobilnet v2 SSD Lite5 | [VOC ](http://host.robots.ox.ac.uk/pascal/VOC/) | 21 | 300x300 | [weights](https://cloud.hipert.unimore.it/s/x4ZfxBKN23zAJQp/download) | +| mobilenetv2ssd512 | Mobilnet v2 SSD Lite5 | [COCO 2017](http://cocodataset.org/) | 81 | 512x512 | [weights](https://cloud.hipert.unimore.it/s/x4ZfxBKN23zAJQp/download) | | resnet101 | Resnet 1016 | [COCO 2014](http://cocodataset.org/) | 80 | 224x224 | weights | -| resnet101_cnet | Centernet (Resnet101 backend)4 | [COCO 2017](http://cocodataset.org/) | 80 | 512x512 | [weights](https://cloud.hipert.unimore.it/s/B6mj33k7beECXsY/download) | +| resnet101_cnet | Centernet (Resnet101 backend)4 | [COCO 2017](http://cocodataset.org/) | 80 | 512x512 | [weights](https://cloud.hipert.unimore.it/s/Ye8f5JJPRo9AxCi/download) | | csresnext50-panet-spp | Cross Stage Partial Network 7 | [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. diff --git a/tests/mobilenetv2ssd512/mobilenetv2ssd512.cpp b/tests/mobilenetv2ssd512/mobilenetv2ssd512.cpp index 3927ab4..c8e3d57 100644 --- a/tests/mobilenetv2ssd512/mobilenetv2ssd512.cpp +++ b/tests/mobilenetv2ssd512/mobilenetv2ssd512.cpp @@ -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;