

You can customize the string that appears in your shortened URL.įor example, you could create, rather than something random like. TinyURL is the best URL shortening solution for anonymous use. The number of team members, brands and monthly clicks varies in the different plans as do the customization options.

#CONDENSE URLS TRIAL#
All plans come with a 14 day free trial and a discount for annual payments. Integrates with other marketing and analytic tools to fine tune campaignsįour plans are available – Basic, Pro, Business and Agency at $29/mo, $79/mo, $149/mo and $299/mo respectively.Dashboard monitors to track user interaction.

#CONDENSE URLS CODE#
Thanks to Ross Wightman for building a powerful Pytorch Image Models repository, our training code is forked from his repository.A URL shortener is an online tool that takes a long, hefty URL and provides a shortened URL that sends the user to the exact same spot. Our work is inspired by CondenseNet: An Efficient DenseNet using Learned Group Convolutions and we use the code in the official repository of CondenseNet. Detection FrameworkĪny discussions or concerns are welcomed! Acknowledgments We simply replace the backbones of FasterRCNN and RetinaNet with our CondenseNetV2s. The detection experiments are conducted based on the mmdetection repository. Python convert_and_eval.py -model cdnv2_a/b/c \ Our experiments show that the proposed models achieve promising performance on image classification (ImageNet and CIFAR) and object detection (MS COCO) in terms of both theoretical efficiency and practical speed.Īs an example, use the following command to train a CondenseNetV2-A/B/C on ImageNet
#CONDENSE URLS UPDATE#
In the proposed network, named CondenseNetV2, each layer can simultaneously learn to 1) selectively reuse a set of most important features from preceding layers and 2) actively update a set of preceding features to increase their utility for later layers. In this paper, we propose an alternative approach named sparse feature reactivation (SFR), aiming at actively increasing the utility of features for reusing. The recent proposed CondenseNet has shown that this mechanism can be further improved if redundant features are removed.

Reusing features in deep networks through dense connectivity is an effective way to achieve high computational efficiency. V2: Sparse Feature Reactivation for Deep Networks},Īuthor=,
