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Aujourd'hui 4 nouvelles :


  • MobileNets: Open Source Models for Efficient On-Device Vision, par Open Source Programs Office, 14 juin 2017

    mercredi 14 juin 2017 :: Google Open Source Blog :: RSS
    Crossposted on the Google Research Blog

    Deep learning has fueled tremendous progress in the field of computer vision in recent years, with neural networks repeatedly pushing the frontier of visual recognition technology. While many of those technologies such as object, landmark, logo and text recognition are provided for internet-connected devices through the Cloud Vision API, we believe that the ever-increasing computational power of mobile devices can enable the delivery of these technologies into the hands of our users, anytime, anywhere, regardless of internet connection. However, visual recognition for on device and embedded applications poses many challenges — models must run quickly with high accuracy in a resource-constrained environment making use of limited computation, power and space.

    Today we are pleased to announce the release of MobileNets, a family of mobile-first computer vision models for TensorFlow, designed to effectively maximize accuracy while being mindful of the restricted resources for an on-device or embedded application. MobileNets are small, low-latency, low-power models parameterized to meet the resource constraints of a variety of use cases. They can be built upon for classification, detection, embeddings and segmentation similar to how other popular large scale models, such as Inception, are used.
    Example use cases include detection, fine-grain classification, attributes and geo-localization.
    This release contains the model definition for MobileNets in TensorFlow using TF-Slim, as well as 16 pre-trained ImageNet classification checkpoints for use in mobile projects of all sizes. The models can be run efficiently on mobile devices with TensorFlow Mobile.
    Model Checkpoint
    Million MACs
    Million Parameters
    Top-1 Accuracy
    Top-5 Accuracy
    569
    4.24
    70.7
    89.5
    418
    4.24
    69.3
    88.9
    291
    4.24
    67.2
    87.5
    186
    4.24
    64.1
    85.3
    317
    2.59
    68.4
    88.2
    233
    2.59
    67.4
    87.3
    162
    2.59
    65.2
    86.1
    104
    2.59
    61.8
    83.6
    150
    1.34
    64.0
    85.4
    110
    1.34
    62.1
    84.0
    77
    1.34
    59.9
    82.5
    49
    1.34
    56.2
    79.6
    41
    0.47
    50.6
    75.0
    34
    0.47
    49.0
    73.6
    21
    0.47
    46.0
    70.7
    14
    0.47
    41.3
    66.2
    Choose the right MobileNet model to fit your latency and size budget. The size of the network in memory and on disk is proportional to the number of parameters. The latency and power usage of the network scales with the number of Multiply-Accumulates (MACs) which measures the number of fused Multiplication and Addition operations. Top-1 and Top-5 accuracies are measured on the ILSVRC dataset.
    We are excited to share MobileNets with the open source community. Information for getting started can be found at the TensorFlow-Slim Image Classification Library. To learn how to run models on-device please go to TensorFlow Mobile. You can read more about the technical details of MobileNets in our paper, MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications.

    By Andrew G. Howard, Senior Software Engineer and Menglong Zhu, Software Engineer

    Acknowledgements
    MobileNets were made possible with the hard work of many engineers and researchers throughout Google. Specifically we would like to thank:

    Core Contributors: Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, Hartwig Adam

    Special thanks to: Benoit Jacob, Skirmantas Kligys, George Papandreou, Liang-Chieh Chen, Derek Chow, Sergio Guadarrama, Jonathan Huang, Andre Hentz, Pete Warden
    Lire la suite Open Source Programs Office
  • INGÉNIEUR DEVOPS (H/F) en Stage à Boulogne-Billancourt, 14 juin 2017

    mercredi 14 juin 2017 :: Remixjobs : emplois linux :: RSS
    Société : Netatmo     Lieu : Boulogne-Billancourt     Type : Stage     Rémunération : En fonction du profil     Posté le : 13 juin 2017
    Au sein de l’équipe d'ingénieurs infrastructure, vous serez en charge de concevoir puis de mettre en place une solution de log centralisé HA et performant ayant pour objectifs :
    • L’analyse en temps réel l'ensemble de nos donnees.
    • La détection automatique des anomalies de l’infrastructure.
    • Exemple de stack: ELK (elasticsearch - logstash - kibana)
    • Exemples d’utilisation :  - Dashboard des metrics utilisateurs, - Détection d’attaques sur nos applicatifs, - Détection de commandes exécutées non autorisées, - Simplification du processus de debug applicatif.

    Infrastructure:
    • Serveurs: 60+
    • ~2000+ logs/sec sur l'ensemble de l'Infra
    Lire la suite
  • Mozilla Launches Campaign to Raise Awareness for Internet Health, par Nefi Alarcon, 14 juin 2017

    mercredi 14 juin 2017 :: The Mozilla Blog :: RSS
    Today, Mozilla unveils several initiatives including an event focused on Internet Health with special guests DeRay McKesson, Lauren Duca and more, a brand new podcast, new tech to help create … Read more The post Mozilla Launches Campaign to Raise Awareness for Internet Health appeared first on (...) Lire la suite Nefi Alarcon
  • Technicien Réseaux et Systèmes (H/F) en CDI, 14 juin 2017

    mercredi 14 juin 2017 :: Remixjobs : emplois linux :: RSS
    Société : Altima     Type : CDI     Rémunération : En fonction du profil     Posté le : 9 juin 2017
    Intégré(e) à l'équipe Hosting d'altima°, vous aurez pour missions :
    • Le paramétrage des comptes Windows, gestion des droits, messageries
    • Intervention sur l'offre d'hébergement de solutions e-Commerce pour laquelle vous serez en contact direct avec nos clients
    • Prise en charge des demandes de niveau 1




    Lire la suite