355-Golden Raand Groninger Landschap Computer vision is a fast-growing subfield of AI

  • https://www.projectpro.io/article/tensorflow-projects-ideas-for-beginners/455
  • https://learnopencv.com/top-10-sources-to-find-computer-vision-and-ai-models/
    The AI community generously shares code, model architectures, and even models trained on large datasets. We are standing on the shoulders of giants, which is why the industry is adopting AI so widely.

    When we start a computer vision project, we first find models that partially solve our problem.

    Let’s say you want to build a security application that looks for humans in restricted areas. First, check if a publicly available pedestrian detection model works for you out of the box. If it does, you do not need to train a new model. If not, experimenting with publicly available models will give you an idea of which architecture to choose for finetuning or transfer learning.

    Today, we will learn about free resources for computer vision, machine learning, and AI models.


  • https://deci.ai/blog/model-zoos-deep-learning-computer-vision/

    Computer vision is a fast-growing subfield of AI and deep learning. From cashierless stores in retail to crop detection in agriculture, there’s an increasing interest in CV applications. This has created a vibrant community that gladly shares architectures, codes, pre-trained models, and even tips for every stage of the development cycle.

    Starting a CV project from scratch takes time. So, the usual process is, given a problem or a use case, you look for models that partially solve it. If one already exists, then training a new model is no longer necessary. Otherwise, you use the publicly available models to guide you through which architecture to choose as a base for fine-tuning or transfer learning.
    . https://www.researchgate.net/publication/291165298_Atlas_of_the_European_dragonflies_and_damselflies
    In this post, we list 13 free resources and model zoos for deep learning and computer vision models that can hopefully help to simplify your work.

  • http://extremambiente.juntaex.es/files/biblioteca_digital/atlas_odonatos.pdf
  • . https://www.researchgate.net/publication/291165298_Atlas_of_the_European_dragonflies_and_damselflies
    r een https://www.projectpro.io/article/tensorflow-projects-ideas-for-beginners/455

  • NVIDIA RTX 8000 https://tweakers.net/pricewatch/1325194/pny-nvidia-quadro-rtx-8000.html

    New hardware: Previously we trained on three NVIDIA GP100 cards, this time we trained on a single NVIDIA RTX 8000. This is a newer, faster GPU with as much VRAM on a single GPU as 3 GP100s, and it supports mixed precision training.
    New ML stack: Previously we trained using code written for Tensorflow 1 (TF1), this time we trained on code written for Tensorflow 2 (TF2). In addition to being newer, TF2 does a lot of things for us like automatically sizing and managing pre-processing queues to keep the GPUs fed, or orchestrating multi-GPU training and weight sharing. TF2 also supports mixed precision training.
    New vision model: Previously we trained an Inception v3 model, this time we trained an Xception model. Xception shares a lot of design features with Inception, but it claims to be better suited to very large datasets like iNaturalist has been growing into. The downsides are that it requires more CPU power and takes up more memory.
    New training code: Previously we trained our models using code that our collaborator Grant van Horn wrote as part of his PhD dissertation. This code was written for Python2 and a TF1 variant called tensorflow-slim, both of which have been end-of-lifed. This time we trained using a new codebase that we developed to take advantage of TF2 and Python 3.

  • https://techbrij.com/setup-tensorflow-jupyter-notebook-vscode-deep-learning

  • https://arxiv.org/ftp/arxiv/papers/2102/2102.01863.pdf Als je zoekt op de eerste regel van je tekst kan je al best veel vinden..

  • github.com/joergmlpts/nature-id
    Google provides three models that have been trained with iNaturalist data - classification models for plants, birds, and insects. These Google models can be downloaded and used with Google's TensorFlow and TensorFlow Lite tools.

  • https://www.projectpro.io/article/tensorflow-projects-ideas-for-beginners/455 lpatop click in veel?
  • his work is the first detailed and comprehensive overview of the distribution of the dragonflies and damselflies of Europe. It is an important milestone for professionals and amateurs alike. Covers the distribution and habitat selection of all 143 European species of dragonflies and damselflies. Gives a complete description of their global and European distribution, illustrated by over 200 distribution maps. Gives for each species information on taxonomy, range, population trends, flights season and habitat. Includes unique photos and flight season diagrams for virtually all European species. Contains extensive background information on taxonomy, conservation, and for each country an overview of the history of odonatological studies. The book is the result of a co-operation of over 50 European dragonfly experts who over the past decade compiled all records of dragonflies and damselflies, from the Azores to the Ural and from the North Cape to Lampedusa. These records were gathered by thousands of volunteers from across Europe. This endeavour was coordinated by Jean- Pierre Boudot (Société Française d’Odonatologie) and Vincent Kalkman (European Invertebrate Survey – Netherlands/Naturalis Biodiversity Centre). To download the file, please click on 'More' and then to 'Download' on the menu above.

  • anavond en i.i.g. ook nog morgen op RTV West :

    de documentaire Zand, Wind, Water

    (over natte duinvalleien o.a. Meijendel in Scheveningen)

    10 jaar natuurherstel in de duinen van Zuid-Holland

    vogels, planten, insecten, zoogdieren, reptielen, alles komt aan bod

    prachtige docu van ong. 25 minuten AANRADER

  • https://pyimagesearch.com/2015/09/07/blur-detection-with-opencv/

  • https://www.nlbif.nl/beeldherkenning-nederlands-caribisch-gebied/

    Naturalis Biodiversity Center, Observation International en COSMONiO hebben afgelopen jaren gezamenlijk gewerkt aan het beschikbaar maken van beeldherkenning voor de dieren en planten die in Nederland en België voorkomen. ObsIdentify, de hiervoor gebouwde app, is in korte tijd zeer populair geworden en heeft geleid tot een toename van het aantal waarnemers, een sterke toename van het aantal waarnemingen en een verbeterde betrouwbaarheid van de waarnemingen. De huidige beeldherkenning faciliteert de herkenning van 22.000 soorten uit de Benelux maar is niet geschikt voor het Nederlands Caribisch gebied, oftewel de eilanden Aruba, Bonaire, Curaçao, Saba, Sint Eustatius en Sint Maarten.

    Met dit project gaat de Dutch Caribbean Nature Alliance (DCNA), Naturalis Biodiversity Center en Observation International deze omissie verhelpen. Hiervoor gaan ze een beeldherkenningmodel maken gericht op de mariene en terrestrische soorten van het Nederlands Caribisch gebied om dat vervolgens gratis via internet en een app vrij beschikbaar te stellen aan alle waarnemers in verschillende talen. Daarnaast wordt het doorgeven van waarnemingen via de app gepromoot en wordt er een actief validatoren team opgezet voor de kwaliteitscontrole. Deze activiteiten zullen leiden tot meer waarnemers, meer waarnemingen en een hogere kwaliteit aan data in het Caribisch deel van het Koninkrijk der Nederlanden.


  • https://www.youtube.com/watch?v=oE71KRItpOk

  • https://www.youtube.com/watch?v=jpJmKkOMsGk

  • https://www.natuurkennis.nl/publicaties-extra/rapporten-provincies/noord-brabant/

  • https://landvanons.nl/wp-content/uploads/2021/04/Beheerplan-Onneresch-februari-2-2022.pdf

  • Conversation Dragonflies Europe Biodiversity

  • Fieldguide Damselfies New Guinea

  • Fieldguide Damselfies Dragonflies

  • Snelle toolBrachtron artikelen

  • Atlas of the European dragonflies and damselflies

  • https://ecuador.inaturalist.org/journal/ahospers/70169-335-download-italian-bird-migration-atlas

  • https://paddenstoelenwerkgroepdrent.files.wordpress.com/2019/12/def-deel-1-paddenstoelenatlas-drenthe-hfd-1.pdf
  • https://vlinderwerkgroepdrenthe.nl/dagvlinderatlas-op-deze-website-te-bekijken/

  • https://ebba2.info/maps/
    Atlas Portugal, Hongarije, Zwitserland

  • Publicado el noviembre 9, 2022 04:56 TARDE por ahospers ahospers


    No hay comentarios todavía.

    Agregar un comentario

    Acceder o Crear una cuenta para agregar comentarios.