Showing 3 open source projects for "artificial images"

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    Software Testing Platform | Testeum

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    Tired of bugs and poor UX going unnoticed despite thorough internal testing? Testeum is the SaaS crowdtesting platform that connects mobile and web app creators with carefully selected testers based on your criteria.
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    Enterprise and Small Business CRM Solution | Clear C2 C2CRM

    Voted Best CRM System with Top Ranked Customer Support. CRM Management includes Sales, Marketing, Relationship Management, and Help Desk.

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  • 1
    AWS Deep Learning Containers

    AWS Deep Learning Containers

    A set of Docker images for training and serving models in TensorFlow

    AWS Deep Learning Containers (DLCs) are a set of Docker images for training and serving models in TensorFlow, TensorFlow 2, PyTorch, and MXNet. Deep Learning Containers provide optimized environments with TensorFlow and MXNet, Nvidia CUDA (for GPU instances), and Intel MKL (for CPU instances) libraries and are available in the Amazon Elastic Container Registry (Amazon ECR). The AWS DLCs are used in Amazon SageMaker as the default vehicles for your SageMaker jobs such as training, inference...
    Downloads: 0 This Week
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  • 2
    TensorFlow.js models

    TensorFlow.js models

    Pretrained models for TensorFlow.js

    This repository hosts a set of pre-trained models that have been ported to TensorFlow.js. The models are hosted on NPM and unpkg so they can be used in any project out of the box. They can be used directly or used in a transfer learning setting with TensorFlow.js. To find out about APIs for models, look at the README in each of the respective directories. In general, we try to hide tensors so the API can be used by non-machine learning experts. New models should have a test NPM script. You...
    Downloads: 0 This Week
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  • 3
    Image classification models for Keras

    Image classification models for Keras

    Keras code and weights files for popular deep learning models

    All architectures are compatible with both TensorFlow and Theano, and upon instantiation the models will be built according to the image dimension ordering set in your Keras configuration file at ~/.keras/keras.json. For instance, if you have set image_dim_ordering=tf, then any model loaded from this repository will get built according to the TensorFlow dimension ordering convention, "Width-Height-Depth". Pre-trained weights can be automatically loaded upon instantiation (weights='imagenet'...
    Downloads: 2 This Week
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