metadata.json

{
    "title": "Chest x-ray image classifier",
    "summary": "Classify chest x-ray images in patological and non patological with this x-ray classifier.",
    "description": [
        "The deep learning revolution has brought significant advances in a number of fields [1], primarily linked to",
        "image and speech recognition. The standardization of image classification tasks like the [ImageNet Large Scale",
        "Visual Recognition Challenge](http://www.image-net.org/challenges/LSVRC/) [2] has resulted in a reliable way to",
        "compare top performing architectures.\n",

        "This Docker container contains the tools to train an image classifier on your personal dataset. It is a highly",
        "customizable tool  that let's you choose between tens of different [top performing architectures](https://github.com/keras-team/keras-applications)",
        "and training parameters.\n",

        "The container also comes with a pretrained general-purpose image classifier trained on ImageNet.\n",

        "The PREDICT method expects an RGB image as input (or the url of an RGB image) and will return a JSON with ",
        "the top 5 predictions.\n",

        "<img class='fit', src='https://raw.githubusercontent.com/deephdc/DEEP-OC-image-classification-tf-dicom/master/images/imagenet.png'/>\n",

        "**References**\n",
        "[1]: Yann LeCun, Yoshua Bengio, and Geofrey Hinton. [Deep learning](https://www.cs.toronto.edu/~hinton/absps/NatureDeepReview.pdf). Nature, 521(7553):436-444, May 2015.\n",
        "[2]: Olga Russakovsky et al. [ImageNet Large Scale Visual Recognition Challenge](https://arxiv.org/abs/1409.0575). International Journal of Computer Vision (IJCV), 115(3):211-252, 2015.\n"
    ],
    "keywords": [
        "tensorflow", "docker", "deep learning", "inference", "pre-trained", "image classification",  "api-v2", "trainable"
    ],
    "license": "Apache 2.0",
    "date_creation": "2019-01-01",
    "training_files_url": "https://cephrgw01.ifca.es:8080/swift/v1/imagenet-tf/",
    "dataset_url": "http://www.image-net.org/challenges/LSVRC/",
    "cite_url": "http://digital.csic.es/handle/10261/194498",
    "sources": {
		"dockerfile_repo": "https://github.com/deephdc/DEEP-OC-image-classification-tf-dicom",
		"docker_registry_repo": "deephdc/deep-oc-image-classification-tf-dicom",
		"code": "https://github.com/deephdc/image-classification-tf-dicom"
	},
    "continuous_integration": {
        "build_status_badge": "https://jenkins.indigo-datacloud.eu/buildStatus/icon?job=Pipeline-as-code/DEEP-OC-org/DEEP-OC-image-classification-tf-dicom/master",
        "build_status_url": "https://jenkins.indigo-datacloud.eu/job/Pipeline-as-code/job/DEEP-OC-org/job/DEEP-OC-image-classification-tf-dicom/job/master"
    },
    "tosca": [
        {
            "title": "Marathon default",
            "url": "https://raw.githubusercontent.com/indigo-dc/tosca-templates/master/deep-oc/deep-oc-marathon-webdav.yml",
            "inputs": [
                "rclone_conf",
                "rclone_url",
                "rclone_vendor",
                "rclone_user",
                "rclone_pass"
            ]
        }
    ]
}