{
    "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20220324.json",
    "version": "0.4.4",
    "changelog": {
        "0.4.4": "fix the wrong GPU index issue of multi-node",
        "0.4.3": "add dataset dir example",
        "0.4.2": "update ONNX-TensorRT descriptions",
        "0.4.1": "update the model weights with the deterministic training",
        "0.4.0": "add the ONNX-TensorRT way of model conversion",
        "0.3.9": "fix mgpu finalize issue",
        "0.3.8": "enable deterministic training",
        "0.3.7": "adapt to BundleWorkflow interface",
        "0.3.6": "add name tag",
        "0.3.5": "fix a comment issue in the data_process script",
        "0.3.4": "add note for multi-gpu training with example dataset",
        "0.3.3": "enhance data preprocess script and readme file",
        "0.3.2": "restructure readme to match updated template",
        "0.3.1": "add workflow, train loss and validation accuracy figures",
        "0.3.0": "update dataset processing",
        "0.2.2": "update to use monai 1.0.1",
        "0.2.1": "enhance readme on commands example",
        "0.2.0": "update license files",
        "0.1.0": "complete the first version model package",
        "0.0.1": "initialize the model package structure"
    },
    "monai_version": "1.2.0",
    "pytorch_version": "1.13.1",
    "numpy_version": "1.22.2",
    "optional_packages_version": {
        "nibabel": "4.0.1",
        "pytorch-ignite": "0.4.9"
    },
    "name": "Endoscopic inbody classification",
    "task": "Endoscopic inbody classification",
    "description": "A pre-trained binary classification model for endoscopic inbody classification task",
    "authors": "NVIDIA DLMED team",
    "copyright": "Copyright (c) 2021-2022, NVIDIA CORPORATION",
    "data_source": "private dataset",
    "data_type": "RGB",
    "image_classes": "three channel data, intensity [0-255]",
    "label_classes": "0: inbody, 1: outbody",
    "pred_classes": "vector whose length equals to 2, [1,0] means in body, [0,1] means out body",
    "eval_metrics": {
        "accuracy": 0.99
    },
    "references": [
        "J. Hu, L. Shen and G. Sun, Squeeze-and-Excitation Networks, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018, pp. 7132-7141. https://arxiv.org/pdf/1709.01507.pdf"
    ],
    "network_data_format": {
        "inputs": {
            "image": {
                "type": "magnitude",
                "format": "RGB",
                "modality": "regular",
                "num_channels": 3,
                "spatial_shape": [
                    256,
                    256
                ],
                "dtype": "float32",
                "value_range": [
                    0,
                    1
                ],
                "is_patch_data": false,
                "channel_def": {
                    "0": "R",
                    "1": "G",
                    "2": "B"
                }
            }
        },
        "outputs": {
            "pred": {
                "type": "probabilities",
                "format": "classes",
                "num_channels": 2,
                "spatial_shape": [
                    1,
                    2
                ],
                "dtype": "float32",
                "value_range": [
                    0,
                    1
                ],
                "is_patch_data": false,
                "channel_def": {
                    "0": "in body",
                    "1": "out body"
                }
            }
        }
    }
}