{
    "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20240725.json",
    "version": "0.5.1",
    "changelog": {
        "0.5.1": "enhance metadata with improved descriptions and task specification",
        "0.5.0": "update to huggingface hosting and fix missing dependencies",
        "0.4.9": "use monai 1.4 and update large files",
        "0.4.8": "update to use monai 1.3.1",
        "0.4.7": "add load_pretrain flag for infer",
        "0.4.6": "add output for inference",
        "0.4.5": "update with EnsureChannelFirstd and remove meta dict usage",
        "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": "1.4.0",
    "pytorch_version": "2.4.0",
    "numpy_version": "1.24.4",
    "required_packages_version": {
        "nibabel": "5.2.1",
        "pytorch-ignite": "0.4.11",
        "pillow": "10.4.0",
        "tensorboard": "2.17.0"
    },
    "supported_apps": {},
    "name": "Endoscopic In-Body Classification",
    "task": "Endoscopic Frame Classification for In-Body vs Out-Body Detection",
    "description": "A binary classification model based on SENet that distinguishes between inside-body and outside-body frames in endoscopic videos. The model processes 256x256 pixel RGB images and filters irrelevant frames, enabling automated procedure analysis.",
    "authors": "MONAI team",
    "copyright": "Copyright (c) MONAI Consortium",
    "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
    },
    "intended_use": "This is a research tool/prototype and not to be used clinically",
    "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"
                }
            }
        }
    }
}