| { | |
| "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" | |
| } | |
| } | |
| } | |
| } | |
| } | |