File size: 4,766 Bytes
f5e7f43 f0416a2 f5e7f43 f0416a2 f5e7f43 f0416a2 f5e7f43 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 |
{
"schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20240725.json",
"version": "0.6.2",
"changelog": {
"0.6.2": "enhance metadata with improved descriptions and task specification",
"0.6.1": "update to huggingface hosting and fix missing dependencies",
"0.6.0": "use monai 1.4 and update large files",
"0.5.9": "update to use monai 1.3.1",
"0.5.8": "add load_pretrain flag for infer",
"0.5.7": "add checkpoint loader for infer",
"0.5.6": "update to use monai 1.3.0",
"0.5.5": "update AddChanneld with EnsureChannelFirstd and set image_only to False",
"0.5.4": "fix the wrong GPU index issue of multi-node",
"0.5.3": "remove error dollar symbol in readme",
"0.5.2": "remove the CheckpointLoader from the train.json",
"0.5.1": "add RAM warning",
"0.5.0": "update TensorRT descriptions",
"0.4.9": "update the model weights",
"0.4.8": "update the TensorRT part in the README file",
"0.4.7": "fix mgpu finalize issue",
"0.4.6": "enable deterministic training",
"0.4.5": "add the command of executing inference with TensorRT models",
"0.4.4": "adapt to BundleWorkflow interface",
"0.4.3": "update this bundle to support TensorRT convert",
"0.4.2": "support monai 1.2 new FlexibleUNet",
"0.4.1": "add name tag",
"0.4.0": "add support for multi-GPU training and evaluation",
"0.3.2": "restructure readme to match updated template",
"0.3.1": "add figures of workflow and metrics, add invert transform",
"0.3.0": "update dataset processing",
"0.2.1": "update to use monai 1.0.1",
"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 Tool Segmentation",
"task": "Binary Segmentation of Surgical Tools in Endoscopic Images",
"description": "A 2D segmentation model that identifies and delineates surgical instruments in endoscopic video frames. The model processes 736x480 pixel RGB images and provides binary segmentation masks. Based on an EfficientNet-UNet architecture, the model supports real-time analysis of surgical procedures.",
"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": "single channel data, 1/255 is tool, 0 is background",
"pred_classes": "2 channels OneHot data, channel 1 is tool, channel 0 is background",
"eval_metrics": {
"mean_iou": 0.86
},
"references": [
"Tan, M. and Le, Q. V. Efficientnet: Rethinking model scaling for convolutional neural networks. ICML, 2019a. https://arxiv.org/pdf/1905.11946.pdf",
"O. Ronneberger, P. Fischer, and T. Brox. U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention, pages 234\u2013241. Springer, 2015. https://arxiv.org/pdf/1505.04597.pdf"
],
"network_data_format": {
"inputs": {
"image": {
"type": "magnitude",
"format": "RGB",
"modality": "regular",
"num_channels": 3,
"spatial_shape": [
736,
480
],
"dtype": "float32",
"value_range": [
0,
1
],
"is_patch_data": false,
"channel_def": {
"0": "R",
"1": "G",
"2": "B"
}
}
},
"outputs": {
"pred": {
"type": "image",
"format": "segmentation",
"num_channels": 2,
"spatial_shape": [
736,
480
],
"dtype": "float32",
"value_range": [
0,
1
],
"is_patch_data": false,
"channel_def": {
"0": "background",
"1": "tools"
}
}
}
}
}
|