Image Segmentation
Transformers
PyTorch
repcontur
feature-extraction
medical
radiology
ct
3d
tumor-segmentation
referring-segmentation
report-generation
visual-question-answering
custom_code
Instructions to use Oncosoft-Research/RepConTUR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Oncosoft-Research/RepConTUR with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="Oncosoft-Research/RepConTUR", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Oncosoft-Research/RepConTUR", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 4,135 Bytes
c6be546 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 | {
"dataset_name": "VoxTellDataset",
"image_reader_writer": "NibabelIOWithReorient",
"configurations": {
"3d_fullres": {
"data_identifier": "nnUNetResEncUNetLPlans_noResampling_3d_fullres",
"preprocessor_name": "DefaultPreprocessor",
"batch_size": 2,
"patch_size": [
192,
192,
192
],
"normalization_schemes": [
"ZScoreNormalization"
],
"architecture": {
"network_class_name": "dynamic_network_architectures.architectures.unet.ResidualEncoderUNet",
"arch_kwargs": {
"n_stages": 6,
"features_per_stage": [
32,
64,
128,
256,
320,
320
],
"conv_op": "torch.nn.modules.conv.Conv3d",
"kernel_sizes": [
[
3,
3,
3
],
[
3,
3,
3
],
[
3,
3,
3
],
[
3,
3,
3
],
[
3,
3,
3
],
[
3,
3,
3
]
],
"strides": [
[
1,
1,
1
],
[
2,
2,
2
],
[
2,
2,
2
],
[
2,
2,
2
],
[
2,
2,
2
],
[
2,
2,
2
]
],
"n_blocks_per_stage": [
1,
3,
4,
6,
6,
6
],
"n_conv_per_stage_decoder": [
1,
1,
1,
1,
1
],
"conv_bias": true,
"norm_op": "torch.nn.modules.instancenorm.InstanceNorm3d",
"norm_op_kwargs": {
"eps": 1e-05,
"affine": true
},
"dropout_op": null,
"dropout_op_kwargs": null,
"nonlin": "torch.nn.LeakyReLU",
"nonlin_kwargs": {
"inplace": true
}
},
"_kw_requires_import": [
"conv_op",
"norm_op",
"dropout_op",
"nonlin"
]
},
"batch_dice": false
}
},
"experiment_planner_used": "nnUNetPlannerResEncL_noResampling"
} |