You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
Copy file name to clipboardExpand all lines: auto3dseg/README.md
+8-7Lines changed: 8 additions & 7 deletions
Display the source diff
Display the rich diff
Original file line number
Diff line number
Diff line change
@@ -58,14 +58,15 @@ To further demonstrate the capabilities of **Auto3DSeg**, [here](./tasks/instanc
58
58
59
59
## Running With Your Own Data
60
60
61
-
To run Auto3DSeg on your own dataset, all you need to do is build a `datalist.json` file for your dataset, and run the AutoRunner on it.
61
+
To run Auto3DSeg on your own dataset, you need to build a `datalist.json` file for your dataset, and run the AutoRunner on it.
62
62
63
-
The datalist format is based on the datasets released by the (Medical Segmentation Decathlon)[http://medicaldecathlon.com].
63
+
The datalist format is based on the datasets released by the [Medical Segmentation Decathlon](http://medicaldecathlon.com).
64
64
See the function `load_decathlon_datalist` in `monai/data/decathlon_datalist.py` for a description of the format.
65
65
66
-
For the AutoRunner, we only need the `training` data, since it will automatically create cross-validation folds.
67
-
You are free to add the cross-validation folds beforehand, these should align with the number of folds set in the configuration of the AutoRunner (by default 5, see [notebook](notebooks/auto_runner.ipynb)).
68
-
Any other metadata, such as `modality`, `numTraining`, `name`, etc. will not be used by the AutoRunner, but we do recommend adding them, to keep track of names and versions of the dataset.
66
+
For the AutoRunner, we only need the `training` list in the JSON, it does not use any other fields.
67
+
The `fold` key for each image is not required, as the AutoRunner will automatically create cross-validation folds.
68
+
If you do add the cross-validation folds beforehand, these should align with the number of folds set in the configuration of the AutoRunner (by default 5, see [notebook](notebooks/auto_runner.ipynb)).
69
+
Any other metadata, such as `modality`, `numTraining`, `name`, etc. will not be used by the AutoRunner, but we do recommend using metadata fields to keep track of names and versions of your dataset.
69
70
In short, your `datalist.json` file should look like this:
70
71
71
72
```
@@ -81,9 +82,9 @@ In short, your `datalist.json` file should look like this:
81
82
82
83
```
83
84
84
-
The AutoRunner will create a `work_dir` folder in the directory from which it is ran, with the resulting models and the copied datalist file _with_ cross-validation folds. This allows you to see which datalist file the models are trained on.
85
+
The AutoRunner will create a `work_dir` folder in the directory from which it is ran, which will contain the resulting models and the copied datalist file _with_ cross-validation folds. This allows you to keep track of which datalist file the models are trained on.
85
86
86
-
See the description below or the file [run_with_minimal_input.md](docs/run_with_minimal_input.md)how to use your datalist with the AutoRunner.
87
+
See the description below or the file [run_with_minimal_input.md](docs/run_with_minimal_input.md) to use your datalist with the AutoRunner.
0 commit comments