Issue changed net_segmentation => net_segment in demos/PROMISE12/PROMISE12_Demo_Notebook.ipynb
"metadata": {},
"source": [
"## Training a network from the command line\n",
- "The simplest way to use NiftyNet is via the commandline net_segmentation.py script. Normally, this is done on the command line with a command like this from the NiftyNet root directory:\n",
+ "The simplest way to use NiftyNet is via the commandline net_segment.py script. Normally, this is done on the command line with a command like this from the NiftyNet root directory:\n",
"\n",
- "```python net_segmentation.py train --conf demo/PROMISE12/promise12_demo_train_config.ini --image_size 32 --label_size 32 --max_iter 10```\n",
+ "```python net_segment.py train --conf demo/PROMISE12/promise12_demo_train_config.ini --image_size 32 --label_size 32 --max_iter 10```\n",
"\n",
"Notice that we use configuration file that is specific to this experiment. This file contains default settings. Also note that we can override these settings on the command line.\n",
"\n",
"source": [
"Now you have trained (a few iterations of) a deep learning network for medical image segmentation. If you have some time on your hands, you can finish training the network (by leaving off the max_iter argument) and try it out, by running the following command\n",
"\n",
- "```python net_segmentation.py inference --conf demo/PROMISE12/promise12_demo_inference_config.ini --image_size 32 --label_size 32```\n",
+ "```python net_segment.py inference --conf demo/PROMISE12/promise12_demo_inference_config.ini --image_size 32 --label_size 32```\n",
"\n",
"or the following python code in the Notebook"
]
"source": [
"Otherwise, you can load up some pre-trained weights for the network:\n",
"\n",
- "```python net_segmentation.py inference --conf demo/PROMISE12/promise12_demo_config.ini --model_dir demo/PROMISE12/pretrained```\n",
+ "```python net_segment.py inference --conf demo/PROMISE12/promise12_demo_config.ini --model_dir demo/PROMISE12/pretrained```\n",
"or the following python code in the Notebook"
]
},
"cell_type": "markdown",
"metadata": {},
"source": [
- "You can use helper functions to parse commandline parameters and automatically match patient data (which is what net_segmentation.py did)."
+ "You can use helper functions to parse commandline parameters and automatically match patient data (which is what net_segment.py did)."
]
},
{
