Nic-Ma commited on
Commit
b6adca4
1 Parent(s): 380435d

Add commands to README

Browse files
Files changed (1) hide show
  1. README.md +22 -7
README.md CHANGED
@@ -6,7 +6,7 @@ tags:
6
  A pre-trained model for volumetric (3D) segmentation of the spleen from CT image.
7
 
8
  # Model Overview
9
- This model is trained using the runnerup [1] awarded pipeline of the "Medical Segmentation Decathlon Challenge 2018" using the UNet architecture [2] with 32 training images and 9 validation images.
10
 
11
  ## Data
12
  The training dataset is Task09_Spleen.tar from http://medicaldecathlon.com/.
@@ -22,19 +22,34 @@ Input: 1 channel CT image
22
  Output: 2 channels: Label 1: spleen; Label 0: everything else
23
 
24
  ## Scores
25
- This model achieve the following Dice score on the validation data (our own split from the training dataset):
26
 
27
- Mean dice = 0.96
28
 
29
  ## commands example
30
  Execute inference:
31
- `python -m monai.bundle run evaluator --meta_file configs/metadata.json --config_file configs/inference.json`
 
 
 
 
32
  Verify the metadata format:
33
- `python -m monai.bundle verify_metadata --meta_file configs/metadata.json --filepath eval/schema.json`
 
 
 
 
34
  Verify the data shape of network:
35
- `python -m monai.bundle verify_net_in_out network_def --meta_file configs/metadata.json --config_file configs/inference.json`
 
 
 
 
36
  Export checkpoint to TorchScript file:
37
- `python -m monai.bundle export network_def --filepath models/model.ts --ckpt_file models/model.pt --meta_file configs/metadata.json --config_file configs/inference.json`
 
 
 
38
 
39
  # Disclaimer
40
  This is an example, not to be used for diagnostic purposes.
 
6
  A pre-trained model for volumetric (3D) segmentation of the spleen from CT image.
7
 
8
  # Model Overview
9
+ This model is trained using the runner-up [1] awarded pipeline of the "Medical Segmentation Decathlon Challenge 2018" using the UNet architecture [2] with 32 training images and 9 validation images.
10
 
11
  ## Data
12
  The training dataset is Task09_Spleen.tar from http://medicaldecathlon.com/.
 
22
  Output: 2 channels: Label 1: spleen; Label 0: everything else
23
 
24
  ## Scores
25
+ This model achieves the following Dice score on the validation data (our own split from the training dataset):
26
 
27
+ Mean Dice = 0.96
28
 
29
  ## commands example
30
  Execute inference:
31
+
32
+ ```
33
+ python -m monai.bundle run evaluator --meta_file configs/metadata.json --config_file configs/inference.json --logging_file configs/logging.conf
34
+ ```
35
+
36
  Verify the metadata format:
37
+
38
+ ```
39
+ python -m monai.bundle verify_metadata --meta_file configs/metadata.json --filepath eval/schema.json
40
+ ```
41
+
42
  Verify the data shape of network:
43
+
44
+ ```
45
+ python -m monai.bundle verify_net_in_out network_def --meta_file configs/metadata.json --config_file configs/inference.json
46
+ ```
47
+
48
  Export checkpoint to TorchScript file:
49
+
50
+ ```
51
+ python -m monai.bundle export network_def --filepath models/model.ts --ckpt_file models/model.pt --meta_file configs/metadata.json --config_file configs/inference.json
52
+ ```
53
 
54
  # Disclaimer
55
  This is an example, not to be used for diagnostic purposes.