Update README Formatting

README.md

@@ -5,14 +5,13 @@ tags:
 library_name: monai
 license: apache-2.0
 ---
-# Description
+# Model Overview
 A pre-trained model for segmenting nuclei cells with user clicks/interactions.
 
 ![nuclick](https://github.com/mostafajahanifar/nuclick_torch/raw/master/docs/11.gif)
 ![nuclick](https://github.com/mostafajahanifar/nuclick_torch/raw/master/docs/33.gif)
 ![nuclick](https://github.com/mostafajahanifar/nuclick_torch/raw/master/docs/22.gif)
 
-# Model Overview
 This model is trained using [BasicUNet](https://docs.monai.io/en/latest/networks.html#basicunet) over [ConSeP](https://warwick.ac.uk/fac/cross_fac/tia/data/hovernet) dataset.
 
 ## Data
@@ -23,17 +22,6 @@ unzip -q consep_dataset.zip
 ```
 ![](https://developer.download.nvidia.com/assets/Clara/Images/monai_pathology_nuclick_annotation_dataset.jpeg)<br/>
 
-## Training configuration
-The training was performed with the following:
-
-- GPU: at least 12GB of GPU memory
-- Actual Model Input: 5 x 128 x 128
-- AMP: True
-- Optimizer: Adam
-- Learning Rate: 1e-4
-- Loss: DiceLoss
-
-
 ### Preprocessing
 After [downloading this dataset](https://warwick.ac.uk/fac/cross_fac/tia/data/hovernet/consep_dataset.zip),
 python script `data_process.py` from `scripts` folder can be used to preprocess and generate the final dataset for training.
@@ -91,33 +79,45 @@ Example dataset.json
 }
 ```
 
+## Training configuration
+The training was performed with the following:
+
+- GPU: at least 12GB of GPU memory
+- Actual Model Input: 5 x 128 x 128
+- AMP: True
+- Optimizer: Adam
+- Learning Rate: 1e-4
+- Loss: DiceLoss
 
-## Input and output formats
-### Input: 5 channels
+
+## Input
+5 channels
 - 3 RGB channels
 - +ve signal channel (this nuclei)
 - -ve signal channel (other nuclei)
 
-### Output: 2 channels
+## Output
+2 channels
  - 0 = Background
  - 1 = Nuclei
 
 ![](https://developer.download.nvidia.com/assets/Clara/Images/monai_pathology_nuclick_annotation_train_in_out.jpeg)
 
-## Scores
+
+## Performance
 This model achieves the following Dice score on the validation data provided as part of the dataset:
 
 - Train Dice score = 0.89
 - Validation Dice score = 0.85
 
 
-## Training Performance
+#### Training Loss and Dice
 A graph showing the training Loss and Dice over 50 epochs.
 
 ![](https://developer.download.nvidia.com/assets/Clara/Images/monai_pathology_nuclick_annotation_train_loss.jpeg) <br>
 ![](https://developer.download.nvidia.com/assets/Clara/Images/monai_pathology_nuclick_annotation_train_dice.jpeg) <br>
 
-## Validation Performance
+#### Validation Dice
 A graph showing the validation mean Dice over 50 epochs.
 
 ![](https://developer.download.nvidia.com/assets/Clara/Images/monai_pathology_nuclick_annotation_val_dice.jpeg) <br>
@@ -140,8 +140,7 @@ python -m monai.bundle run --config_file configs/train.json
 torchrun --standalone --nnodes=1 --nproc_per_node=2 -m monai.bundle run --config_file "['configs/train.json','configs/multi_gpu_train.json']"
 ```
 
-Please note that the distributed training related options depend on the actual running environment, thus you may need to remove `--standalone`, modify `--nnodes` or do some other necessary changes according to the machine you used.
-Please refer to [pytorch's official tutorial](https://pytorch.org/tutorials/intermediate/ddp_tutorial.html) for more details.
+Please note that the distributed training-related options depend on the actual running environment; thus, users may need to remove `--standalone`, modify `--nnodes`, or do some other necessary changes according to the machine used. For more details, please refer to [pytorch's official tutorial](https://pytorch.org/tutorials/intermediate/ddp_tutorial.html).
 
 #### Override the `train` config to execute evaluation with the trained model:
 
@@ -161,9 +160,6 @@ torchrun --standalone --nnodes=1 --nproc_per_node=2 -m monai.bundle run --config
 python -m monai.bundle run --config_file configs/inference.json
 ```
 
-# Disclaimer
-This is an example, not to be used for diagnostic purposes.
-
 # References
 [1] Koohbanani, Navid Alemi, et al. "NuClick: a deep learning framework for interactive segmentation of microscopic images." Medical Image Analysis 65 (2020): 101771. https://arxiv.org/abs/2005.14511.
 

configs/metadata.json

@@ -1,7 +1,8 @@
 {
     "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20220324.json",
-    "version": "0.0.8",
+    "version": "0.0.9",
     "changelog": {
+        "0.0.9": "Update README Formatting",
         "0.0.8": "enable deterministic training",
         "0.0.7": "Update with figure links",
         "0.0.6": "adapt to BundleWorkflow interface",

docs/README.md

@@ -1,11 +1,10 @@
-# Description
+# Model Overview
 A pre-trained model for segmenting nuclei cells with user clicks/interactions.
 
 ![nuclick](https://github.com/mostafajahanifar/nuclick_torch/raw/master/docs/11.gif)
 ![nuclick](https://github.com/mostafajahanifar/nuclick_torch/raw/master/docs/33.gif)
 ![nuclick](https://github.com/mostafajahanifar/nuclick_torch/raw/master/docs/22.gif)
 
-# Model Overview
 This model is trained using [BasicUNet](https://docs.monai.io/en/latest/networks.html#basicunet) over [ConSeP](https://warwick.ac.uk/fac/cross_fac/tia/data/hovernet) dataset.
 
 ## Data
@@ -16,17 +15,6 @@ unzip -q consep_dataset.zip
 ```
 ![](https://developer.download.nvidia.com/assets/Clara/Images/monai_pathology_nuclick_annotation_dataset.jpeg)<br/>
 
-## Training configuration
-The training was performed with the following:
-
-- GPU: at least 12GB of GPU memory
-- Actual Model Input: 5 x 128 x 128
-- AMP: True
-- Optimizer: Adam
-- Learning Rate: 1e-4
-- Loss: DiceLoss
-
-
 ### Preprocessing
 After [downloading this dataset](https://warwick.ac.uk/fac/cross_fac/tia/data/hovernet/consep_dataset.zip),
 python script `data_process.py` from `scripts` folder can be used to preprocess and generate the final dataset for training.
@@ -84,33 +72,45 @@ Example dataset.json
 }
 ```
 
+## Training configuration
+The training was performed with the following:
+
+- GPU: at least 12GB of GPU memory
+- Actual Model Input: 5 x 128 x 128
+- AMP: True
+- Optimizer: Adam
+- Learning Rate: 1e-4
+- Loss: DiceLoss
 
-## Input and output formats
-### Input: 5 channels
+
+## Input
+5 channels
 - 3 RGB channels
 - +ve signal channel (this nuclei)
 - -ve signal channel (other nuclei)
 
-### Output: 2 channels
+## Output
+2 channels
  - 0 = Background
  - 1 = Nuclei
 
 ![](https://developer.download.nvidia.com/assets/Clara/Images/monai_pathology_nuclick_annotation_train_in_out.jpeg)
 
-## Scores
+
+## Performance
 This model achieves the following Dice score on the validation data provided as part of the dataset:
 
 - Train Dice score = 0.89
 - Validation Dice score = 0.85
 
 
-## Training Performance
+#### Training Loss and Dice
 A graph showing the training Loss and Dice over 50 epochs.
 
 ![](https://developer.download.nvidia.com/assets/Clara/Images/monai_pathology_nuclick_annotation_train_loss.jpeg) <br>
 ![](https://developer.download.nvidia.com/assets/Clara/Images/monai_pathology_nuclick_annotation_train_dice.jpeg) <br>
 
-## Validation Performance
+#### Validation Dice
 A graph showing the validation mean Dice over 50 epochs.
 
 ![](https://developer.download.nvidia.com/assets/Clara/Images/monai_pathology_nuclick_annotation_val_dice.jpeg) <br>
@@ -133,8 +133,7 @@ python -m monai.bundle run --config_file configs/train.json
 torchrun --standalone --nnodes=1 --nproc_per_node=2 -m monai.bundle run --config_file "['configs/train.json','configs/multi_gpu_train.json']"
 ```
 
-Please note that the distributed training related options depend on the actual running environment, thus you may need to remove `--standalone`, modify `--nnodes` or do some other necessary changes according to the machine you used.
-Please refer to [pytorch's official tutorial](https://pytorch.org/tutorials/intermediate/ddp_tutorial.html) for more details.
+Please note that the distributed training-related options depend on the actual running environment; thus, users may need to remove `--standalone`, modify `--nnodes`, or do some other necessary changes according to the machine used. For more details, please refer to [pytorch's official tutorial](https://pytorch.org/tutorials/intermediate/ddp_tutorial.html).
 
 #### Override the `train` config to execute evaluation with the trained model:
 
@@ -154,9 +153,6 @@ torchrun --standalone --nnodes=1 --nproc_per_node=2 -m monai.bundle run --config
 python -m monai.bundle run --config_file configs/inference.json
 ```
 
-# Disclaimer
-This is an example, not to be used for diagnostic purposes.
-
 # References
 [1] Koohbanani, Navid Alemi, et al. "NuClick: a deep learning framework for interactive segmentation of microscopic images." Medical Image Analysis 65 (2020): 101771. https://arxiv.org/abs/2005.14511.