Update README.md

README.md

@@ -3,30 +3,88 @@ tags:
 - fastai
 ---
 
-# Amazing!
-
-🥳 Congratulations on hosting your fastai model on the Hugging Face Hub!
+# Model card
 
-# Some next steps
-1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
+## Model description
+Fastai `unet` created with `unet_learner` using `resnet34`
 
-2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)).
+## Intended uses & limitations
+This is only used for demonstration of fine tuning capabilities with fastai. It may be useful for further research. This model should **not** be used for gastrointestinal polyp diagnosis.
 
-3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)!
+## Training and evaluation data
+The model was trained on [Kvasir SEG dataset](https://datasets.simula.no/kvasir-seg/). Kvasir SEG is an open-access dataset of gastrointestinal polyp images and corresponding segmentation masks, manually annotated and verified by an experienced gastroenterologist.  
+20% of the data set were used as validation set and 80% as training set.  
+  
+### Model training details:
 
-Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card.
+#### Data pre-processing
+Masks were converted to 1 bit images: 0 for background and 1 for mask using
+```python
+Path('/notebooks/Kvasir-SEG/masks1b-binary').mkdir(parents=True, exist_ok=True)
+for img_path in tqdm(get_image_files(path/'masks')):
+    img = Image.open(img_path)
+    thresh = 127
+    fn = lambda x : 1 if x > thresh else 0
+    img1b = img.convert('L').point(fn)
+    img1b.save(path/'masks1b-binary'/f'{img_path.stem}.png')
+```
+#### Data loaders
+`SegmentationDataloaders` were used to create fastai data loaders
+```python
+def label_func(fn): return path/'masks1b-binary'/f'{fn.stem}.png'
+dls = SegmentationDataLoaders.from_label_func(
+    path, bs=24, fnames = get_image_files(path/'images'),
+    label_func = label_func,
+    codes = list(range(2)),
+    item_tfms=Resize(320),
+    batch_tfms=aug_transforms(size=224, flip_vert=True)
+)
+```
+An sample of training images:
+![show_batch](path/to/image)
 
+#### Learner
+Create learner with Dice and JaccardCoeff metrics
+```python
+learn = unet_learner(dls, resnet34, metrics=[Dice, JaccardCoeff]).to_fp16()
+```
 
----
+#### Learning rate
+Learning rate finder
+![lr_find](path/to/image)
 
+#### Fine tuning
+Fine tuning for 12 epochs  
+`learn.fine_tune(12, 1e-4)`
+```
+epoch 	train_loss 	valid_loss 	dice 	jaccard_coeff 	time
+0 	0.582160 	0.433768 	0.593044 	0.421508 	00:38
+epoch 	train_loss 	valid_loss 	dice 	jaccard_coeff 	time
+0 	0.307588 	0.261374 	0.712569 	0.553481 	00:38
+1 	0.261775 	0.232007 	0.714458 	0.555764 	00:38
+2 	0.246054 	0.227708 	0.781048 	0.640754 	00:38
+3 	0.224612 	0.185920 	0.796701 	0.662097 	00:39
+4 	0.208768 	0.179064 	0.821945 	0.697714 	00:39
+5 	0.192531 	0.171336 	0.816464 	0.689851 	00:39
+6 	0.177166 	0.167357 	0.820771 	0.696023 	00:39
+7 	0.168222 	0.158182 	0.838388 	0.721745 	00:39
+8 	0.155157 	0.161950 	0.829525 	0.708709 	00:39
+9 	0.148792 	0.164533 	0.828383 	0.707043 	00:38
+10 	0.143541 	0.158669 	0.833519 	0.714559 	00:39
+11 	0.140083 	0.159437 	0.832745 	0.713422 	00:38
+```
+![loss_graph](path/to/image)
 
-# Model card
+#### Results
+Visualization of results
+![show_results](path/to/image)
 
-## Model description
-More information needed
+Top losses
+![top_losses](path/to/image)
 
-## Intended uses & limitations
-More information needed
+#### Libraries used:
+`huggingface_hub.__version__`  
+`'0.8.1'`  
 
-## Training and evaluation data
-More information needed
+`fastai.__version__`  
+`'2.6.3'`