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+# Meta-Learning: Finetuning of Pretrained Network with Attention
+
+This repo contains the code submitted to Medival challenge on GI tract's poly segmentaion. The pre-trained
+weights of Unet model trained on brain MRI images were fine tuned ; which is a open source available at  https://pytorch.org/hub/mateuszbuda_brain-segmentation-pytorch_unet/,thanks to "mateuszbuda" .Attention mechanism was incorporated while finetuning weights transfered from Unet trained on MRI dataset.
+
+
+
+## Docker
+
+```
+docker build -f Dockerfile -t polypseg .
+```
+
+
+
+## Data
+Dataset were made publicly available on  [/multimediaeval.github.io/editions/2020/tasks/medico/](https://multimediaeval.github.io/editions/2020/tasks/medico/). It consists of  images of  polyps in GI tract and its masks. Data folders should be  arranged as in the tree structure below to load data in different mode.
+![Data Folder Structure](./readme_fig/folder_tree.png)
+
+
+## Model
+
+The pretrained model has been finetuned  with the polyp  dataset.Unet model is guided with attention mechanism.The dataset consists of 1000 images which was then splitted into 80:20 ratio of train and validation set. The test data consisted of 160 images .
+
+
+## Training steps
+
+1. Download the dataset .
+2. Run docker container.
+3. Run `main.py` script.Root path is set to `./medico2020`. For  help run: `python3 main.py --help`.
+
+
+
+## Test prediction
+
+1. Download the test data
+2. Run docker container.
+3. Run `test.py`  which will load the provided  trained weights from `./weights/unet.pt` file  
+. Test predicted images will be saved in "./predictions". For  help run: `python3 test.py --help`.
+
+
+
+
+# Results
+
+For viewing the graph,images and events; "./log" can be loaded and viewed in tensorboard.
+
+
+Mean DSC on validation=96.35 %  
+
+Mean IOU on validation=87.91%
+
+## Plot
+
+![Validation DSC VS Iterations ](./readme_fig/val_dsc.png)
+
+
+## Sample predictions(Red) over groundtruth(Green)
+
+![Green- GT, Red- Pred](./readme_fig/img.gif)