--- a/README.md
+++ b/README.md
@@ -1,60 +1,50 @@
-# 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)
+# 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.
+
+## 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%
+