• Adarsh Ghimire
• Basit Alawode
• Divya Velayudhan
• Shibani Hamza

We split the given training data into new training and validation splits using the ratio 80:20. We provide the link to these splits below:

• Train and Validation Split link

The respective folders in the link also include the xml generated from the given dataset mask of each image.

Below is a plot showing the mean average precision (mAP) of the model on the validation set during training.

NOTE: The below interpretability images are independent of the above predictions.

NOTE: The below interpretability images are independent of the above predictions.

NOTE: The below interpretability images are independent of the above predictions.

1. Trained Model Weights:

Our trained model weights can be downloaded from here

2. Test Dataset Results Excel Sheets:

The excel sheets for the test sets can be found inside the results/excel folder.

1. Download this repository and open in a python editor (preferably VS code).

2. Create the python environment

conda create -y --name bleedgen python==3.7.16
conda activate bleedgen  

2. Install pytorch and torchvision

pip install torch==1.10.0+cu111 torchvision==0.11.0+cu111 torchaudio==0.10.0 -f https://download.pytorch.org/whl/torch_stable.html

3. Install other packages

pip install -r requirements.txt

4. Download the train and validation data using this link

5. Unzip and put the train and validation data into their respective folders (train/Train) and (train/Val).

6. Also, put the test data into their respective folders (test/Test_Dataset_1) and (test/Test_Dataset_2).

To train, open and run train.py in the created environment.

python train.py

NOTE:

1. Training results will be available inside the runs folder.

To test:

1. Download our already trained model weights here and put inside the trained_weights folder.

2. Open and run test.py in the created environment.

python test.py

NOTE:

1. Comment any two of Lines 17, 18, and 19 in test.py to select one of validation set, test set 1, and test set 2 for testing.

2. Testing results will be available inside the results folder.

• This work is based on this vision transformer library.