DLCV Final Project ( Skull Fractracture Detection )

How to run your code?

1. bash case_level_train.sh <training_data_directory> <json_file> <test_data_directory> out.csv

example: bash case_level_train.sh skull/train skull/records_train.json skull/test out.csv

2. cd FasterRCNN
3. bash coords_train.sh <train_data_directory> <test_data_directory> <json_file> <output_csv_file>

example bash coords_train.sh ../skull/train ../skull/test ../skull/records_train.json result.csv

Usage

To start working on this final project, you should clone this repository into your local machine by using the following command:

git clone https://github.com/ntudlcv/DLCV-Fall-2021-Final-1-<team name>.git

Note that you should replace <team_name> with your own team name.

For more details, please click this link to view the slides of Final Project - Skull Fracture Detection. Note that video and introduction pdf files for final project can be accessed in your NTU COOL.

Dataset

In the starter code of this repository, we have provided a shell script for downloading and extracting the dataset for this assignment. For Linux users, simply use the following command.

bash ./get_dataset.sh

The shell script will automatically download the dataset and store the data in a folder called skull. Note that this command by default only works on Linux. If you are using other operating systems, you should download the dataset from this link and unzip the compressed file manually.

⚠️ IMPORTANT NOTE ⚠️

1. Please do not disclose the dataset! Also, do not upload your get_dataset.sh to your (public) Github.
2. You should keep a copy of the dataset only in your local machine. DO NOT upload the dataset to this remote repository. If you extract the dataset manually, be sure to put them in a folder called skull under the root directory of your local repository so that it will be included in the default .gitignore file.

🆕 NOTE
For the sake of conformity, please use the python3 command to call your .py files in all your shell scripts. Do not use python or other aliases, otherwise your commands may fail in our autograding scripts.

Evaluation Code

In the starter code of this repository, we have provided a python script for evaluating the results for this project. For Linux users, use the following command to evaluate the results.

python3 for_students_eval.py --pred_file <path to your prediction csv file> --gt_file <path to the ground-truth csv file>

Submission Rules

Deadline

110/1/18 (Tue.) 23:59 (GMT+8)

Q&A

If you have any problems related to Final Project, you may

• Use TA hours
• Contact TAs by e-mail ([email protected])
• Post your question under Final Project FAQ section in NTU Cool Discussion