Kaggle Vesuvius Ink Detection Challenge

Code for 8th place solution in Kaggle Vesuvius Ink Detection Challenge.

Please refer to this Kaggle forum post for a description of the solution

A Word on the Directory Structure

Since we only formed team in the last week of the competition, our work are mostly independently and are located in two separated folders respectively (i.e. felix_work and yoyobar_work). That said, we have made the pipeline for inference and models reproduction as simple as possible.

Hardware Use

Felix:

CPU: Intel i7-13700KF GPU: 3090 x 1 RAM: 64GB

Yoyobar:

CPU: 15 vCPU Intel(R) Xeon(R) Platinum 8375C CPU @ 2.90GHz GPU: 4090 x 1
RAM: 80GB

Environment

All requirements should be detailed in requirements.txt. Using Anaconda is strongly recommended.

conda create -n vesuvius python=3.8
conda activate vesuvius
pip install -r requirements.txt

Prepare dataset

Download competition dataset

$ cd felix_work
$ kaggle competitions download -c vesuvius-challenge-ink-detection
$ unzip vesuvius-challenge-ink-detection.zip

After unzipping, you will see two folders train and test that contains the train and test data for the competition are created

Data Transformation (e.g. create CV folds and resize images)

Navigate to the project root directory where the Makefile is located. Issue the following make command

$ make prepare_data

Replicate Submission

This is to replicate our final submission notebook (public LB: 0.792859 / private LB: 0.55514).

We have created a kaggle dataset titled vesuvius-8th-place-solution-models with all the model checkpoints required to run the inference script.

Download the kaggle dataset that contains all the model checkpoints

$ kaggle datasets download -d renman/vesuvius-8th-place-solution-models

Unzip vesuvius-8th-place-solution-models and you will see a final_models directory that contains all the models checkpoints

Run inference code

$ make run_inference

The submission.csv file will be generated in the same directory

Replicate Training

This is to run the training pipeline to regenerate all the models we have used for inference

$ make reproduce_models

Notice that all reproduced models are automatically being copied to the final_models directory to be consumed by the inference script. No manual model copy and pasting is required.