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Imageomics HDR Anomaly Challenge Sample

This repository contains sample training code and submissions for the 2024 HDR Anomaly Challenge: Hybrid Butterfly Detection. It is designed to give participants a reference for both working on the challenge, and also the expected publication of their submissions following the challenge (i.e., how to open-source your submission).

Repository Structure

For your repository, you will want to complete the structure information below and add other files (e.g., training code):

submission
  <model weights>
  model.py
  requirements.txt

We also recommend that you include a CITATION.cff for your work.

Note: If you have requirements not included in the whitelist, please check the issues on the challenge GitHub to see if someone else has requested it before making your own issue.

Structure of this Repository

HDR-anomaly-challenge-sample
│
├── BioCLIP_code_submission
│   ├── clf.pkl
│   ├── metadata
│   ├── model.py
│   └── requirements.txt
│
├── BioCLIP_train
│   ├── classifier.py
│   ├── data_utils.py
│   ├── dataset.py
│   ├── evaluation.py
│   ├── model_utils.py
│   └── training.py
│
├── DINO_SGD_code_submission
│   ├── clf.pkl
│   ├── metadata
│   ├── model.py
│   └── requirements.txt
│
└── DINO_train
    ├── classifier.py
    ├── data_utils.py
    ├── dataset.py
    ├── evaluation.py
    ├── model_utils.py
    └── training.py

This repository also includes butterfly_sample_notebook.ipynb which loads the metadata for the images and displays a histogram of the hybrid/non-hybrid distribution by subspecies. It then downloads 15% of the data and runs through a simplified sample submission training with that subset (the sample image amount can be adjusted to work within network constraints). To run this notebook, first clone this repository and create a fresh conda environment, then install the requirements file:

conda create -n butterfly-sample -c conda-forge pip -y
conda activate butterfly-sample
pip install -r requirements.txt
jupyter lab

References

List any sources used in developing your model (e.g., baseline model that was fine-tuned).

training data references