- 💪 Challenge Page: https://www.aicrowd.com/challenges/seismic-facies-identification-challenge
- 🗣️ Discussion Forum: https://www.aicrowd.com/challenges/seismic-facies-identification-challenge/discussion
- 🏆 Leaderboard: https://www.aicrowd.com/challenges/seismic-facies-identification-challenge/leaderboards
git clone https://github.com/AIcrowd/seismic-facies-identification-starter-kit
cd seismic-facies-identification-starter-kit
pip install -r requirements.txt
Download all the files from the AIcrowd Resources page,
and put them in the data/ folder. This should give you a folder structure similar to :
data/
├── data_test_1.npz
├── data_test_2.npz
├── data_train.npz
└── labels_train.npz
NOTE: If you have not accepted the challenge rules (by clicking on the Participate button), you will be asked to agree to the Rules of the competition at this point.
NOTE: For Round-2, you will have to submit predictions using data_test_2.npz and for Round-1 you should submit predictions using data_test_1.npz.
python random_predict.py
This should generate a prediction.npz file, which you can upload by clicking on Create Submission on the challenge page.
NOTE The submitted npz is expected to contain a prediction key storing a ndarray of the same shape as that of the data_test.npz. Please refer here for an example.
Please refer to compute_score.py for the code that is used to compute the scores on the leaderboard. If there are any optimizations you would want to suggest, or any bugs you find, please consider sending across a pull request.
- Just a simple Video-Notebook - Starter Pack
- Introduction and General Approach Final Pack!
- EDA in details, baseline and advanced models
- Seismic Facies Identification Starter
- Detectron2 & COCO Dataset 🔥 • Web Application & Visualizations • End-to-End Baseline & Tensorflow
- PyTorch starter 0.857 F1-Score on public LB
- Need extra features? Different input approach? Try Seismic Attributes!
- End to End solution that gives above 80% Accuracy
S.P. Mohanty mohanty@aicrowd.com