Dataset
The Drill Core Image Dataset (DCID) is a large-scale benchmark designed for lithology classification based on RGB core images. It provides two primary versions:
All original images are 512×512 pixels in resolution. Each category is split into training and testing subsets in an 8:2 ratio. Additional variants are generated by resizing to smaller resolutions (32, 64, 128, 256) and applying real-world data augmentation (RWDA) to simulate image imperfections.
The dataset naming follows the DCID-R-C-L-I format:
R: resolution (32, 64, 128, 256, 512)C: number of categories (7 or 35)L: RWDA level (0.0 – 0.4)I: injection scope (N, T, E, A)
The DCID-7 dataset contains 35,000 images (5,000 per category).
Each class has 4,000 training and 1,000 testing images (8:2 ratio).
This version is suitable for evaluating model upper-bound performance.
The DCID-35 dataset contains 35,000 images (1,000 per category).
Each class has 800 training and 200 testing images.
This fine-grained version is designed to assess model generalization under complex conditions.
Download the DCID.zip archive from Hugging Face and extract it:
unzip DCID.zip -d ./DCID
This will give you the following folders:
DCID-512-7/ and noise-512-7/DCID-512-35/ and noise-512-35/We provide a script build_dcid_dataset.py to generate different dataset variants.
Example: Create a 32×32 resolution, 7 classes, 40% RWDA (train set only) dataset:
python build_dcid_dataset.py \
--root ./DCID \
--R 32 \
--C 7 \
--L 0.4 \
--I T \
--out_dir ./output
This generates a new dataset at:
./output/DCID-32-7-0.4-T/
R: target resolution (32, 64, 128, 256)
C: number of categories (7 or 35)
L: RWDA level (0.0–0.4)
I: injection scope:
N: noneT: train set onlyE: test set onlyA: all (train + test)If you use this dataset in your work, please cite:
@article{Li2025DCID,
title = {A large-scale, high-quality dataset for lithology identification: Construction and applications},
author = {Jia-Yu Li and Ji-Zhou Tang and Xian-Zheng Zhao and Bo Fan and Wen-Ya Jiang and Shun-Yao Song and Jian-Bing Li and Kai-Da Chen and Zheng-Guang Zhao},
journal = {Petroleum Science},
year = {2025},
issn = {1995-8226},
doi = {10.1016/j.petsci.2025.04.013}
}