Duplicate from 168sir/drill-core-image-dataset

Co-authored-by: 123 <168sir@users.noreply.huggingface.co>

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DCID.zip

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README.md

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+---
+license: cc-by-nc-4.0
+task_categories:
+- image-classification
+language:
+- en
+tags:
+- lithology
+- geology
+- rock
+- drill core
+- core images
+- lithology identification
+- lithology classification
+- DCID
+pretty_name: Drill Core Image Dataset (DCID)
+size_categories:
+- 10K<n<100K
+---
+
+# Dataset Card for Drill Core Image Dataset (DCID)
+
+## Dataset Details
+
+### Dataset Description
+
+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:
+
+- **DCID-7**: 7 lithology categories with 5,000 images per class.
+- **DCID-35**: 35 lithology categories with 1,000 images per class.
+
+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.
+
+- **Curated by:** Jia-Yu Li, Ji-Zhou Tang, et al.
+- **Shared by:** Jia-Yu Li (lijiayu1120@tongji.edu.cn), Ji-Zhou Tang (jeremytang@tongji.edu.cn)
+- **License:** CC BY-NC 4.0 (Creative Commons Attribution-NonCommercial 4.0)
+
+---
+
+### Visual Overview
+
+#### DCID Naming Convention
+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`)  
+
+![DCID Naming Convention](./DCID-R-C-L-I.jpg)
+
+---
+
+#### DCID-7 Dataset
+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.
+
+![DCID-7 Dataset Overview](./DCID-7.jpg)
+
+---
+
+#### DCID-35 Dataset
+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.
+
+![DCID-35 Dataset Overview](./DCID-35.jpg)
+
+---
+
+### Dataset Sources
+
+- **GitHub Repository:** [https://github.com/JiayuLi1120/drill-core-image-dataset](https://github.com/JiayuLi1120/drill-core-image-dataset)  
+- **Hugging Face Dataset:** [https://huggingface.co/datasets/168sir/drill-core-image-dataset](https://huggingface.co/datasets/168sir/drill-core-image-dataset)  
+- **Paper:** [https://doi.org/10.1016/j.petsci.2025.04.013](https://doi.org/10.1016/j.petsci.2025.04.013)
+
+---
+
+## Usage
+
+### Step 1: Download and extract
+Download the `DCID.zip` archive from [Hugging Face](https://huggingface.co/datasets/168sir/drill-core-image-dataset) and extract it:
+
+```bash
+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/`
+
+---
+
+### Step 2: Build custom dataset versions
+
+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:
+
+```bash
+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/
+```
+
+---
+
+### Script Parameters
+
+* **`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`: none
+  * `T`: train set only
+  * `E`: test set only
+  * `A`: all (train + test)
+
+---
+
+## Citation
+
+If you use this dataset in your work, please cite:
+
+```bibtex
+@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}
+}