Update README.md

README.md

@@ -1,4 +1,19 @@
 ---
+license: cc-by-4.0
+task_categories:
+- image-to-image
+language:
+- en
+pretty_name: CellOPC
+size_categories:
+- 100K<n<1M
+tags:
+- optical-proximity-correction
+- inverse-lithography
+- mask-optimization
+- vlsi
+- eda
+- lithography
 dataset_info:
   features:
   - name: image
@@ -33,3 +48,57 @@ configs:
   - split: test
     path: data/test-*
 ---
+
+# CellOPC
+
+CellOPC is a large-scale benchmark dataset for cell- and context-aware mask optimization. It is designed to support deep learning research for optical proximity correction (OPC) and inverse lithography technique (ILT) mask generation.
+
+## Dataset Description
+
+CellOPC is constructed from real integrated circuit layouts at the 45 nm technology node. Each sample is clipped around a standard-cell placement instance to preserve cell-level hierarchy and surrounding layout context. The dataset provides paired input layout/target images and optimized mask images for learning cell-wise mask generation under different context sizes.
+
+The dataset contains both model-based OPC and ILT mask types. It is intended to evaluate how standard-cell identity, neighboring geometries, and input context size affect mask prediction and lithography-aware printability.
+
+## Dataset Structure
+
+Each sample contains the following fields:
+
+- `conditioning_image`: input target/layout image.
+- `image`: ground-truth optimized mask image.
+- `mask_type`: mask generation type, such as `opc` or `ilt`.
+- `context`: context size used when clipping the input layout.
+- `source_dataset`: source subset name, such as `cellopc_opc_16`.
+
+The dataset contains three splits:
+
+| Split | Number of Examples |
+|---|---:|
+| train | 451,912 |
+| validation | 112,974 |
+| test | 80 |
+
+## Intended Use
+
+CellOPC is intended for:
+
+- training image-to-image mask generation models;
+- benchmarking deep learning methods for OPC and ILT;
+- studying the impact of context size on mask prediction;
+- evaluating cell-aware and context-aware mask optimization.
+
+## Loading the Dataset
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("ChristyHu/CellOPC")
+
+train_set = dataset["train"]
+val_set = dataset["validation"]
+test_set = dataset["test"]
+
+sample = train_set[0]
+layout = sample["conditioning_image"]
+mask = sample["image"]
+mask_type = sample["mask_type"]
+context = sample["context"]