Upload DocLayNet 6-class filtered dataset

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README.md

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+---
+license: cdla-permissive-2.0
+task_categories:
+- object-detection
+- document-layout-analysis
+tags:
+- document-ai
+- layout-analysis
+- object-detection
+- doclaynet
+- filtered
+size_categories:
+- 10K<n<100K
+---
+
+# DocLayNet 6-Class Filtered Dataset
+
+## Dataset Description
+
+This is a filtered version of the [DocLayNet dataset](https://huggingface.co/datasets/docling-project/DocLayNet) containing only 6 most relevant layout element classes for document layout analysis tasks.
+
+### Original Dataset
+
+DocLayNet is a human-annotated document layout segmentation dataset containing 80,863 pages from diverse sources with 11 distinct layout categories.
+
+**Citation:**
+```
+@article{doclaynet2022,
+  title = {DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis},
+  author = {Pfitzmann, Birgit and Auer, Christoph and Dolfi, Michele and Nassar, Ahmed S and Staar, Peter W J},
+  year = {2022},
+  doi = {10.1145/3534678.3539043},
+}
+```
+
+### Filtering Methodology
+
+**Classes Retained (6):**
+1. **Text** - Body text paragraphs
+2. **List-item** - List elements (bulleted, numbered)
+3. **Section-header** - Section and subsection titles
+4. **Picture** - Images, figures, diagrams
+5. **Table** - Tabular data structures
+6. **Caption** - Image and table captions
+
+**Classes Removed (5):**
+- Footnote
+- Formula
+- Page-footer
+- Page-header
+- Title
+
+**Rationale:** Focus on the most common and semantically important layout elements for general document understanding tasks. The 6 retained classes represent 85.1% of all annotations in the original dataset.
+
+## Dataset Statistics
+
+### Split Distribution
+
+| Split | Images | Annotations | Classes |
+|-------|--------|-------------|---------|
+| Train | 68,673 | 800,614 | 6 |
+| Validation | 6,446 | 85,057 | 6 |
+| Test | 4,952 | 56,483 | 6 |
+| **Total** | **80,071** | **942,154** | **6** |
+
+### Class Distribution (Training Set)
+
+Based on 800,614 annotations:
+
+| Class ID | Class Name | Count | Percentage |
+|----------|------------|-------|------------|
+| 0 | Caption | 19,218 | 2.4% |
+| 1 | List-item | 161,818 | 20.2% |
+| 2 | Picture | 39,667 | 5.0% |
+| 3 | Section-header | 118,590 | 14.8% |
+| 4 | Table | 30,070 | 3.8% |
+| 5 | Text | 431,251 | 53.9% |
+
+### Retention from Original Dataset
+
+- **Images retained:** 99.0%
+- **Annotations retained:** 85.1%
+
+## Dataset Structure
+
+### Format
+
+Annotations are provided in **COCO JSON format**:
+
+```
+DocLayNet_6class/
+├── coco/
+│   ├── train.json      # Training annotations
+│   ├── val.json        # Validation annotations
+│   └── test.json       # Test annotations
+└── README.md           # This file
+```
+
+Images are **NOT included** - use the original DocLayNet image files from:
+- HuggingFace: `docling-project/DocLayNet`
+- Official source: https://github.com/DS4SD/DocLayNet
+
+### Loading the Dataset
+
+#### Using HuggingFace Datasets
+
+```python
+from datasets import load_dataset
+
+# Load the filtered annotations
+dataset = load_dataset("YOUR_USERNAME/doclaynet-6class")
+
+# Access splits
+train_data = dataset["train"]
+val_data = dataset["validation"]
+test_data = dataset["test"]
+```
+
+#### Manual Loading
+
+```python
+import json
+from pathlib import Path
+
+# Load COCO annotations
+with open("coco/train.json") as f:
+    train_coco = json.load(f)
+
+# Categories
+categories = train_coco["categories"]  # 6 classes with IDs 0-5
+
+# Images
+images = train_coco["images"]  # Image metadata
+
+# Annotations
+annotations = train_coco["annotations"]  # Bounding boxes
+```
+
+### Annotation Format
+
+Each annotation follows the COCO format:
+
+```json
+{
+  "id": 12345,
+  "image_id": 123,
+  "category_id": 5,  // 0-5 (remapped from original 11 classes)
+  "bbox": [x_min, y_min, width, height],  // In pixels
+  "area": 12345.67,
+  "iscrowd": 0
+}
+```
+
+### Category Mapping
+
+Original DocLayNet → 6-Class Filtered:
+
+| Original ID | Original Name | Filtered ID | Filtered Name | Status |
+|-------------|---------------|-------------|---------------|--------|
+| 0 | Caption | 0 | Caption | ✅ Kept |
+| 1 | Footnote | - | - | ❌ Removed |
+| 2 | Formula | - | - | ❌ Removed |
+| 3 | List-item | 1 | List-item | ✅ Kept |
+| 4 | Page-footer | - | - | ❌ Removed |
+| 5 | Page-header | - | - | ❌ Removed |
+| 6 | Picture | 2 | Picture | ✅ Kept |
+| 7 | Section-header | 3 | Section-header | ✅ Kept |
+| 8 | Table | 4 | Table | ✅ Kept |
+| 9 | Text | 5 | Text | ✅ Kept |
+| 10 | Title | - | - | ❌ Removed |
+
+## Use Cases
+
+This filtered dataset is ideal for:
+
+- **Document layout analysis** with focus on content structure
+- **Information extraction** from documents (text, tables, figures)
+- **Object detection** model training for document AI
+- **Multi-scale document understanding** tasks
+- **Transfer learning** from general object detection to document analysis
+
+## Evaluation Benchmarks
+
+### Zero-Shot Performance (COCO Pretrained Models)
+
+Models pretrained on COCO and evaluated without fine-tuning on DocLayNet 6-class:
+
+| Model | mAP@0.5 | mAP@0.5:0.95 | Inference (ms) | Parameters |
+|-------|---------|--------------|----------------|------------|
+| YOLOv11n | TBD | TBD | TBD | 2.6M |
+| YOLOv11m | TBD | TBD | TBD | 20M |
+| RT-DETR-L | TBD | TBD | TBD | 32M |
+
+*Evaluation in progress on test set (4,952 images at 1024×1024 resolution)*
+
+## Limitations
+
+1. **Images not included**: You must obtain images from the original DocLayNet dataset
+2. **Class imbalance**: Text class dominates (53.9% of annotations)
+3. **Domain specific**: Focused on document layout, may not generalize to other domains
+4. **Annotation quality**: Inherits any annotation errors from original DocLayNet
+
+## Ethical Considerations
+
+- Dataset maintains the original DocLayNet license (CDLA-Permissive-2.0)
+- No personal or sensitive information in annotations
+- Source documents from diverse domains (financial, scientific, patents, manuals)
+- Should not be used to discriminate based on document type or origin
+
+## Citation
+
+If you use this filtered dataset, please cite both:
+
+1. **Original DocLayNet paper:**
+```bibtex
+@article{doclaynet2022,
+  title = {DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis},
+  author = {Pfitzmann, Birgit and Auer, Christoph and Dolfi, Michele and Nassar, Ahmed S and Staar, Peter W J},
+  year = {2022},
+  doi = {10.1145/3534678.3539043},
+}
+```
+
+2. **This filtered version:**
+```bibtex
+@misc{doclaynet6class2024,
+  title = {DocLayNet 6-Class: Filtered Document Layout Analysis Dataset},
+  author = {[Your Name]},
+  year = {2024},
+  howpublished = {\url{https://huggingface.co/datasets/YOUR_USERNAME/doclaynet-6class}},
+  note = {Filtered subset of DocLayNet containing 6 primary layout element classes}
+}
+```
+
+## License
+
+This filtered dataset maintains the original license:
+
+**CDLA-Permissive-2.0** (Community Data License Agreement – Permissive – Version 2.0)
+
+See: https://cdla.dev/permissive-2-0/
+
+## Acknowledgments
+
+- Original DocLayNet dataset: IBM Research
+- Filtering and curation: [Your Name/Organization]
+- Built using the layout-for-tools evaluation framework
+
+## Contact
+
+For questions or issues with this filtered dataset, please open an issue on the repository.
+
+For questions about the original DocLayNet dataset, see: https://github.com/DS4SD/DocLayNet

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coco/train.json

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dataset_infos.json

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+{
+  "default": {
+    "description": "DocLayNet 6-class filtered dataset for document layout analysis",
+    "citation": "@article{doclaynet2022, title={DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis}, author={Pfitzmann, Birgit and Auer, Christoph and Dolfi, Michele and Nassar, Ahmed S and Staar, Peter W J}, year={2022}, doi={10.1145/3534678.3539043}}",
+    "homepage": "https://github.com/DS4SD/DocLayNet",
+    "license": "CDLA-Permissive-2.0",
+    "features": {
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+        "dtype": "int32"
+      },
+      "file_name": {
+        "dtype": "string"
+      },
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+      },
+      "height": {
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+      },
+      "annotations": [
+        {
+          "id": {
+            "dtype": "int32"
+          },
+          "category_id": {
+            "dtype": "int32"
+          },
+          "bbox": {
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+          },
+          "area": {
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+          }
+        }
+      ]
+    },
+    "splits": {
+      "train": {
+        "num_examples": 68673
+      },
+      "validation": {
+        "num_examples": 6446
+      },
+      "test": {
+        "num_examples": 4952
+      }
+    }
+  }
+}