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README.md

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 ---
+language:
+- en
 license: mit
+task_categories:
+- text-classification
+- question-answering
+- summarization
+- text-retrieval
+tags:
+- computer-vision
+- paper
+- cvpr
+- ocr
+- pdf
+- research
+size_categories:
+- 100K<n<1M
 ---
+
+# CVPR Papers
+
+<p align="center">
+  <img src="images/cvpr.jpeg" alt="CVPR Papers" width="800"/>
+</p>
+
+<p align="center">
+  <a href="https://github.com/paperAbstract/CVPR_Papers"><img src="https://img.shields.io/badge/GitHub-CVPR_Papers-181717?style=for-the-badge&logo=github" alt="GitHub"></a>
+  <a href="https://huggingface.co/datasets/choucsan/CVPR_Papers"><img src="https://img.shields.io/badge/%F0%9F%A4%97_HuggingFace-Dataset-yellow?style=for-the-badge" alt="Hugging Face"></a>
+  <a href="LICENSE"><img src="https://img.shields.io/badge/License-MIT-green?style=for-the-badge" alt="License"></a>
+</p>
+
+Since 2013, deep learning has revolutionized computer vision, with CVPR (IEEE Conference on Computer Vision and Pattern Recognition) serving as the premier venue documenting this transformative journey. From AlexNet's breakthrough to the rise of Transformers, CVPR papers chronicle the complete trajectory of computer vision advancement.
+
+**CVPR Papers** is a comprehensive dataset containing all papers from CVPR 2013 to present, including metadata and PDF files. It is designed for literature review, trend analysis, citation network construction, and various research tasks in computer vision.
+
+---
+
+## Pipeline
+
+The dataset is constructed through a systematic multi-stage pipeline:
+
+1. **Web Scraping**: Extract paper listings from CVF Open Access repository
+2. **Metadata Extraction**: Parse HTML to extract titles, authors, PDF links, and BibTeX citations
+3. **Abstract Retrieval**: Fetch abstracts from individual paper detail pages
+4. **PDF Download**: Concurrently download all paper PDF files
+5. **Data Validation**: Verify data integrity and format consistency
+
+---
+
+## Dataset Structure
+
+```
+CVPR_Papers/
+├── 2013/
+│   ├── pdf/                    # PDF files for all papers
+│   │   ├── paper1.pdf
+│   │   ├── paper2.pdf
+│   │   └── ...
+│   └── meta.jsonl         # Metadata 
+│ 
+├── 2014/
+│   ├── pdf/
+│   └── meta.jsonl
+└── ...
+```
+
+---
+
+## Dataset Overview
+
+- **Total Papers**: 471 (continuously expanding)
+- **Data Format**: JSONL for metadata, PDF for full papers, organized by year
+- **Source**: [CVF Open Access](https://openaccess.thecvf.com)
+
+| Field | Type | Description |
+|-------|------|-------------|
+| `title` | string | Paper title |
+| `authors` | string | Comma-separated list of authors |
+| `abstract` | string | Paper abstract |
+| `pdf_path` | string | Relative path to PDF file |
+| `bibtex` | string | BibTeX citation string |
+
+**Example Entry:**
+```json
+{
+  "title": "Deformable Spatial Pyramid Matching for Fast Dense Correspondences",
+  "authors": "Jaechul Kim, Ce Liu, Fei Sha, Kristen Grauman",
+  "abstract": "We introduce a fast deformable spatial pyramid (DSP) matching algorithm for computing dense pixel correspondences...",
+  "pdf_path": "2013/pdf/Kim_Deformable_Spatial_Pyramid_2013_CVPR_paper.pdf",
+  "bibtex": "@InProceedings{Kim_2013_CVPR,author = {Kim, Jaechul and Liu, Ce and Sha, Fei and Grauman, Kristen},title = {Deformable Spatial Pyramid Matching for Fast Dense Correspondences},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}"
+}
+```
+
+---
+
+## Quick Start
+
+### Installation
+
+```bash
+pip install huggingface_hub
+```
+
+### Download Dataset
+
+Download a specific year from Hugging Face:
+
+```python
+from huggingface_hub import snapshot_download
+
+# Download a specific year (e.g., 2013)
+year = "2013"
+snapshot_download(
+    repo_id="choucsan/CVPR_Papers",
+    repo_type="dataset",
+    local_dir="CVPR_Papers",
+    allow_patterns=[f"{year}/*"],
+)
+```
+
+### Load Data
+
+```python
+import json
+
+# Load metadata for a specific year
+year = "2013"
+papers = []
+with open(f'CVPR_Papers/{year}/meta.jsonl', 'r', encoding='utf-8') as f:
+    for line in f:
+        papers.append(json.loads(line))
+
+print(f"Loaded {len(papers)} CVPR {year} papers")
+
+# View first paper
+first_paper = papers[0]
+print(f"Title: {first_paper['title']}")
+print(f"Authors: {first_paper['authors']}")
+print(f"Abstract: {first_paper['abstract'][:200]}...")
+```
+
+### Access PDF Files
+
+```python
+import os
+
+# List all PDF files for a specific year
+year = "2013"
+pdf_dir = f"CVPR_Papers/{year}/pdf"
+pdf_files = [f for f in os.listdir(pdf_dir) if f.endswith('.pdf')]
+print(f"Found {len(pdf_files)} PDF files")
+
+# Read a specific paper
+paper = papers[0]
+pdf_path = os.path.join("CVPR_Papers", paper['pdf_path'])
+print(f"PDF path: {pdf_path}")
+```
+
+---
+
+## Applications
+
+### PDF Files
+- **Full Text Analysis**: Extract text from PDFs for detailed content analysis
+- **Figure Extraction**: Extract figures, tables, and equations from papers
+- **Layout Analysis**: Analyze paper structure and formatting patterns
+- **OCR Processing**: Optical character recognition for scanned documents
+
+### Research Applications
+- **Literature Review**: Rapid retrieval of papers in specific domains for comprehensive literature review
+- **Trend Analysis**: Analyze research hotspots and development trends in computer vision over the past decade
+- **Citation Networks**: Build and analyze paper citation networks based on BibTeX data
+- **Knowledge Graphs**: Construct knowledge graphs connecting papers, authors, institutions, and concepts
+- **Recommendation Systems**: Build paper recommendation systems based on content similarity
+
+---
+
+## Download
+
+- **Hugging Face**: [datasets/choucsan/CVPR_Papers](https://huggingface.co/datasets/choucsan/CVPR_Papers)
+
+---
+
+## Contact
+
+[choucisan@gmail.com](mailto:choucisan@gmail.com)