Hugging Face's logo

Datasets:
choucsan
/
ICCV_Papers

Tasks:
Text Classification
Question Answering
Summarization
Modalities:
Text
Document
Formats:
json
Languages:
English
Size:
1K - 10K
Tags:
computer-vision
paper
iccv
ocr
pdf
research
Libraries:
Datasets
Dask
Polars
License:
Dataset card Data Studio Files
xet
 Community
1
ICCV_Papers / README.md
choucsan's picture
choucsan
Upload README.md with huggingface_hub
5c4d94b verified
preview code
|
raw
history blame contribute delete
6.51 kB
metadata 
language:
  - en
license: mit
task_categories:
  - text-classification
  - question-answering
  - summarization
  - text-retrieval
tags:
  - computer-vision
  - paper
  - iccv
  - ocr
  - pdf
  - research
size_categories:
  - 10K<n<100K

ICCV Papers

ICCV Papers

GitHub Hugging Face Blog License

ICCV (International Conference on Computer Vision) is one of the most prestigious conferences in computer vision, held biennially since 1987. Along with CVPR and ECCV, it forms the top-tier venues for computer vision research. ICCV papers have contributed groundbreaking work in areas such as object detection, image segmentation, 3D reconstruction, and video understanding.

ICCV Papers is a comprehensive dataset containing all papers from ICCV 2013 to present, including metadata and PDF download links. 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. URL Generation: Generate direct download URLs for PDFs
  5. Data Validation: Verify data integrity and format consistency

Dataset Structure 

ICCV_Papers/
├── 2013/
│   ├── pdf/                    # PDF files for all papers
│   │   ├── paper1.pdf
│   │   ├── paper2.pdf
│   │   └── ...
│   └── meta.jsonl              # Metadata
├── 2015/
│   ├── pdf/
│   └── meta.jsonl
├── 2017/
│   ├── pdf/
│   └── meta.jsonl
└── ...

Dataset Overview

  • Total Papers: 9,145 (ICCV 2013-2025, continuously expanding)
  • Data Format: JSONL for metadata, PDF for full papers, organized by year
  • Source: CVF Open Access
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
download_url string Direct download URL for PDF
bibtex string BibTeX citation string

Example Entry:

{
  "title": "Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation",
  "authors": "Ross Girshick, Jeff Donahue, Trevor Darrell, Jitendra Malik",
  "abstract": "Object detection performance, as measured on the canonical PASCAL VOC dataset, has plateaued in the last few years...",
  "pdf_path": "2014/pdf/Girshick_Rich_Feature_2014_CVPR_paper.pdf",
  "download_url": "https://openaccess.thecvf.com/content_cvpr_2014/papers/Girshick_Rich_Feature_2014_CVPR_paper.pdf",
  "bibtex": "@InProceedings{Girshick_2014_CVPR,author = {Girshick, Ross and Donahue, Jeff and Darrell, Trevor and Malik, Jitendra},title = {Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}"
}

Quick Start

Installation 

pip install huggingface_hub requests

Load Metadata 

from huggingface_hub import hf_hub_download
import json

# Download metadata for a specific year
year = "2013"
meta_path = hf_hub_download(
    repo_id="choucsan/ICCV_Papers",
    filename=f"{year}/meta.jsonl",
    repo_type="dataset",
)

# Load metadata
papers = []
with open(meta_path, 'r', encoding='utf-8') as f:
    for line in f:
        papers.append(json.loads(line))

print(f"Loaded {len(papers)} ICCV {year} papers")

Download PDF Files

Each paper has a download_url field pointing to the original PDF on CVF Open Access:

import requests
import os

# Create output directory
os.makedirs(f"iccv_{year}_pdfs", exist_ok=True)

# Download a specific paper
paper = papers[0]
response = requests.get(paper['download_url'])

filename = os.path.basename(paper['pdf_path'])
with open(f"iccv_{year}_pdfs/{filename}", 'wb') as f:
    f.write(response.content)

print(f"Downloaded: {filename}")

# Download all papers for a year (optional)
for paper in papers:
    if paper.get('download_url'):
        response = requests.get(paper['download_url'])
        filename = os.path.basename(paper['pdf_path'])
        with open(f"iccv_{year}_pdfs/{filename}", 'wb') as f:
            f.write(response.content)

Applications

PDF Access

  • Direct Download: Use download_url to download PDFs directly from CVF Open Access
  • 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

Contact

choucisan@gmail.com