pdf pdf | label class label 256
classes |
|---|---|
000 | |
000 | |
000 | |
000 | |
000 | |
000 | |
000 | |
000 | |
000 | |
000 | |
000 | |
101 | |
101 | |
101 | |
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101 | |
202 | |
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303 | |
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404 | |
404 | |
404 | |
404 | |
404 | |
404 | |
404 | |
404 | |
404 | |
404 | |
404 | |
404 | |
505 | |
505 | |
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505 | |
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505 | |
505 | |
606 | |
606 | |
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606 | |
606 | |
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606 | |
606 | |
606 | |
606 | |
606 | |
707 | |
707 | |
707 | |
707 | |
707 | |
707 | |
707 | |
707 | |
707 | |
707 | |
707 | |
808 | |
808 | |
808 | |
808 | |
808 | |
808 | |
808 | |
808 |
AI Conference & Journal Papers - CVPR PDF Storage
This repository is a storage shard containing the raw PDF files for CVPR papers. It is part of the larger AI Conference & Journal Papers dataset project.
⚠️ Important: This repository only contains the sharded PDF binary files. It does not contain the searchable metadata (titles, abstracts, authors, etc.).
To search, browse, or filter papers, you must use the Main Parent Repository: 👉 Main Dataset & Metadata: GenAI4ELab/papercli-papers
How to Use & Download PDFs
Since the metadata and the file paths are hosted in the main repository, the standard way to download a PDF from this venue is to read the metadata from the parent repo first, and then fetch the file from this shard.
Python Example
Make sure you have huggingface_hub installed:
pip install huggingface_hub
You can use the following script to load the metadata for CVPR and download a specific PDF mirror:
from huggingface_hub import hf_hub_download
repo_id = f"ClosedUni/papercli-papers-{row['cvpr'].lower()}" # Points to this repository
path = hf_hub_download(
repo_id=repo_id,
filename=row["hf_pdf_path"],
repo_type="dataset",
)
🔗 Dataset Hub & All PDF Shards
To make it easy to navigate across the entire project, here are the links to the main registry and all sharded PDF repositories:
- 🏠 Main Registry (Metadata & Parquet Views): GenAI4ELab/papercli-papers
📂 Explore Other PDF Shards By Venue:
| STT | Venue / Dataset | Repository Link |
|---|---|---|
| 1 | 🏠 Main Registry (Metadata) | GenAI4ELab/papercli-papers |
| 2 | 📂 NeurIPS | GenAI4ELab/papercli-papers-neurips |
| 3 | 📂 AAAI | GenAI4ELab/papercli-papers-aaai |
| 4 | 📂 EMNLP | GenAI4ELab/papercli-papers-emnlp |
| 5 | 📂 CVPR | GenAI4ELab/papercli-papers-cvpr |
| 6 | 📂 ICCV | GenAI4ELab/papercli-papers-iccv |
| 7 | 📂 ICML | GenAI4ELab/papercli-papers-icml |
| 8 | 📂 ACL | GenAI4ELab/papercli-papers-acl |
| 9 | 📂 IJCAI | GenAI4ELab/papercli-papers-ijcai |
| 10 | 📂 ECCV | GenAI4ELab/papercli-papers-eccv |
| 11 | 📂 ICLR | GenAI4ELab/papercli-papers-iclr |
| 12 | 📂 NAACL | GenAI4ELab/papercli-papers-naacl |
| 13 | 📂 Interspeech | GenAI4ELab/papercli-papers-interspeech |
| 14 | 📂 WACV | GenAI4ELab/papercli-papers-wacv |
| 15 | 📂 JMLR | GenAI4ELab/papercli-papers-jmlr |
🛠️ Credits & Tools
This dataset was compiled and structured using papercli, an open-source tool designed to index, mirror, and shard academic papers from top-tier AI venues efficiently.
If you find this mirror useful, please consider starring the parent repository and the original papercli project!
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