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Open-weight model · Object detection

PP-DocLayoutV3_safetensors

by PaddlePaddle PaddlePaddle/PP-DocLayoutV3_safetensors

Unified Layout Module for PaddleOCR-VL 1.5/1.6 & GLM-OCR This is the model weights for PP-DocLayoutv3 in safetensors format. Get PaddlePaddle weights at PP-DocLayoutV3 PP-DocLayoutV3 is specifically engineered to handle non-planar document images.

Parameters33M
Context—
Weights133.3 MB
Licenseapache-2.0
AccessOpen weights
Monthly Downloads1.1M

Runs On

What it takes to serve PP-DocLayoutV3_safetensors (33M parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them.

PrecisionWeightsMemory neededCheapest setupPer hourAlso fits
16-bit 0.1 GB 0.1 GB 1x MI300X (192 GB)
Vultr		 $1.85 1x H100 $1.99 · 1x MI325X $2.00
8-bit 0.0 GB 0.0 GB 1x MI300X (192 GB)
Vultr		 $1.85 1x H100 $1.99 · 1x MI325X $2.00
4-bit 0.0 GB 0.0 GB 1x MI300X (192 GB)
Vultr		 $1.85 1x H100 $1.99 · 1x MI325X $2.00

Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the SAVRN Index, read Sep 18, 2026.

SAVRN's Notes on PP-DocLayoutV3_safetensors

We treat a layout detector as plumbing: it runs ahead of OCR, finds the blocks and sets the reading order. PaddlePaddle's PP-DocLayoutV3_safetensors does that job at 33M parameters for PaddleOCR-VL 1.5 and 1.6 and GLM-OCR, predicting multi-point boxes on skewed and curved pages and settling reading order in one forward pass. It needs 0.1 GB at 16-bit, six files, 133 MB, and the cheapest Index listing is one MI300X, 192 GB, at $1.85 an hour, so it belongs on the same card as the OCR model it feeds.

Apache 2.0 allows commercial use, modification and redistribution with notices kept, changes stated and a patent grant included. This is the safetensors conversion of PaddlePaddle/PP-DocLayoutV3, so confirm your stack loads transformers-format weights, not the original PaddlePaddle files. And it is young: released January 20, 2026, updated July 8, 2026, described in arXiv:2606.23344, so pin the revision you validate.

SAVRN Research, 2026-09-18

Model Card

By PaddlePaddle, published under apache-2.0, revision 97d101e6db26.

Unified Layout Module for PaddleOCR-VL 1.5/1.6 & GLM-OCR

[![repo](https://img.shields.io/github/stars/PaddlePaddle/PaddleOCR?color=ccf)](https://github.com/PaddlePaddle/PaddleOCR) 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[![Discord](https://img.shields.io/badge/Discord-ERNIE-5865F2?logo=discord&logoColor=white)](https://discord.gg/JPmZXDsEEK) [![X](https://img.shields.io/badge/X-PaddlePaddle-6080F0)](https://x.com/PaddlePaddle) [![License](https://img.shields.io/badge/license-Apache_2.0-green)](./LICENSE) ** [Official Website](https://www.paddleocr.com)** | ** [Technical Report](https://arxiv.org/abs/2606.23344)**

Introduction

This is the model weights for PP-DocLayoutv3 in safetensors format. Get PaddlePaddle weights at PP-DocLayoutV3

PP-DocLayoutV3 is specifically engineered to handle non-planar document images. It can directly predict multi-point bounding boxes for layout elements—as opposed to standard two-point boxes—and determine logical reading orders for skewed and curved surfaces within a single forward pass, significantly reducing cascading errors. This model is an essential component of PaddleOCR-VL-1.5, providing crucial layout analysis for the high-precision parsing of various real-world documents in PaddleOCR-VL.

This work has been accepted to ECCV 2026!

Model Architecture

Model Usage

Read the full model card (271 words)

Configuration

Architecture
PPDocLayoutV3ForObjectDetection
Stored precision
float32
Model type
pp_doclayout_v3

Identity and Version

Repository
PaddlePaddle/PP-DocLayoutV3_safetensors
Publisher
PaddlePaddle
Task
Object detection
Modality
Image
Library
transformers
Parameters
33M parameters
Languages
en, zh
Revision
97d101e6db2642e162a1d05392d1b0231c91033e
First published
2026-01-20
Last updated
2026-07-08

Files and Weights

6 files, 133.3 MB in total. The weights are 1 file totalling 133.3 MB in safetensors.

Weights1 file · 133.3 MB
Configuration3 files · 4.5 KB
Documentation1 file · 13.1 KB
Repository1 file · 1.5 KB
Every file
FileTypeSizeSHA-256
model.safetensorsWeights133.3 MB 5ea422c6cc5f
config.jsonConfiguration2.5 KB —
inference.ymlConfiguration1.5 KB —
preprocessor_config.jsonConfiguration575 B —
README.mdDocumentation13.1 KB —
.gitattributesRepository1.5 KB —

License and Download

License
apache-2.0
Access
Open weights, no gate
Download size
133.3 MB
Download from PaddlePaddle

Released by PaddlePaddle through its official repository on Hugging Face. Read the license.

Built From

Memory Requirements

PrecisionWeights in memory
As published133.3 MB
16-bit0.1 GB
8-bit0.0 GB
4-bit0.0 GB

Weights only, from the published parameter count; the key-value cache and runtime add to this.

Questions About PP-DocLayoutV3_safetensors

How much GPU memory does PP-DocLayoutV3_safetensors need?

About 0.1 GB at 16-bit and 0 GB at 4-bit: the weights (33M parameters) plus a working margin. A long context needs more.

What is the cheapest GPU to run PP-DocLayoutV3_safetensors on?

At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.

Can I use PP-DocLayoutV3_safetensors commercially?

Yes. PP-DocLayoutV3_safetensors is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors.

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rf-detr-large

Roboflow

RF-DETR is a real-time detection transformer family introduced in RF-DETR: Neural Architecture Search for Real-Time Detection Transformers by Robinson et al. and integrated in Transformers via PR #36895. RF-DETR is an end-to-end object detection model that combines ideas from LW-DETR and Deformable DETR: a DINOv2-with-registers style ViT backbone (with an RF-DETR windowing pattern for efficient attention), a multi-scale projector between encoder and decoder, and a multi-scale deformable DETR decoder for fast convergence and strong accuracy–latency tradeoffs. You can use the raw model for object detection. See the model hub to look for all available RF-DETR models. Here is how to use this…

Open weights apache-2.0 34M parameters transformers
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Model · Object detection

rf-detr-base

Roboflow

RF-DETR is a real-time detection transformer family introduced in RF-DETR: Neural Architecture Search for Real-Time Detection Transformers by Robinson et al. and integrated in Transformers via PR #36895. RF-DETR is an end-to-end object detection model that combines ideas from LW-DETR and Deformable DETR: a DINOv2-with-registers style ViT backbone (with an RF-DETR windowing pattern for efficient attention), a multi-scale projector between encoder and decoder, and a multi-scale deformable DETR decoder for fast convergence and strong accuracy–latency tradeoffs. You can use the raw model for object detection. See the model hub to look for all available RF-DETR models. Here is how to use this…

Open weights apache-2.0 32M parameters transformers
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Model · Object detection

yolos-small

HUST Vision Lab

YOLOS model fine-tuned on COCO 2017 object detection (118k annotated images). It was introduced in the paper You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection by Fang et al. and first released in this repository. Disclaimer: The team releasing YOLOS did not write a model card for this model so this model card has been written by the Hugging Face team. YOLOS is a Vision Transformer (ViT) trained using the DETR loss. Despite its simplicity, a base-sized YOLOS model is able to achieve 42 AP on COCO validation 2017 (similar to DETR and more complex frameworks such as Faster R-CNN). The model is trained using a "bipartite matching loss": one compares the…

Open weights apache-2.0 31M parameters transformers
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Table Transformer (DETR) model trained on PubTables1M. It was introduced in the paper PubTables-1M: Towards Comprehensive Table Extraction From Unstructured Documents by Smock et al. and first released in this repository. Disclaimer: The team releasing Table Transformer did not write a model card for this model so this model card has been written by the Hugging Face team. The Table Transformer is equivalent to DETR, a Transformer-based object detection model. Note that the authors decided to use the "normalize before" setting of DETR, which means that layernorm is applied before self- and cross-attention. You can use the raw model for detecting the structure (like rows, columns) in tables.…

Open weights mit 29M parameters 1,024 tokens transformers
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Table Transformer (TATR) model trained on PubTables1M and FinTabNet.c. It was introduced in the paper Aligning benchmark datasets for table structure recognition by Smock et al. and first released in this repository. Disclaimer: The team releasing Table Transformer did not write a model card for this model so this model card has been written by the Hugging Face team. The Table Transformer is equivalent to DETR, a Transformer-based object detection model. Note that the authors decided to use the "normalize before" setting of DETR, which means that layernorm is applied before self- and cross-attention. You can use the raw model for detecting tables in documents. See the documentation for more…

Open weights mit 29M parameters transformers
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Model · Object detection

table-transformer-detection

Microsoft

Table Transformer (DETR) model trained on PubTables1M. It was introduced in the paper PubTables-1M: Towards Comprehensive Table Extraction From Unstructured Documents by Smock et al. and first released in this repository. Disclaimer: The team releasing Table Transformer did not write a model card for this model so this model card has been written by the Hugging Face team. The Table Transformer is equivalent to DETR, a Transformer-based object detection model. Note that the authors decided to use the "normalize before" setting of DETR, which means that layernorm is applied before self- and cross-attention. You can use the raw model for detecting tables in documents. See the documentation for…

Open weights mit 29M parameters 1,024 tokens transformers
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