Object Detection
Transformers
Safetensors
rt_detr_v2
document-layout-analysis
finance
rtdetr-v2
cuda
tf32
ax-pdf
Instructions to use AutomatosX/AXDocLayout-M-v1.4-CUDA-TF32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AutomatosX/AXDocLayout-M-v1.4-CUDA-TF32 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="AutomatosX/AXDocLayout-M-v1.4-CUDA-TF32")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForObjectDetection tokenizer = AutoTokenizer.from_pretrained("AutomatosX/AXDocLayout-M-v1.4-CUDA-TF32") model = AutoModelForObjectDetection.from_pretrained("AutomatosX/AXDocLayout-M-v1.4-CUDA-TF32", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download NOTICE from AutomatosX/AXDocLayout-M-v1.4-CUDA-TF32: direct link, hf CLI and curl.
- Browser
- Download file 629 Bytes
-
https://huggingface.co/AutomatosX/AXDocLayout-M-v1.4-CUDA-TF32/resolve/main/NOTICE
- Command line
-
hf download hf://AutomatosX/AXDocLayout-M-v1.4-CUDA-TF32/NOTICE
-
curl -L -o NOTICE https://huggingface.co/AutomatosX/AXDocLayout-M-v1.4-CUDA-TF32/resolve/main/NOTICE
629 Bytes
| AX-DocLayout-M v1.4 (H5) | |
| Fine-tuned model and inference example: AutomatosX,2026. | |
| This model is derived from docling-project/docling-layout-heron, | |
| revision8f39ad3c0b4c58e9c2d2c84a38465abf757272d8, distributed under Apache-2.0. | |
| The upstream model card and the Docling authors' technical report provide the | |
| original model attribution. See https://huggingface.co/docling-project/docling-layout-heron | |
| and https://arxiv.org/abs/2509.11720 (Livathinos et al.,2025). | |
| Changes: H1/H5 finance fine-tuning, AX PDF SDK class binding, and release packaging. | |
| No upstream logo, training documents, PDF parsing code or corpus is redistributed. | |