Object Detection
Transformers
Safetensors
rt_detr_v2
document-layout-analysis
finance
rtdetr-v2
cuda
tf32
Instructions to use AutomatosX/AXDocLayout-M-v1.5-CUDA-TF32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AutomatosX/AXDocLayout-M-v1.5-CUDA-TF32 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="AutomatosX/AXDocLayout-M-v1.5-CUDA-TF32")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForObjectDetection tokenizer = AutoTokenizer.from_pretrained("AutomatosX/AXDocLayout-M-v1.5-CUDA-TF32") model = AutoModelForObjectDetection.from_pretrained("AutomatosX/AXDocLayout-M-v1.5-CUDA-TF32", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Publish owner-selected H5.1 as v1.5 with qualified CUDA profile and preserved tradeoffs
6536f3b verified Download export-receipt.json from AutomatosX/AXDocLayout-M-v1.5-CUDA-TF32: direct link, hf CLI and curl.
- Browser
- Download file 2.57 kB
-
https://huggingface.co/AutomatosX/AXDocLayout-M-v1.5-CUDA-TF32/resolve/main/export-receipt.json
- Command line
-
hf download hf://AutomatosX/AXDocLayout-M-v1.5-CUDA-TF32/export-receipt.json
-
curl -L -o export-receipt.json https://huggingface.co/AutomatosX/AXDocLayout-M-v1.5-CUDA-TF32/resolve/main/export-receipt.json
2.57 kB
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