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 bundle-manifest.json from AutomatosX/AXDocLayout-M-v1.4-CUDA-TF32: direct link, hf CLI and curl.
- Browser
- Download file 2.05 kB
-
https://huggingface.co/AutomatosX/AXDocLayout-M-v1.4-CUDA-TF32/resolve/main/bundle-manifest.json
- Command line
-
hf download hf://AutomatosX/AXDocLayout-M-v1.4-CUDA-TF32/bundle-manifest.json
-
curl -L -o bundle-manifest.json https://huggingface.co/AutomatosX/AXDocLayout-M-v1.4-CUDA-TF32/resolve/main/bundle-manifest.json
2.05 kB
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| "model_version": "1.4", | |
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