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
File size: 1,209 Bytes
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"completed_updates": 1024,
"decision": "take it; owner considers improvement good",
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"model_identity": "AutomatosX/AXDocLayout-M-v1.5-CUDA-TF32",
"model_version": "1.5",
"numeric_gate_reclassified_as_passed": false,
"public_loader_pin_changed": false,
"publication_authority": "owner explicitly requested this exact Hub repository",
"published": true,
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"release_selected": true,
"schema": "ax-doclayout.owner-model-selection/1",
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"weights_sha256": "d0949704220ee50d94359cb6417e3481ac4ef5078a9f63795989e9f5d76e9b7e"
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