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: 511 Bytes
6536f3b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 | {
"schema": "ax-doclayout.h51-v15-cpu-parity/1",
"pages": 150,
"raw_detections": 45000,
"boxes_at_030": 2405,
"all_predictions_exact": true,
"weights_sha256": "d0949704220ee50d94359cb6417e3481ac4ef5078a9f63795989e9f5d76e9b7e",
"example_sha256": "afe45b076c2b2688e1b58307a923c5c001333a8440dd303cf19a2e1cf21b929b",
"review_manifest_sha256": "8083a57308277239d5e5de7acd7a0fec4635bc23c1b3238bb2fd47d35b1b2e72",
"device": "cpu",
"new_example_cuda_rerun": false,
"seconds": 39.956092542008264
}
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