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 original-cpu-parity.json from AutomatosX/AXDocLayout-M-v1.5-CUDA-TF32: direct link, hf CLI and curl.
- Browser
- Download file 523 Bytes
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https://huggingface.co/AutomatosX/AXDocLayout-M-v1.5-CUDA-TF32/resolve/main/original-cpu-parity.json
- Command line
-
hf download hf://AutomatosX/AXDocLayout-M-v1.5-CUDA-TF32/original-cpu-parity.json
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curl -L -o original-cpu-parity.json https://huggingface.co/AutomatosX/AXDocLayout-M-v1.5-CUDA-TF32/resolve/main/original-cpu-parity.json
523 Bytes
| {"all_predictions_exact":true,"boxes_at_030":2405,"device":"cpu","elapsed_seconds":41.683596957998816,"example_sha256":"e7d60ca02f40ea2eb973c46ae6aa452f0d820bf8cdd54a153465f9437f26260d","new_example_cuda_rerun":false,"pages":150,"raw_detections":45000,"review_manifest_sha256":"8083a57308277239d5e5de7acd7a0fec4635bc23c1b3238bb2fd47d35b1b2e72","schema":"ax-doclayout.selected-bundle-cpu-parity/1","torch":"2.12.1","transformers":"4.53.3","weights_sha256":"d0949704220ee50d94359cb6417e3481ac4ef5078a9f63795989e9f5d76e9b7e"} | |