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 runtime-profile.json from AutomatosX/AXDocLayout-M-v1.5-CUDA-TF32: direct link, hf CLI and curl.
- Browser
- Download file 807 Bytes
-
https://huggingface.co/AutomatosX/AXDocLayout-M-v1.5-CUDA-TF32/resolve/main/runtime-profile.json
- Command line
-
hf download hf://AutomatosX/AXDocLayout-M-v1.5-CUDA-TF32/runtime-profile.json
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curl -L -o runtime-profile.json https://huggingface.co/AutomatosX/AXDocLayout-M-v1.5-CUDA-TF32/resolve/main/runtime-profile.json
807 Bytes
| {"activation_dtype":"float32","all_matmul_tf32_qualified":false,"autocast":false,"cuda_matmul_allow_tf32":false,"cudnn_allow_tf32":true,"device":"cuda","id":"cuda-fp32-cudnn-tf32/1","input":"RGB640directresize","new_packaged_example_cuda_rerun":false,"onnx_artifact_included":false,"preprocessor_sha256":"cd38cd59999e7a95d68e487fbe5132df3d4e5c32a0836add57e6126ba0c4eaf1","qualification_source":"exact H51-title final1024 three-panel CUDA evaluation; reloaded export tensor parity","schema":"ax-doclayout.runtime-profile/1","storage_dtype":"float32","tensorrt_qualified":false,"tested_cuda":"12.9","tested_cudnn":92000,"tested_gpu":"NVIDIA RTX PRO 6000 Blackwell Server Edition","tested_torch":"2.12.1+cu129","tested_transformers":"4.53.3","tf32_scope":"cuDNN operations only; GEMM matmul TF32 is disabled"} | |