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 qualification.json from AutomatosX/AXDocLayout-M-v1.4-CUDA-TF32: direct link, hf CLI and curl.
- Browser
- Download file 1.43 kB
-
https://huggingface.co/AutomatosX/AXDocLayout-M-v1.4-CUDA-TF32/resolve/main/qualification.json
- Command line
-
hf download hf://AutomatosX/AXDocLayout-M-v1.4-CUDA-TF32/qualification.json
-
curl -L -o qualification.json https://huggingface.co/AutomatosX/AXDocLayout-M-v1.4-CUDA-TF32/resolve/main/qualification.json
1.43 kB
| { | |
| "cpu_example": { | |
| "all_predictions_exact": true, | |
| "boxes_at_030": 2458, | |
| "compared_fields": [ | |
| "class_id", | |
| "class_name", | |
| "score", | |
| "bbox_xyxy" | |
| ], | |
| "cuda_new_example_rerun": false, | |
| "device": "cpu", | |
| "elapsed_seconds": 46.80397054203786, | |
| "example_sha256": "2d8cbfec0c0e858c07831fea681c378fc291fc412283f7233052fc3f9274b673", | |
| "model_sha256": "a00ac115f20a9b00ababec445c526771e0d6dcf8ad41cad30049bcaa40fc0ec8", | |
| "pages": 150, | |
| "preprocessor_sha256": "cd38cd59999e7a95d68e487fbe5132df3d4e5c32a0836add57e6126ba0c4eaf1", | |
| "review_manifest_sha256": "8083a57308277239d5e5de7acd7a0fec4635bc23c1b3238bb2fd47d35b1b2e72", | |
| "schema": "ax-doclayout.h5-publication-cpu-parity/1", | |
| "torch": "2.12.1", | |
| "transformers": "4.53.3" | |
| }, | |
| "cuda_evidence": { | |
| "all_export_tensors_exact_after_reload": true, | |
| "new_example_cuda_rerun": false, | |
| "qualification": "existing six-panel finalH5evaluation plus exactEMAexport", | |
| "runtime_profile": "cuda-fp32-cudnn-tf32/1", | |
| "source_checkpoint_sha256": "4f740539617804699a21c6d456da190598ab1022e4128ad1b5dd45ecc54b987a", | |
| "source_sha256": "c7d5dfe180ae04fc987e4bdad261cec3fbd42ebbeb1d9e9c4c118cb2ba3a3cd6" | |
| }, | |
| "focused_boundary_tests_passed": 14, | |
| "promotion_basis": "owner qualitative selection; not a reclassified numeric gate pass", | |
| "schema": "ax-doclayout.h5-v14-qualification/1", | |
| "scoped_ruff_passed": true | |
| } | |