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
File size: 1,432 Bytes
d2446c1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 | {
"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
}
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