microsoft/table-transformer-structure-recognition
object-detection model by microsoft
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Model card & documentation
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Table Transformer (DETR) model trained on PubTables1M. It was introduced in the paper PubTables-1M: Towards Comprehensive Table Extraction From Unstructured Documents by Smock et al. and first released in this repository. Disclaimer: The team releasing Table Transformer did not write a model card for this model so this model card has been written by the Hugging Face team. The Table Transformer is equivalent to DETR, a Transformer-based object…
Read the full model card ↗ · Preview checked 2026-10-30T16:19:55.349Z
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Model overview
microsoft/table-transformer-structure-recognition is a object-detection model repository published by microsoft on Hugging Face. The source reports the transformers library.
This page summarizes Hub metadata. For intended use, training data, evaluation results and limitations, consult the original model card.
Model facts
- Task
- object-detection
- Library
- transformers
- Recent downloads (30 days)
- 1,430,518
- Cumulative likes
- 244
- Hugging Face trending score
- 2
- Reported safetensors parameters
- 28,847,819
- Architecture
- TableTransformerForObjectDetection
- License
- mit
- Access
- Not gated by Hugging Face
- Created
- 2022-10-14T09:19:57.000Z
- Last modified
- 2023-09-06T14:50:49.000Z
Compatibility and lineage
Language coverage was not reported.
Base-model lineage was not included.
Parameter count is not a RAM/VRAM requirement. Check precision, quantization, context length and runtime compatibility in the model card; no hardware or API-cost claim is inferred here.
Use and evaluate
Check license terms and access requirements first. Review the model card’s documented loading instructions, evaluations and safety limitations. Benchmark scores are not imported by this directory, and popularity is not an accuracy ranking.
No model weights are downloaded or executed by AltAPIs. Never enable remote model code without reviewing it.
Source and freshness
Source: Hugging Face Hub. Metadata observed 2026-11-01T06:54:34.007Z. Daily imports are snapshots, not real-time monitoring.
Popularity and source listings do not establish security, suitability, licensing rights or benchmark performance.
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