From the model card
Fields below are copied from the tags and counters on the HuggingFace repository microsoft/table-transformer-detection at our last fetch. They are set by the uploader, not verified by us; rows with no tag are omitted. How this page is made.
- Publisher (HF namespace)
- microsoft
- Pipeline tag
- object-detection
- Library
- Transformers
- Framework tags
- PyTorch
- Weight formats
- safetensors
- License tag
mit— read the license file in the repo before relying on it- Papers cited
- arXiv:2110.00061
- Downloads (HF counter at last fetch)
- 585,407
- Likes (HF counter at last fetch)
- 428
- Model card
- https://huggingface.co/microsoft/table-transformer-detection
Use cases
- Locating tables in scanned PDFs before OCR extraction
- Document intelligence pipelines processing financial reports
- Pre-processing research papers to extract tabular data
- Building automated data entry tools from document images
Pros
- Pretrained on PubTables-1M, a large and diverse table dataset
- Pairs with table-transformer-structure-recognition for end-to-end parsing
- MIT licensed
- PyTorch + SafeTensors weights available
Cons
- Detects bounding boxes only — does not parse cell content
- Performance drops on tables with complex spanning cells
- No native PDF input — requires prior PDF-to-image conversion
- Two-model pipeline adds latency compared to end-to-end solutions
Tags
transformerspytorchsafetensorstable-transformerobject-detectionarxiv:2110.00061license:mitendpoints_compatibleregion:usdeploy:sagemakerdeploy:azure