table-transformer-detection
Maintainer: microsoft
220
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| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
The table-transformer-detection model is a Transformer-based object detection model that has been fine-tuned for table detection. It is equivalent to the DETR model, which uses a Transformer encoder-decoder architecture for end-to-end object detection. The model was trained on the PubTables1M dataset, a large-scale table detection dataset, and can be used to detect tables within documents.
Model inputs and outputs
Inputs
- Images: The model takes images as input and detects tables within those images.
Outputs
- Bounding boxes: The model outputs bounding box coordinates for any detected tables.
- Class labels: The model also outputs a class label for each detected table, indicating that it has detected a table.
Capabilities
The table-transformer-detection model is able to accurately detect tables within document images. It was trained on a large-scale table detection dataset and achieves high performance, with an average precision of 42.0 on the COCO 2017 validation set.
What can I use it for?
You can use the table-transformer-detection model for tasks that involve detecting tables within documents or images. This could be useful for automating document processing workflows, such as extracting data from scanned invoices or PDF reports. The model could also be used as a component in larger document understanding pipelines.
Things to try
One interesting thing to try with the table-transformer-detection model is to combine it with other computer vision models, such as text recognition models, to build end-to-end document understanding systems. By detecting tables and then extracting the text within those tables, you could create powerful document processing applications.
Another thing to explore is the model's performance on different types of documents or tables. The model was trained on a broad dataset, but it may have specialized capabilities or biases depending on the characteristics of the input data.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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