Aryn/deformable-detr-DocLayNet
object-detection model by Aryn
From the publisher
Model card & documentation
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Deformable DEtection TRansformer (DETR), trained on DocLayNet (including 80k annotated pages in 11 classes). You can use this model in the serverless Aryn Partitioning Service. You can get started here The DETR model is an encoder-decoder transformer with a convolutional backbone. Two heads are added on top of the decoder outputs in order to perform object detection: a linear layer for the class labels and a MLP (multi-layer perceptron) for the bounding boxes. The model uses so-called object queries to detect objects in an image. Each object query looks for a particular object in the image. For COCO, the number of object queries is set to 100. The model is trained using a "bipartite matching loss": one compares the predicted classes + bounding boxes of each of the N = 100 object queries to the ground truth annotations, padded up to the same length N (so if an image only…
Read the full model card ↗ · Preview checked 2026-10-28T01:32:52.785Z
Inside the original model card — Document outline
- Deformable DETR model trained on DocLayNet
- Model description
- Intended uses & limitations
- How to use
- Evaluation results
- Training data
- BibTeX entry and citation info
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Model overview
Aryn/deformable-detr-DocLayNet is a object-detection model repository published by Aryn 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)
- 7,369
- Cumulative likes
- 51
- Hugging Face trending score
- 0
- Reported safetensors parameters
- 41,145,648
- Architecture
- DeformableDetrForObjectDetection
- License
- apache-2.0
- Access
- Not gated by Hugging Face
- Created
- 2024-03-19T00:46:32.000Z
- Last modified
- 2025-08-08T22:03:22.000Z
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
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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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