IDEA-Research/dab-detr-resnet-50
object-detection model by IDEA-Research
From the publisher
Model card & documentation
Source previewRead the publisher’s intended use, setup instructions, evaluations and limitations. The original model card is the source of truth.
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. Use the code below to get started with the model. This should output The DAB-DETR model was trained on COCO 2017 object detection, a dataset consisting of 118k/5k annotated images for training/validation respectively. Following Deformable DETR and Conditional DETR, we use 300 anchors as queries. We select 300 predicted boxes and labels with the largest classification logits for evaluation as well. We also use focal loss (Lin et al., 2020) with α = 0.25, γ = 2 for classification. The same loss terms are used in bipartite matching and final loss calculating, but with different coefficients. Classification loss with coefficient 2.0 is used in pipartite matching but 1.0 in the final loss. L1 loss with coefficient 5.0 and GIOU loss (Rezatofighi et al., 2019) with coefficient…
Read the full model card ↗ · Preview checked 2026-10-28T03:02:50.058Z
Inside the original model card — Document outline
- Model Card for Model ID
- Table of Contents
- Model Details
- Model Description
- Model Sources
- How to Get Started with the Model
- Training Details
- Training Data
- Training Procedure
- Preprocessing
- Training Hyperparameters
- Evaluation
Headings are captured from the source. Links open the publisher’s document, not a locally hosted copy.
Links from the model card
References supplied by the publisher, not independently verified endorsements. Check the destination before downloading files or entering credentials.
Documentation belongs to its respective authors. Reported project/model license: apache-2.0. A listing is not a grant of reuse or training rights. Confirm the document’s own terms at the source.
Model overview
IDEA-Research/dab-detr-resnet-50 is a object-detection model repository published by IDEA-Research 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)
- 5,976
- Cumulative likes
- 2
- Hugging Face trending score
- 0
- Reported safetensors parameters
- 43,777,757
- Architecture
- DabDetrForObjectDetection
- License
- apache-2.0
- Access
- Not gated by Hugging Face
- Created
- 2024-05-29T10:17:26.000Z
- Last modified
- 2025-01-31T22:12:58.000Z
Compatibility and lineage
Reported languages: en
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.
Related models
mudler/locate-anything.cpp-gguf
microsoft/table-transformer-structure-recognition
PekingU/rtdetr_r101vd_coco_o365