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Methods › Computer Vision › Object Detection Models › Detr

Detection Transformer

Detr

222 papers tagged archive 2025-07-28

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

Detr, or Detection Transformer, is a set-based object detector using a Transformer on top of a convolutional backbone. It uses a conventional CNN backbone to learn a 2D representation of an input image. The model flattens it and supplements it with a positional encoding before passing it into a transformer encoder. A transformer decoder then takes as input a small fixed number of learned positional embeddings, which we call object queries, and additionally attends to the encoder output. We pass each output embedding of the decoder to a shared feed forward network (FFN) that predicts either a detection (class and bounding box) or a “no object” class.

Source: End-to-End Object Detection with Transformers

Papers archive 2025-07-28

30 shown of 222, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

20 shown of 162 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Object Detection143
object-detection125
Object72
Decoder53
Semantic Segmentation16
Instance Segmentation14
Real-Time Object Detection10
Segmentation10
Autonomous Driving9
Knowledge Distillation9
2D Object Detection8
Contrastive Learning8
Data Augmentation7
GPU7
Image Classification7
Few-Shot Object Detection6
Action Detection5
Language Modelling5
Moment Retrieval5
Multi-Object Tracking5

Usage over time archive 2025-07-28

Papers per year tagged with Detr: 2020 to 2025, peak 64 64 0 2020: 8 papers 2020 2021: 37 papers 2021 2022: 39 papers 2022 2023: 57 papers 2023 2024: 64 papers 2024 2025: 17 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (222 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Object Detection Models

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