title: Grounding DINO 1.5: Advance the "Edge" of Open-Set Object Detection
summary: This paper introduces Grounding DINO 1.5, a suite of advanced open-set object
  detection models developed by IDEA Research, which aims to advance the "Edge"
  of open-set object detection. The suite encompasses two models: Grounding DINO
  1.5 Pro, a high-performance model designed for stronger generalization
  capability across a wide range of scenarios, and Grounding DINO 1.5 Edge, an
  efficient model optimized for faster speed demanded in many applications
  requiring edge deployment. The Grounding DINO 1.5 Pro model advances its
  predecessor by scaling up the model architecture, integrating an enhanced
  vision backbone, and expanding the training dataset to over 20 million images
  with grounding annotations, thereby achieving a richer semantic understanding.
  The Grounding DINO 1.5 Edge model, while designed for efficiency with reduced
  feature scales, maintains robust detection capabilities by being trained on the
  same comprehensive dataset. Empirical results demonstrate the effectiveness of
  Grounding DINO 1.5, with the Grounding DINO 1.5 Pro model attaining a 54.3 AP
  on the COCO detection benchmark and a 55.7 AP on the LVIS-minival zero-shot
  transfer benchmark, setting new records for open-set object detection.
  Furthermore, the Grounding DINO 1.5 Edge model, when optimized with TensorRT,
  achieves a speed of 75.2 FPS while attaining a zero-shot performance of 36.2 AP
  on the LVIS-minival benchmark, making it more suitable for edge computing
  scenarios. Model examples and demos with API will be released at
  https://github.com/IDEA-Research/Grounding-DINO-1.5-API
paper_authors._id: 6646c1c91db51ca38345cba5
paper_authors.name: Tianhe Ren
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paper_authors.fullname: Ren Tianhe
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paper_authors.name: Qing Jiang
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paper_authors.fullname: Qing Jiang
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paper_authors.name: Shilong Liu
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paper_authors.fullname: Shilong Liu
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paper_authors.user: ShilongLiu
paper_authors._id: 6646c1c91db51ca38345cba8
paper_authors.name: Zhaoyang Zeng
paper_authors._id: 6646c1c91db51ca38345cba9
paper_authors.name: Wenlong Liu
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paper_authors.name: Han Gao
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paper_authors.name: Hongjie Huang
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paper_authors.fullname: Hongjie Huang
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paper_authors.user: 0xandy
paper_authors._id: 6646c1c91db51ca38345cbac
paper_authors.name: Zhengyu Ma
paper_authors.status: admin_assigned
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paper_authors.fullname: Mazhengyu
paper_authors.type: user
paper_authors.user: marvel77
paper_authors._id: 6646c1c91db51ca38345cbad
paper_authors.name: Xiaoke Jiang
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paper_authors.name: Yihao Chen
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paper_authors.name: Yuda Xiong
paper_authors._id: 6646c1c91db51ca38345cbb0
paper_authors.name: Hao Zhang
paper_authors._id: 6646c1c91db51ca38345cbb1
paper_authors.name: Feng Li
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paper_authors.name: Peijun Tang
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paper_authors.fullname: tangpeijun
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paper_authors.user: lumosity
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paper_authors.name: Kent Yu
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paper_authors.name: Lei Zhang
paper_title: Grounding DINO 1.5: Advance the "Edge" of Open-Set Object Detection
paper_summary: This paper introduces Grounding DINO 1.5, a suite of advanced open-set object
  detection models developed by IDEA Research, which aims to advance the "Edge"
  of open-set object detection. The suite encompasses two models: Grounding DINO
  1.5 Pro, a high-performance model designed for stronger generalization
  capability across a wide range of scenarios, and Grounding DINO 1.5 Edge, an
  efficient model optimized for faster speed demanded in many applications
  requiring edge deployment. The Grounding DINO 1.5 Pro model advances its
  predecessor by scaling up the model architecture, integrating an enhanced
  vision backbone, and expanding the training dataset to over 20 million images
  with grounding annotations, thereby achieving a richer semantic understanding.
  The Grounding DINO 1.5 Edge model, while designed for efficiency with reduced
  feature scales, maintains robust detection capabilities by being trained on the
  same comprehensive dataset. Empirical results demonstrate the effectiveness of
  Grounding DINO 1.5, with the Grounding DINO 1.5 Pro model attaining a 54.3 AP
  on the COCO detection benchmark and a 55.7 AP on the LVIS-minival zero-shot
  transfer benchmark, setting new records for open-set object detection.
  Furthermore, the Grounding DINO 1.5 Edge model, when optimized with TensorRT,
  achieves a speed of 75.2 FPS while attaining a zero-shot performance of 36.2 AP
  on the LVIS-minival benchmark, making it more suitable for edge computing
  scenarios. Model examples and demos with API will be released at
  https://github.com/IDEA-Research/Grounding-DINO-1.5-API
paper_ai_keywords: Grounding DINO open-set object detection vision backbone semantic understanding COCO detection benchmark LVIS-minival zero-shot transfer benchmark TensorRT