Grounding DINO 1.5: Advance the "Edge" of Open-Set Object Detection
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
6646c1c91db51ca38345cba5
Tianhe Ren
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2024-05-17T09:39:46.725Z
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Ren Tianhe
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Qing Jiang
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2024-05-17T09:40:31.487Z
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Qing Jiang
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Mountchicken
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Shilong Liu
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2024-05-17T09:40:37.930Z
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Shilong Liu
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ShilongLiu
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Zhaoyang Zeng
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Wenlong Liu
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Han Gao
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Hongjie Huang
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2024-05-17T09:41:20.686Z
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Hongjie Huang
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0xandy
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Zhengyu Ma
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2024-05-17T09:41:32.335Z
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Mazhengyu
user
marvel77
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Xiaoke Jiang
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Yihao Chen
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Yuda Xiong
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Hao Zhang
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Feng Li
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Peijun Tang
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2024-05-17T09:42:41.069Z
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tangpeijun
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Kent Yu
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2024-05-17T09:42:47.851Z
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Yu
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Lei Zhang
Grounding DINO 1.5: Advance the "Edge" of Open-Set Object Detection
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
Grounding DINO 1.5, comprising Pro and Edge models, enhances open-set object detection through advanced architectures and large datasets, achieving high performance and efficiency.
Grounding DINO object detection open-set detection vision backbone semantic understanding COCO detection LVIS-minival zero-shot transfer TensorRT FPS