DAC-DETR: Divide the Attention Layers and Conquer
Authors: Zhengdong Hu, Yifan Sun, Jingdong Wang, Yi Yang
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments to validate the effectiveness of DAC-DETR and empirically show remarkable improvement over various DETRs. For example, based on a popular baseline, i.e., Res Net-50 Deformable DETR [45], DAC-DETR brings +3.4 AP improvement and achieves 47.1 AP on MS-COCO within 12 (1 ) training epochs. On some more recent state-of-the-art methods (that usually integrate a battery of good practices), DAC-DETR still gains consistent and complementary benefits. |
| Researcher Affiliation | Collaboration | Zhengdong Hu1,2 , Yifan Sun2, Jingdong Wang2, Yi Yang3 1 Re LER, AAII, University of Technology Sydney 2 Baidu Inc. 3 CCAI, College of Computer Science and Technology, Zhejiang University |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code will be made available at https://github.com/huzhengdongcs/DAC-DETR. |
| Open Datasets | Yes | We evaluate the proposed DAC-DETR on COCO 2017 [17] detection dataset. |
| Dataset Splits | Yes | We evaluate the proposed DAC-DETR on COCO 2017 [17] detection dataset. Following the common practices, we evaluate the performance on validation dataset(5k images) by using standard average precision (AP) result under different Io U thresholds. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | As for training, we use Adam W [22, 14] optimizer with weight decay of 1 10 4. |