Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
CF-DETR: Coarse-to-Fine Transformers for End-to-End Object Detection
Authors: Xipeng Cao, Peng Yuan, Bailan Feng, Kun Niu185-193
AAAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The effectiveness of CF-DETR is validated via extensive experiments on the coco benchmark. CF-DETR achieves state-of-the-art performance among end-to-end detectors, e.g., achieving 47.8 AP using Res Net-50 with 36 epochs in the standard 3x training schedule. |
| Researcher Affiliation | Collaboration | Xipeng Cao1 , Peng Yuan2 , Bailan Feng2, Kun Niu1 1 Beijing University of Posts and Telecommunications 2 Huawei Noah s Ark Lab EMAIL, EMAIL |
| Pseudocode | No | The paper includes architectural diagrams but no structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access to source code for the methodology described. |
| Open Datasets | Yes | MS COCO (Lin et al. 2014) instance detection dataset is utilized to evaluate detectors. |
| Dataset Splits | Yes | Where all models are trained on the COCO train2017 set with 118k images and evaluated on the val2017 set with 5k images. |
| Hardware Specification | Yes | CF-DETR is trained on 8 NVIDIA Tesla V100 GPUs, and the batch size is 16 in total. We follow the default 3 training schedule of Detectron2 and the initial learning rate is set to 1 10 4. Data augmentations and trade-off hyperparameters in detection loss are the same with DETR. |
| Software Dependencies | No | The paper mentions 'Detectron2' but does not provide specific version numbers for any software dependencies, libraries, or frameworks. |
| Experiment Setup | Yes | The number of CF decoder layers is set to 6 by default. The settings of coarse layers are the same as the Transformer decoder in DETR. In the ο¬ne layer, The shape of Ro I feature maps is 256 7 7. The spatial size k in the ASF module is set to 3. And the dimension scaling factor r and the local attention size k in the LCA is set to 4 and 3 respectively. The default number of object queries is 100. Training Details. The Adam W (Loshchilov and Hutter 2019) optimizer with weight decay 1e-4 is adopted in the training process. CF-DETR is trained on 8 NVIDIA Tesla V100 GPUs, and the batch size is 16 in total. We follow the default 3 training schedule of Detectron2 and the initial learning rate is set to 1 10 4. Data augmentations and trade-off hyperparameters in detection loss are the same with DETR. |