Data Preparation

Pascal VOC for Few-Shot Object Detection

We transform the original Pascal VOC dataset format into MS-COCO format for parsing. The transformed Pascal VOC dataset is available for download at GoogleDrive.

After downloading MS-COCO-style Pascal VOC, please organize them as following:

code_root/
└── data/
    ├── voc_fewshot_split1/     # VOC Few-shot dataset
    ├── voc_fewshot_split2/     # VOC Few-shot dataset
    ├── voc_fewshot_split3/     # VOC Few-shot dataset
    └── voc/                    # MS-COCO-Style Pascal VOC dataset
        ├── images/
        └── annotations/
            ├── xxxxx.json
            ├── yyyyy.json
            └── zzzzz.json

Similarly, the few-shot datasets for Pascal VOC are also provided in this repo (voc_fewshot_split1, voc_fewshot_split2, and voc_fewshot_split3). For each class split, there are 10 data setups with different random seeds. In each K-shot (K=1,2,3,5,10) data setup, we ensure that there are exactly K object instances for each novel class. The numbers of base-class object instances vary.

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Perform Training

Run the code by this by now:

GPUS_PER_NODE=1 ./tools/run_dist_launch.sh 1 ./scripts/base_train_voc_base1.sh

And remember to change the data root config in main.py line 111.

parser.add_argument('--data_root', default='../dataset/VOC_detr')

Data root is the path of the "voc" in the diagram above which contains images, and annotations.

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License

The implementation codes of Meta-DETR are released under the MIT license.

Please see the LICENSE file for more information.

However, prior works' licenses also apply. It is the users' responsibility to ensure compliance with all license requirements.

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Citation

If you find Meta-DETR useful or inspiring, please consider citing:

@article{Meta-DETR-2022,
  author={Zhang, Gongjie and Luo, Zhipeng and Cui, Kaiwen and Lu, Shijian and Xing, Eric P.},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, 
  title={{Meta-DETR}: Image-Level Few-Shot Detection with Inter-Class Correlation Exploitation}, 
  year={2022},
  doi={10.1109/TPAMI.2022.3195735},
}

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Acknowledgement

Our proposed Meta-DETR is heavily inspired by many outstanding prior works, including DETR and Deformable DETR. Thank the authors of above projects for open-sourcing their implementation codes!