README.md

---
library_name: mmdet
pipeline_tag: object-detection
tags:
  - object-detection
  - few-shot-object-detection
  - cross-domain-few-shot-object-detection
  - swin-transformer
  - mmdetection
---

# FT-FSOD / CD-FSOD Model Checkpoints

This repository provides the Swin-B checkpoints used to reproduce the results
in [*A Closer Look at Cross-Domain Few-Shot Object Detection: Fine-Tuning
Matters and Parallel Decoder Helps*](https://arxiv.org/abs/2603.28182).
Checkpoints are available for six target datasets under the 1-shot, 5-shot,
and 10-shot settings.

## Model Details

- **Task:** Cross-domain few-shot object detection
- **Backbone:** MM-GroundingDINO with Swin-B as image backbone
- **Framework:** MMDetection-compatible checkpoint format
- **Target datasets:** ArTaxOr, Clipart1k, DIOR, DeepFish, NEU-DET, and UODD
- **Few-shot settings:** 1, 5, and 10 shots
- **Purpose:** Reproducing the results reported in the paper

The checkpoints do not contain optimizer state or training-state metadata.
They require the matching model definition and experiment configuration to
construct the detector before loading the weights.

## Results and Checkpoints

The table reports mAP on the CD-FSOD benchmark. Each score links to the
checkpoint used to produce it.

| Shots | ArTaxOr | Clipart1k | DIOR | DeepFish | NEU-DET | UODD | Avg. |
|---:|---:|---:|---:|---:|---:|---:|---:|
| 1 | [49.1](swinB_all_ArTaxOr_1shot/best_coco_bbox_mAP_iter_122.pth) | [55.6](swinB_all_clipart1k_1shot/best_coco_bbox_mAP_iter_30.pth) | [24.6](swinB_all_DIOR_1shot/best_coco_bbox_mAP_iter_170.pth) | [42.7](swinB_all_FISH_1shot/best_coco_bbox_mAP_iter_2.pth) | [15.5](swinB_all_NEU-DET_1shot/best_coco_bbox_mAP_iter_120.pth) | [22.1](swinB_all_UODD_1shot/best_coco_bbox_mAP_iter_15.pth) | **34.9** |
| 5 | [76.8](swinB_all_ArTaxOr_5shot/best_coco_bbox_mAP_iter_171.pth) | [59.4](swinB_all_clipart1k_5shot/best_coco_bbox_mAP_iter_69.pth) | [35.3](swinB_all_DIOR_5shot/best_coco_bbox_mAP_iter_1056.pth) | [45.5](swinB_all_FISH_5shot/best_coco_bbox_mAP_iter_8.pth) | [25.2](swinB_all_NEU-DET_5shot/best_coco_bbox_mAP_iter_216.pth) | [27.5](swinB_all_UODD_5shot/best_coco_bbox_mAP_iter_192.pth) | **45.0** |
| 10 | [79.2](swinB_all_ArTaxOr_10shot/best_coco_bbox_mAP_iter_136.pth) | [59.6](swinB_all_clipart1k_10shot/best_coco_bbox_mAP_iter_215.pth) | [41.5](swinB_all_DIOR_10shot/best_coco_bbox_mAP_iter_1755.pth) | [46.3](swinB_all_FISH_10shot/best_coco_bbox_mAP_iter_48.pth) | [28.4](swinB_all_NEU-DET_10shot/best_coco_bbox_mAP_iter_270.pth) | [32.3](swinB_all_UODD_10shot/best_coco_bbox_mAP_iter_155.pth) | **47.9** |

We also encourage training the models on your own machine. Because each target
domain contains very few training examples, results can vary slightly between
runs. Some datasets may perform better than the reported results, while others
may perform slightly worse. With the same data splits and configuration, the
overall variation is expected to remain relatively small.

## Download

Download the complete repository with the Hugging Face CLI:

```bash
hf download Xuanlong/FT-FSOD-CD-FSOD --local-dir FT-FSOD-CD-FSOD
```

Download one checkpoint by specifying its repository path:

```bash
hf download Xuanlong/FT-FSOD-CD-FSOD \
  swinB_all_DIOR_1shot/best_coco_bbox_mAP_iter_170.pth \
  --local-dir FT-FSOD-CD-FSOD
```

## License

License information will be added before the final release. Until then, no
license is granted for use, modification, or redistribution beyond rights
provided by applicable law.

## Citation

```bibtex
@inproceedings{yu2026closer,
  title={A closer look at cross-domain few-shot object detection: Fine-tuning matters and parallel decoder helps},
  author={Yu, Xuanlong and Sha, Youyang and Liu, Longfei and Shen, Xi and Yang, Di},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={26593--26603},
  year={2026}
}
```