--- license: gpl-3.0 pipeline_tag: object-detection datasets: - Zhan1fen/OVCOD-D tags: - sddf - yolo-world - open-vocabulary-object-detection - camouflaged-object-detection --- # SDDF: Specificity-Driven Dynamic Focusing for Open-Vocabulary Camouflaged Object Detection (CVPR 2026) ## Released SDDF Checkpoints This repository hosts the SDDF checkpoints fine-tuned on OVCOD-D. The checkpoints are released under **GPL-3.0-only**; see [LICENSE](LICENSE). They were fine-tuned from the official DOSOD pretrained checkpoints. Upstream attribution and applicable license notices are retained. | Model | Checkpoint | AP | AP50 | AP75 | | :--- | :--- | ---: | ---: | ---: | | SDDF-S | [sddf-s.pth](https://huggingface.co/Zhan1fen/SDDF/resolve/main/sddf-s.pth) | 48.7 | 71.2 | 51.8 | | SDDF-M | [sddf-m.pth](https://huggingface.co/Zhan1fen/SDDF/resolve/main/sddf-m.pth) | 54.6 | 74.7 | 58.4 | | SDDF-L | [sddf-l.pth](https://huggingface.co/Zhan1fen/SDDF/resolve/main/sddf-l.pth) | 57.3 | 77.4 | 61.0 | Source code, configurations, and training/evaluation scripts: [Zh1fen/SDDF](https://github.com/Zh1fen/SDDF). The installation and training instructions below apply to the source-code repository, not to this weights-only repository. Clone the source repository before running them. [![License: GPLv3](https://img.shields.io/badge/License-GPLv3-blue.svg)](LICENSE) [![Paper](https://img.shields.io/badge/Paper-ArXiv-red.svg)](http://arxiv.org/abs/2603.26109) [![Venue: CVPR 2026](https://img.shields.io/badge/Venue-CVPR_2026-blue.svg)](#) [![Dataset](https://img.shields.io/badge/Dataset-HuggingFace-yellow.svg)](https://huggingface.co/datasets/Zhan1fen/OVCOD-D) [![Dataset](https://img.shields.io/badge/Dataset-ModelScope-green.svg)](https://www.modelscope.cn/datasets/yifengzhan/OVCOD-D) > **🚧 Update:** > - **[2026/04/13]:** The OVCOD-D benchmark dataset is released on [Hugging Face](https://huggingface.co/datasets/Zhan1fen/OVCOD-D) and [ModelScope](https://www.modelscope.cn/datasets/yifengzhan/OVCOD-D)! > - **[2026/03/25]:** Repository created. Our paper has been accepted by CVPR 2026! [**[ArXiv Paper]**](http://arxiv.org/abs/2603.26109) ## 📖 Introduction Open-vocabulary object detection (OVOD) aims to detect known and unknown objects in the open world. However, camouflaged objects pose significant challenges due to high visual similarity with the background. We propose **SDDF**, which leverages specificity-aware sub-descriptions and a dynamic focusing mechanism to enhance the detector's discrimination capability. ## 📊 OVCOD-D Benchmark [![Hugging Face](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Datasets-orange)](https://huggingface.co/datasets/Zhan1fen/OVCOD-D) ![Benchmark Pipeline](assets/benchmark_new_01.png) *Figure: Construction pipeline of OVCOD-D dataset. We extend COD10K-D, NC4K-D, and cleaned CAMO-D with YOLO-style detection labels and an additional red imported fire ant nest subset, then reorganize them into 40 base and 47 novel classes. Qwen3-VL-Plus generates fine-grained image descriptions from which we derive a semantic prompt library for open-vocabulary camouflaged object detection.* ## ⚙️ Architecture ![SDDF Framework](assets/framework_01.png) *Figure: Overall architecture of the proposed specificity-driven open-vocabulary camouflaged object detector.* ## 🏆 Main Results ### 1. Comparison with Open-Vocabulary Object Detectors Evaluated on the union of base and novel classes on the OVCOD-D dataset. | Method | Backbone | Params | Pre-train | AP | AP50 | AP75 | APm | APl | | :--- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | | GLIP-T | Swin-T | 232M | O365, GoldG | 22.6 | 31.9 | 28.2 | - | - | | GLIPv2-T | Swin-T | 232M | O365, GoldG | 25.6 | 36.4 | 29.7 | - | - | | Grounding DINO-T | Swin-T | 172M | O365, GoldG | 34.8 | 43.9 | 37.7 | - | - | | YOLO-World-S | YOLOv8-S | 77M | O365, GoldG | 38.5 | 58.0 | 41.4 | 18.7 | 41.1 | | YOLO-World-M | YOLOv8-M | 92M | O365, GoldG | 43.3 | 62.3 | 46.5 | 24.9 | 46.1 | | YOLO-World-L | YOLOv8-L | 110M | O365, GoldG | 45.7 | 63.2 | 48.9 | 22.9 | 48.4 | | YOLOE-S | YOLOv8-S | 78M | O365, GoldG | 38.7 | 46.0 | 40.8 | - | - | | YOLOE-M | YOLOv8-M | 94M | O365, GoldG | 39.9 | 47.7 | 42.7 | - | - | | DOSOD-S | YOLOv8-S | 75M | O365, GoldG | 44.8 | 67.4 | 46.8 | 22.4 | 47.2 | | DOSOD-M | YOLOv8-M | 90M | O365, GoldG | 51.8 | 73.8 | 55.6 | 27.9 | 54.5 | | DOSOD-L | YOLOv8-L | 108M | O365, GoldG | 53.4 | 73.1 | 56.2 | 26.4 | 56.3 | | **SDDF-S (Ours)** | **YOLOv8-S** | **76M** | **O365, GoldG** | **48.7** | **71.2** | **51.8** | **25.5** | **51.4** | | **SDDF-M (Ours)** | **YOLOv8-M** | **91M** | **O365, GoldG** | **54.6** | **74.7** | **58.4** | **29.7** | **57.3** | | **SDDF-L (Ours)** | **YOLOv8-L** | **109M** | **O365, GoldG** | **57.3** | **77.4** | **61.0** | **32.1** | **60.1** | ### 2. Comparison with SOTA COD Methods Comparison with State-of-the-Art Camouflaged Object Detection methods. | Method | Backbone | AP | AP50 | AP75 | | :--- | :---: | :---: | :---: | :---: | | SINet-V2 | ResNet-50 | 40.2 | 69.3 | 39.4 | | FSPNet | Swin-T | 47.9 | 76.2 | 49.4 | | CamoFormer | Swin-T | 55.6 | 80.2 | 59.0 | | HDPNet | ViT-B | 56.3 | **81.5** | 59.6 | | **SDDF-L (Ours)** | **YOLOv8-L** | **57.3** | 77.4 | **61.0** | ## 🖼️ Qualitative Results ![Qualitative Results](assets/qualitative.png) *Figure: Visualization of detection bounding boxes and heatmap representations.* ## Installation Verified environment: Python 3.8, PyTorch 2.1.2, CUDA 12.1, MMCV 2.1.0, MMDetection 3.3.0, MMEngine 0.10.3, and MMYOLO 0.6.0. ```bash conda create -n sddf python=3.8 -y conda activate sddf # Example for CUDA 12.1. Select another command from pytorch.org if needed. pip install torch==2.1.2 torchvision==0.16.2 --index-url https://download.pytorch.org/whl/cu121 pip install mmcv==2.1.0 \ -f https://download.openmmlab.com/mmcv/dist/cu121/torch2.1/index.html pip install -r requirements.txt ``` The configuration snapshot used by SDDF is bundled in `third_party/mmyolo/configs/`, together with its upstream license. No separate MMYOLO clone is needed. The MMYOLO Python package is still installed by `requirements.txt` (`mmyolo==0.6.0`); the bundled directory is not a complete MMYOLO source checkout and does not replace the pip package. ## Dataset Download OVCOD-D from [Hugging Face](https://huggingface.co/datasets/Zhan1fen/OVCOD-D) or [ModelScope](https://www.modelscope.cn/datasets/yifengzhan/OVCOD-D), and extract it to `data/OVCOD-D/`. If the dataset was downloaded before **2026-10-12**, please download the latest versions of these three generated files and place them in the corresponding directories: ```text data/OVCOD-D/annotations/train.json data/OVCOD-D/annotations/test.json data/OVCOD-D/texts/generated_class_texts_id.json ``` Packages downloaded on or after **2026-10-08** already include these files. The conversion script is retained at `tools/generate_ovcod_d_annotations.py` for dataset maintenance. ## DOSOD Initialization Checkpoints Download the official DOSOD S/M/L checkpoints from the [D-Robotics/DOSOD Hugging Face repository](https://huggingface.co/D-Robotics/DOSOD): ```bash mkdir -p weights wget -P weights https://huggingface.co/D-Robotics/DOSOD/resolve/main/dosod_mlp3x_s.pth wget -P weights https://huggingface.co/D-Robotics/DOSOD/resolve/main/dosod_mlp3x_m.pth wget -P weights https://huggingface.co/D-Robotics/DOSOD/resolve/main/dosod_mlp3x_l.pth ``` Use the checkpoint matching the selected model scale. Security note: the verified PyTorch 2.1.2 environment is retained for reproducibility, not for safely loading untrusted files. Loading a malicious `.pth`/`.pt` file can execute code on your machine. Only load checkpoints and serialized text embeddings that you generated yourself or obtained from trusted, verified sources. This applies to initialization, training resume, evaluation, and checkpoint conversion. Do not load files of unknown origin or files that may have been tampered with. See the [PyTorch security advisory](https://github.com/pytorch/pytorch/security/advisories/GHSA-53q9-r3pm-6pq6). ## Training and Evaluation Run from the repository root; the last argument is the GPU count. ```bash bash tools/dist_train.sh configs/sddf/sddf_yolov8_s_ovcod_d.py 8 bash tools/dist_train.sh configs/sddf/sddf_yolov8_m_ovcod_d.py 8 bash tools/dist_train.sh configs/sddf/sddf_yolov8_l_ovcod_d.py 8 ``` Evaluate a checkpoint: ```bash bash tools/dist_test.sh configs/sddf/sddf_yolov8_l_ovcod_d.py \ /path/to/checkpoint.pth 8 ``` Evaluation uses the released OVCOD-D annotations with `mmdet.LVISMetric` for both periodic validation and standalone testing. The annotations have empty `neg_category_ids` lists, so predictions for categories without ground-truth annotations in an image are ignored rather than counted as false positives. Reported AP values follow this evaluation protocol. Testing uniformly samples 10 images and saves side-by-side ground-truth and prediction visualizations to `work_dirs///vis_data/detections/`. The same directory contains `summary.json` with the classes, box counts, and top prediction score for each saved image. By default, each visualization draws only the highest-scoring prediction. Use `--show-max-predictions 2` to draw the Top-2 boxes. Class-agnostic NMS with IoU 0.5 is applied only to keep saved images readable: ```bash bash tools/dist_test.sh configs/sddf/sddf_yolov8_l_ovcod_d.py \ /path/to/checkpoint.pth 8 --show-num 12 \ --show-max-predictions 2 --show-nms-iou-thr 0.5 ``` Pass `--no-save-vis` to run evaluation without saving detection images. This release supports SDDF training and offline evaluation, including detection visualization. Reparameterized deployment and export are not provided. The sub-description bank is a runtime cache, not part of the checkpoint `state_dict`. `SubTextBankInjectHook` rebuilds it for training, validation, and standalone testing from `class_text_path`. A model used outside these runners must explicitly call `head_module.set_sub_text_bank(...)` after loading weights. The hook records a SHA-256 of the ordered descriptions and rebuilds the cache when descriptions, text-encoder weights, model, device, or dtype change. Use the same description file for reproducing a result. Keep its category-key order unchanged while bank-order canonicalization is pending. Run the smoke test: ```bash pip install -r requirements-dev.txt pytest -q ``` ## License and Third-Party Code SDDF is distributed as a derivative work under the GNU General Public License version 3. SDDF-authored source contributions are licensed under `GPL-3.0-only`. See [LICENSE](LICENSE) for the full terms and [third_party/THIRD_PARTY_NOTICES.md](https://github.com/Zh1fen/SDDF/blob/main/third_party/THIRD_PARTY_NOTICES.md) for code provenance, upstream copyrights, applicable third-party licenses, and modification notices. This implementation builds on [YOLO-World](https://github.com/AILab-CVC/YOLO-World), [DOSOD](https://github.com/D-Robotics-AI-Lab/DOSOD), and [MMYOLO](https://github.com/open-mmlab/mmyolo), whose repository licenses are GPLv3. OpenMMLab code with Apache-2.0 notices retains those terms; the corresponding license is included in [LICENSES/Apache-2.0.txt](https://github.com/Zh1fen/SDDF/blob/main/LICENSES/Apache-2.0.txt). Upstream copyright and license notices must be retained when redistributing code. The source-code license does not automatically license the OVCOD-D dataset, external pretrained checkpoints, downloaded model code, or the paper figures in `assets/`. The figures are included with the SDDF authors' permission for this repository's documentation; this does not grant a separate blanket license for reuse of the figures or their underlying images. Consult the respective rights holders and distribution terms for these non-code materials. External DOSOD checkpoints are linked to their official source, not redistributed in this repository. When creating a source archive that includes `third_party/`, also include its original copyright, LICENSE, and applicable NOTICE files, together with the root LICENSE, `LICENSES/`, and `third_party/THIRD_PARTY_NOTICES.md`. The bundled MMYOLO configuration snapshot includes `third_party/mmyolo/LICENSE`; another copy is retained at [LICENSES/MMYOLO-GPL-3.0.txt](https://github.com/Zh1fen/SDDF/blob/main/LICENSES/MMYOLO-GPL-3.0.txt). ## 📧 Contact If you have any questions, please feel free to contact us or open an issue. ## 📝 Citation If you find our work helpful for your research, please consider citing: ```bibtex @InProceedings{Liang_2026_CVPR, author = {Liang, Jiaming and Zhan, Yifeng and Liu, Chunlin and Zheng, Weihua and Peng, Bingye and Liang, Qiwei and Cai, Boyang and Mai, Xiaochun and Nie, Qiang}, title = {SDDF: Specificity-Driven Dynamic Focusing for Open-Vocabulary Camouflaged Object Detection}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2026}, pages = {13049-13058} } ```