Code release of paper:

ST-SAM: SAM-Driven Self-Training Framework for Semi-Supervised Camouflaged Object Detection

[Xihang Hu], [Fuming Sun], [Jiazhe Liu], [Feilong Xu], [Xiaoli Zhang*]

Semi-supervised Camouflaged Object Detection (SSCOD) aims to reduce reliance on costly pixel-level annotations by leveraging limited annotated data and abundant unlabeled data. However, existing SSCOD methods based on Teacher-Student frameworks suffer from severe prediction bias and error propagation under scarce supervision, while their multi-network architectures incur high computational overhead and limited scalability. To overcome these limitations, we propose ST-SAM, a highly annotation-efficient yet concise framework that breaks away from conventional SSCOD constraints. Specifically, ST-SAM employs Self-Training strategy that dynamically filters and expands high-confidence pseudo-labels to enhance a single-model architecture, thereby fundamentally circumventing inter-model prediction bias. Furthermore, by transforming pseudo-labels into hybrid prompts containing domain-specific knowledge, ST-SAM effectively harnesses the Segment Anything Model's potential for specialized tasks to mitigate error accumulation in self-training. Experiments on COD benchmark datasets demonstrate that ST-SAM achieves state-of-the-art performance with only 1% labeled data, outperforming existing SSCOD methods and even matching fully supervised methods. Remarkably, ST-SAM requires training only a single network, without relying on specific models or loss functions. This work establishes a new paradigm for annotation-efficient SSCOD.

• Python 3.9
• Pytorch 2.5.1
• Torchvision 0.8.2
• Numpy 1.26.3

• Training and Test Sets.

You can download the tested result maps at - [Baidu Pan link] (https://pan.baidu.com/s/1vCTX5Qem3NGD0tiKDIcCnw?pwd=2025) with the fetch code:2025.

You can download the parameter pth at - [Baidu Pan link] (https://pan.baidu.com/s/1i8eiS_cmiEQvwYoquwfnyw?pwd=2025) with the fetch code:2025.

• Due to a mistake in the codes, an unused backbone was declared, resulting in the doubled size of the saved pth. However, this does not affect the final result.

Thanks to the project of (https://github.com/jiwei0921/Saliency-Evaluation-Toolbox)

Feel free to send e-mails to me (1558239392@qq.com).

The codes and datasets are based on Noisy-COD and SAM. Please also follow their licenses. Thanks for the awesome works.