用预训练模型零样本检测细菌菌落，无需标注数据即可准确分割。
Colony Grounded SAM2: Zero-shot detection and segmentation of bacterial colonies using foundation models
- 基于微调的Grounding DINO和SAM2模型，实现零样本推理
- 在分布外数据上达到93.1%的平均精度和0.85的Dice分数
- 适合微生物学图像标注与分类，开源可用
在琼脂平板图像中检测与分类细菌菌落对微生物学至关重要，但受限于标注数据集的缺乏。为此，我们提出Colony Grounded SAM2，一种无需额外训练即可在多种场景下进行零样本检测与分割的推理流程。通过将预训练的基础模型Grounding DINO和Segment Anything Model 2在微生物领域进行微调，构建出对数据变化具有鲁棒性的模型。结果表明，在分布外数据上平均精度达93.1%，Dice@detection得分为0.85，展现出优异的检测与分割能力。整个流程及模型权重均开源共享，以支持微生物学中的标注与分类任务。
原文摘要 · Abstract (English)
The detection and classification of bacterial colonies in images of agar-plates is important in microbiology, but is hindered by the lack of labeled datasets. Therefore, we propose Colony Grounded SAM2, a zero-shot inference pipeline to detect and segment bacterial colonies in multiple settings without any further training. By utilizing the pre-trained foundation models Grounding DINO and Segment Anything Model 2, fine-tuned to the microbiological domain, we developed a model that is robust to data changes. Results showed a mean Average Precision of 93.1\% and a $Dice@detection$ score of 0.85, showing excellent detection and segmentation capabilities on out-of-distribution datasets. The entire pipeline with model weights are shared open access to aid with annotation- and classification purposes in microbiology.
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