Model details
模型介绍 中文整理 SAM-HQ (Segment Anything in High Quality)
*Segment Anything in High Quality** Lei Ke, Mingqiao Ye, Martin Danelljan, Yifan Liu, Yu-Wing Tai, Chi-Keung Tang, Fisher Yu \
ETH Zurich & HKUST
我们提出HQ-SAM对SAM进行升级，实现高质量的零弹分割。详情请参阅我们的论文。
SAM and HQ-SAM两者效果比较
SAM vs. HQ-SAM**
介绍
最近的细分模型(SAM)代表了一个巨大的飞跃，在扩大细分模型，允许强大的零射击能力和灵活的提示。尽管使用了11亿个掩模进行训练，但SAM的掩模预测质量在许多情况下都存在不足，特别是在处理结构复杂的物体时。我们提出红旗-地对空导弹，使地对空导弹具备准确分割任何目标的能力，同时保持地对空导弹原有的提示设计、效率和零弹通用性。我们的精心设计重用并保留了SAM的预训练模型权值。
SAM**与**HQ-SAM**的定量比较
注:对于基于框提示的评估，我们给SAM、MobileSAM和我们的HQ-SAM提供相同的图像/视频边界框，并采用SAM的单掩模输出模式。
我们提供全面的性能，模型尺寸和速度对SAM变体的比较:
COCO上的各种ViT主干:
注意:对于COCO数据集，我们使用在COCO数据集上训练的SOTA检测器FocalNet-DINO作为我们的框提示生成器。
YTVIS and HQ-YTVIS
注:使用vitl骨干网。我们采用在YouTubeVIS 2019数据集上训练的SOTA检测器Mask2Former作为我们的视频框提示生成器，同时重用其对象关联预测。
DAVIS
注:使用vitl骨干网。我们采用SOTA模型XMem作为视频框提示生成器，同时重用其对象关联预测。
使用不同的点进行交互式分割比较
注:使用vitl骨干网。对高品质的COIFT(零射击)和DIS val设置。
Quote
如果您发现HQ-SAM在您的研究中很有用，或者参考了提供的基线结果，请记得标记
优先采用原作者提供的中文模型卡片；仅有英文说明时由闲社AI翻译整理。重要参数、许可证和商用范围请以原文为准。
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