The Hong Kong Research Institute of Textiles and Apparel(hereinafter referred to as"HKRITA")and Seiko Epson Corporation(hereinafter referred to as"Epson")have succeeded in developing new regenerate...The Hong Kong Research Institute of Textiles and Apparel(hereinafter referred to as"HKRITA")and Seiko Epson Corporation(hereinafter referred to as"Epson")have succeeded in developing new regenerated cellulose fiber with a silk-like sheen from waste cotton fabric through a new production process.A movement toward using fibers recycled from discarded clothing has been gaining momentum,driven by a growing recognition of waste clothing as a global environmental issue.In Europe in particular,a move is under way to use more regenerated fiber and reduce textile waste.展开更多
针对服装风格人工分类受主观性、地域等因素影响而造成的分类错误问题,研究了一种基于人工智能的服装风格图像分类方法。首先,在FashionStyle14数据集基础上筛除重复或无效图像,构建服装风格图像数据集;然后,采用迁移学习方法,对Efficie...针对服装风格人工分类受主观性、地域等因素影响而造成的分类错误问题,研究了一种基于人工智能的服装风格图像分类方法。首先,在FashionStyle14数据集基础上筛除重复或无效图像,构建服装风格图像数据集;然后,采用迁移学习方法,对EfficientNet V2、RegNet Y 16GF和ViT Large 16等模型进行微调训练,生成新模型,实现基于单个深度学习的服装风格图像分类;最后,为进一步提高图像分类的准确性、可靠性和鲁棒性,分别采用基于投票、加权平均和堆叠的集成学习方法对上述单个模型进行组合预测。迁移学习实验结果表明,基于ViT Large 16的深度学习模型在测试集上表现最佳,平均准确率为77.024%;集成学习方法实验结果显示,基于投票的集成学习方法在相同测试集上平均准确率可达78.833%。研究结果为解决服装风格分类问题提供了新的思路。展开更多
文摘The Hong Kong Research Institute of Textiles and Apparel(hereinafter referred to as"HKRITA")and Seiko Epson Corporation(hereinafter referred to as"Epson")have succeeded in developing new regenerated cellulose fiber with a silk-like sheen from waste cotton fabric through a new production process.A movement toward using fibers recycled from discarded clothing has been gaining momentum,driven by a growing recognition of waste clothing as a global environmental issue.In Europe in particular,a move is under way to use more regenerated fiber and reduce textile waste.
文摘针对服装风格人工分类受主观性、地域等因素影响而造成的分类错误问题,研究了一种基于人工智能的服装风格图像分类方法。首先,在FashionStyle14数据集基础上筛除重复或无效图像,构建服装风格图像数据集;然后,采用迁移学习方法,对EfficientNet V2、RegNet Y 16GF和ViT Large 16等模型进行微调训练,生成新模型,实现基于单个深度学习的服装风格图像分类;最后,为进一步提高图像分类的准确性、可靠性和鲁棒性,分别采用基于投票、加权平均和堆叠的集成学习方法对上述单个模型进行组合预测。迁移学习实验结果表明,基于ViT Large 16的深度学习模型在测试集上表现最佳,平均准确率为77.024%;集成学习方法实验结果显示,基于投票的集成学习方法在相同测试集上平均准确率可达78.833%。研究结果为解决服装风格分类问题提供了新的思路。
