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1.实现了bert_generation模型结合clip模型,在coco2017数据集上的ZOC任务全参微调,任务链接:https://gitee.com/mindspore/community/issues/IAUP32
由于原baseline中没有loss的说明,这里通过精度评估的参数来对比
2.pytorch任务结果对比:

以上图片是baseline仓库中的原论文中的图片
3.在mindnlp微调后结果:



cifar10:
cifar100:
tin'yimagenet:
可见,mindnlp微调后,模型与任务水准持平或略高于pytorch结果