【开源实习】GIT模型应用开发&&LayoutLMv3模型应用开发 #2027
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任务任务编号：#IAADHU |
任务任务编号：#IBLNVF |
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验收通过，可以合入 |
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第一个自定义数据集上微调GIT模型进行图像描述的笔记本中，我将在一个小型图像描述数据集上微调
这是在Mindspore的训练过程图，50个epoch后loss稳定在0.06左右
这是在pytorch的训练过程图，50个epoch后loss稳定在0.09左右
下面是mindspore的推理图，可以看到推理结果与预期一致
下面是pytorch的推理图，可以看到准确度相较于mindspore差一点
第二个使用 GIT 进行图像/视频描述生成和图像/视频问答的笔记本中，演示了如何使用MindSpore的 GIT 模型来对图像或视频进行描述生成，以及在图像或视频上进行问答。
选择其中一个推理模型进行比较：
mindspore：
pytorch:
结果一致