The performance on more tasks about delvm HOT 2 OPEN

Thank you for your interest in our work. 🙏

Indeed, we did not use data from other tasks for training, so our model currently does not have the capability for zero-shot learning, such as depth estimation. Even with in-context prompts, our model would not be able to perform such tasks as they are not included in our training data.

We open-sourced our data processing code, which you can refer to for generating data suitable for internLM, potentially applicable to tasks like depth estimation. However, it's important to note that based on our experience, if the training data is too limited and lacks sufficient data augmentation, the model's performance can be significantly affected.

from delvm.

Thank you for your interest in our work. 🙏

Indeed, we did not use data from other tasks for training, so our model currently does not have the capability for zero-shot learning, such as depth estimation. Even with in-context prompts, our model would not be able to perform such tasks as they are not included in our training data.

We open-sourced our data processing code, which you can refer to for generating data suitable for internLM, potentially applicable to tasks like depth estimation. However, it's important to note that based on our experience, if the training data is too limited and lacks sufficient data augmentation, the model's performance can be significantly affected.

I see in LVM there are some examples/tasks are not included in their training sets but still can work well. Can you briefly explain why your model can not do this? thx.

from delvm.