Dataset Selection and Usage in Stage 2 Training

Oct 13, 2025 by thesweetdesigns.com 32 views

Thank you for sharing this paper and the associated codebase. We appreciate your interest in understanding the dataset selection and usage mentioned in the paper for Stage 2 training. In this article, we will delve into the specifics of the LLaVA 1.5 dataset and its usage in the Stage 2 training process.

Dataset Selection and Usage in Stage 2 Training

The paper states that 25% of the data from each of the four datasets - COCO-Panoptic, RefCOCO/+/g, COCO-Interactive, and LLaVA 1.5 - is used for Stage 2 training. However, the data in LLaVA 1.5 is more than that of the other three datasets (120000*3). This raises a question about how the data is selected from the LLaVA set.

Understanding the LLaVA 1.5 Dataset

LLaVA 1.5 is a large-scale dataset designed for visual question answering (VQA) tasks. It contains a vast amount of data, with 120,000 images and corresponding question-answer pairs. The dataset is divided into three subsets: train, validation, and test. Each subset contains a specific number of images and question-answer pairs.

Data Selection from LLaVA 1.5

Given the large size of the LLaVA 1.5 dataset, it is essential to understand how the data is selected for Stage 2 training. The paper mentions that 25% of the data from each dataset is used for Stage 2 training. However, it does not provide specific details on how the data is selected from the LLaVA set.

Review of the Train.py Code

Upon reviewing the train.py code, it appears that all data from LLaVA is being used for training. This raises concerns about the data selection process and whether it aligns with the paper's stated 25% data usage.

Clarification on Data Selection from LLaVA 1.5

To clarify the data selection process from LLaVA 1.5, we need to understand the specific implementation details in the codebase. The train.py code is responsible for loading the data and preparing it for training. However, without specific details on the data selection process, it is challenging to determine whether the code is adhering to the paper's stated 25% data usage.

Potential Issues with Data Selection

The potential issues with data selection from LLaVA 1.5 are:

Inconsistent data usage: If the code is using all data from LLaVA 1.5, it may not align with the paper's stated 25% data usage.
Data imbalance: The large size of the LLaVA 1.5 dataset may lead to data imbalance issues, where some classes or subsets have a disproportionate number of samples.
Overfitting: Using all data from LLaVA 1.5 may lead to overfitting, where the model becomes too specialized to the training data and fails to generalize well to new, unseen data.

Conclusion

In conclusion, the data selection process from LLaVA 1.5 is unclear, and further clarification is needed to ensure that the code is adhering to the paper's stated 25% data usage. The potential issues with data selection, such as inconsistent data usage, data imbalance, and overfitting, highlight the importance of careful data selection and preprocessing in machine learning tasks.

Recommendations

To address the concerns raised in this article, we recommend the following:

Provide specific details on data selection: The authors should provide specific details on how the data is selected from the LLaVA set, including the implementation details in the codebase.
Verify data usage: The authors should verify that the code is adhering to the paper's stated 25% data usage and address any inconsistencies.
Address data imbalance and overfitting: The authors should address the potential issues with data imbalance and overfitting by implementing techniques such as data augmentation, regularization, and early stopping.

In our previous article, we discussed the question about the LLaVA data used in Stage 2 and highlighted the importance of understanding the dataset selection and usage process. In this Q&A article, we will address some of the most frequently asked questions related to the LLaVA data used in Stage 2.

Q: What is the LLaVA 1.5 dataset, and why is it used in Stage 2 training?

A: LLaVA 1.5 is a large-scale dataset designed for visual question answering (VQA) tasks. It contains a vast amount of data, with 120,000 images and corresponding question-answer pairs. The dataset is divided into three subsets: train, validation, and test. Each subset contains a specific number of images and question-answer pairs. The LLaVA 1.5 dataset is used in Stage 2 training because it provides a large and diverse set of data that can help improve the model's performance on VQA tasks.

Q: How is the data selected from the LLaVA set, and what percentage of the data is used?

A: The paper states that 25% of the data from each of the four datasets - COCO-Panoptic, RefCOCO/+/g, COCO-Interactive, and LLaVA 1.5 - is used for Stage 2 training. However, the data in LLaVA 1.5 is more than that of the other three datasets (120000*3). The exact method of data selection from the LLaVA set is not specified in the paper, and further clarification is needed to ensure that the code is adhering to the paper's stated 25% data usage.

Q: Why is all data from LLaVA being used for training, and what are the potential issues with this approach?

A: The train.py code appears to be using all data from LLaVA for training, which may not align with the paper's stated 25% data usage. This approach may lead to potential issues such as:

Inconsistent data usage: If the code is using all data from LLaVA, it may not align with the paper's stated 25% data usage.
Data imbalance: The large size of the LLaVA dataset may lead to data imbalance issues, where some classes or subsets have a disproportionate number of samples.
Overfitting: Using all data from LLaVA may lead to overfitting, where the model becomes too specialized to the training data and fails to generalize well to new, unseen data.

Q: How can the authors address the potential issues with data selection and usage?

A: To address the concerns raised in this article, the authors should:

Provide specific details on data selection: The authors should provide specific details on how the data is selected from the LLaVA set, including the implementation details in the codebase.
Verify data usage: The authors should verify that the code is adhering to the paper's stated 25% data usage and address any inconsistencies.
Address data imbalance and overfitting: The authors should address the potential issues with data imbalance and overfitting by implementing techniques such as data augmentation, regularization, and early stopping.

Q: What are the benefits of using the LLaVA 1.5 dataset in Stage 2 training?

A: The benefits of using the LLaVA 1.5 dataset in Stage 2 training include:

Improved model performance: The large and diverse set of data in the LLaVA 1.5 dataset can help improve the model's performance on VQA tasks.
Better generalization: The LLaVA 1.5 dataset can help the model generalize better to new, unseen data.
Increased robustness: The LLaVA 1.5 dataset can help the model become more robust to different types of data and tasks.

Conclusion

In conclusion, the LLaVA data used in Stage 2 is a critical component of the model's performance. However, the data selection and usage process is unclear, and further clarification is needed to ensure that the code is adhering to the paper's stated 25% data usage. By addressing the potential issues with data selection and usage, the authors can ensure that the model is trained on a diverse and representative set of data, leading to better results in the Stage 2 training process.