A complete solution for dataset quality assurance
Data consistency is a key factor in the effectiveness of VLM-R1 and the following quality control process is recommended:
- pretreatment stage::
- Check all images for readability using opencv's imread
- Validating annotation file formats with json_validator
- Run the dataset_verifier.py script provided by the project to check the image-annotation correspondence.
- Recommendations for labeling specifications::
- Maintains the same subject-property-position ternary structure as RefCOCO
- Use consistent-id labeling strategy for fuzzy targets
- Contains samples of the same object from at least 3 different viewpoints
- Validation during training::
- Set -validation_steps=100 in grpo_rec.py
- Enable -skip_broken_data to automatically filter anomaly samples
- Monitor abnormal fluctuations in the loss curve
Special note: Saving images on an SSD instead of an HDD significantly reduces the probability of loading errors, and avoiding Chinese and special characters in the path.
This answer comes from the articleVLM-R1: A Visual Language Model for Localizing Image Targets through Natural LanguageThe
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