Auto-labelling 1.2M robotics frames with VLMs: a failover story

1. 1Load 1.2M reconstructed event-camera frames into a dataset
2. 2Preprocess frames using data augmentation techniques to enhance quality
3. 3Fine-tune a pre-trained VLM on the dataset to generate captions
4. 4Evaluate the performance of the VLM using metrics such as accuracy and F1-score
5. 5Implement a failover strategy to handle errors and inconsistencies in the auto-labelling process

Computer vision engineers and robotics researchers can benefit from this approach to automate labeling tasks and improve model accuracy

💡 Vision-Language Models can be used to automate labeling tasks for large robotics datasets, but require careful fine-tuning and error handling

Auto-label 1.2M robotics frames with VLMs! Learn how to overcome challenges and achieve accurate results #VLM #Robotics #ComputerVision