Lightweight Multimodal Models for Edge-Based Defect Detection
X-Lab
3 minutes
Sep 23, 2024
In industrial production, ensuring high product quality is critical to maintaining competitiveness and minimizing costs associated with defects. Traditional methods for defect detection often rely on manual inspection or rule-based algorithms, which can be time-consuming, inconsistent, and less effective with complex or subtle defects.
Recently, multimodal models like ChatGPT4o [2] and BLIP2 [1]—with GPT-3-large functioning as the language model component, have proven highly effective in Visual Question Answering (VQA) tasks for detecting defects. These models achieve expert-level accuracy and are capable of sophisticated interpretations, even without prior training specific to the tasks (zero-shot settings). However, their high computational requirements and significant resource demands make them less suitable for use on edge devices, which have limited processing power and energy availability. For example, Figure 1 shows a defective bottle from the public MVTEC dataset.
Figure 1 A broken bottle and VQA task were answered by 3 multi-modality models
Scenario Assumptions
Q: You are a product quality engineer, justify the quality of the product and provide the reason.
In the case of BLIP2, the model achieves expert-level performance in VQA tasks for defect detection when using GPT-3-large as the LLM part. However, when the LLM part of BLIP2 is replaced with a more resource-efficient version, such as GPT-2-large 774M, its performance in defect detection tasks significantly declines. The decline in performance of smaller models is due to their limited capacity to capture complex patterns and nuances in data, as well as reduced generalization ability from a smaller pre-training corpus.
This discussion aims to explore strategies such as model pruning, domain-specific supervised fine-tuning (SFT), and knowledge distillation to retain performance while enabling edge deployment.
This discussion aims to explore strategies such as model pruning, domain-specific supervised fine-tuning (SFT), and knowledge distillation to retain performance while enabling edge deployment.
Potential Solutions
Model Pruning
Model pruning involves reducing the size of a large multimodal model by removing unnecessary parameters or components while retaining those critical for task performance. This process can significantly decrease the computational resources required, making the model suitable for edge devices. Various pruning techniques, like structured pruning (removing entire neurons or filters) and unstructured pruning (removing individual weights), can be explored to determine the most effective strategy for maintaining defect detection accuracy while optimizing model size.
Domain-Specific Supervised Fine-Tuning (SFT)
Fine-tuning a pre-trained model on domain-specific data can enhance its ability to detect defects unique to a particular industrial setting. This approach allows a smaller model to leverage the extensive knowledge gained from pre-training on large datasets and adapt it to specific types of defects encountered in production. Additionally, combining real-world data with synthetic data generated through augmentation techniques can further enhance the model's robustness and generalization capabilities.
Knowledge Distillation
Knowledge distillation [3][4] is a technique where a smaller model (the student) learns to replicate the behavior of a larger, more complex model (the teacher). In this context, a large model like BLIP2 with GPT-3-large can be fine-tuned for defect detection tasks, and then distilled knowledge is transferred to a smaller model. This process involves training the student model to mimic the output or internal representations of the teacher model, thereby retaining much of the original model's accuracy and capability while reducing its size and computational requirements.
Discussion
These strategies provide a roadmap for developing lightweight multimodal models capable of effective defect detection at the edge. By combining model pruning, domain-specific supervised fine-tuning, and knowledge distillation, it is possible to create models that maintain high-performance levels despite their smaller size. This approach can help overcome the limitations imposed by resource constraints on edge devices, making advanced AI-driven defect detection feasible in real-world industrial environments.
References
[1] Li J, Li D, Savarese S, et al. Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models[C]//International conference on machine learning. PMLR, 2023: 19730-19742.
[2] Brown T B. Language models are few-shot learners[J]. arXiv preprint arXiv: 2005.14165, 2020.
[3] Hinton G. Distilling the Knowledge in a Neural Network[J]. arXiv preprint arXiv:1503.02531, 2015.
[4] Gou J, Yu B, Maybank S J, et al. Knowledge distillation: A survey[J]. International Journal of Computer Vision, 2021, 129(6): 1789-1819.
