Real-World Computer Vision Pitfalls: Hallucinations to Hardware
▼ Summary
– The project aimed to build a model identifying laptop damage from photos but encountered issues like hallucinations and unreliable outputs.
– Initial monolithic prompting failed due to hallucinations, junk image detection problems, and inconsistent accuracy.
– A multimodal approach combining image captioning and text-only LLMs was tried but introduced new issues like persistent hallucinations.
– An agentic framework improved results by using specialized agents for different components and junk detection, reducing hallucinations.
– A hybrid solution combining agentic and monolithic approaches, plus fine-tuning, achieved reliable performance by balancing precision and coverage.
Computer vision projects often encounter unexpected challenges, and our experience building a laptop damage detection system proved no different. What began as a straightforward application of image models and large language models (LLMs) quickly revealed complexities, from false positives to irrelevant image inputs. The journey led us to rethink traditional approaches and ultimately develop a hybrid solution combining multiple AI techniques.
Our initial strategy relied on monolithic prompting, feeding images directly into a multimodal LLM with a single instruction to identify damage. While simple in theory, real-world data exposed critical flaws:
Hallucinations: The model frequently invented nonexistent damage or misclassified components.
Resolution experiments taught us valuable lessons. First, we addressed image quality by training the model on mixed-resolution datasets, improving resilience against blurry or low-quality uploads. However, hallucinations and junk detection remained problematic.
Next, we explored a multimodal detour, generating image captions through an iterative process where an LLM refined descriptions based on similarity scores from an embedding model like SigLIP. While innovative, this introduced new issues:
Captions still hallucinated damage details. The breakthrough came from repurposing agentic frameworks, typically used for workflow automation. By decomposing the task into specialized agents, we achieved sharper results:
An orchestrator agent identified visible laptop components (screens, keyboards, etc.). This modular approach slashed hallucinations and improved transparency, though trade-offs emerged: Latency increased due to sequential agent processing.
The final hybrid system merged strengths from multiple methods:
- Agentic framework for precise, explainable detection of known issues.
- Monolithic LLM prompt to catch anomalies agents might miss.
- Targeted fine-tuning on high-priority damage scenarios for added reliability.
Key takeaways from the project
Agentic frameworks excel beyond workflows: their structured, modular design can enhance model accuracy when adapted creatively. What seemed like a simple computer vision task evolved into a testament to adaptive problem-solving.
By reimagining tools like agentic frameworks for unconventional roles, we built a solution that balanced precision, coverage, and scalability, proving that innovation often lies at the intersection of existing technologies.
(Source: VentureBeat)
