Waste-Bench: A Comprehensive Benchmark for Evaluating VLLMs in Cluttered Environments

摘要

Recent advancements in Large Language Models (LLMs) have paved the way for VisionLarge Language Models (VLLMs) capable ofperforming a wide range of visual understand-ing tasks. While LLMs have demonstrated impressive performance on standard naturalimages, their capabilities have not been thoroughly explored in cluttered datasets where there is complex environment having deformedshaped objects. In this work, we introduce a novel dataset specifically designed for waste classification in real-world scenarios, character-ized by complex environments and deformed shaped objects. Along with this dataset, we present an in-depth evaluation approach to rig-orously assess the robustness and accuracy of VLLMs. The introduced dataset and comprehensive analysis provide valuable insights intothe performance of VLLMs under challenging conditions. Our findings highlight the critical need for further advancements in VLLM’s ro-bustness to perform better in complex enviroments. The dataset and code for our experiments are available at https://github.com/aliman80/wastebench.