VLMEvalKit is a vision-language model evaluation framework and inference engine designed to run standardized benchmarks and measure model accuracy across diverse visual datasets. It serves as a multimodal model benchmark and performance toolkit for calculating metrics and comparing model responses.
The toolkit includes a specialized visual reasoning evaluator that uses adversarial samples to distinguish actual image understanding from reliance on language patterns. It also provides capabilities for image generation evaluation, testing a model's ability to create or modify visuals based on text descriptions.
The framework covers multimodal inference execution and image-to-text generation, supported by batch inference execution to increase throughput. It provides utilities for benchmark score calculation, a model response browser for reviewing raw outputs, and attention mechanism optimization to reduce memory usage during inference.
