open-compass/VLMEvalKit

Features

• Multimodal Inference Engines - Provides an execution engine designed to process combined text and visual data to generate single multimodal model responses.
• Multimodal Benchmarks - Provides a comprehensive framework for evaluating and comparing the performance of multimodal models.
• Adversarial Reasoning Testing - Uses adversarial samples to ensure model image understanding is not relying on language patterns.
• Automated Dataset Evaluation - Automates the execution of evaluators against structured benchmark datasets to measure visual reasoning.
• Image-Text Prompt Inferences - Processes large sets of combined image and text prompts to generate predictions for evaluation.
• Image-to-Text Transformers - Enables the analysis of images and text prompts to generate descriptive written responses or direct answers.
• Visual Reasoning Evaluators - Measures actual image understanding versus language patterns using a specialized adversarial testing suite.
• Model Evaluation Frameworks - Provides a complete toolkit for running standardized benchmarks and measuring VLM accuracy.
• Accuracy Calculators - Provides utilities to calculate accuracy and performance metrics by comparing model predictions against ground-truth labels.
• Visual Question Answering Evaluation - Analyzes visual content to answer natural language questions and extract meaning from images.
• Adversarial Visual Reasoning Evaluation - Tests a model's ability to answer questions using adversarial samples that prevent reliance on language patterns.
• Multimodal Evaluation Benchmarks - Calculates performance metrics and compares model responses using diverse image-text evaluation sets.
• Vision-Language Model Benchmarking - Measures vision-language model performance using standardized benchmarks and accuracy metrics.
• Attention Kernel Optimizers - Includes optimizations for attention mechanisms to reduce memory overhead and increase computation speed during inference.
• Batch Inference Engines - Implements batch processing of multimodal inputs to maximize hardware throughput during large-scale model evaluation.
• Inference-Scoring Decoupling - Separates model prediction generation from metric calculation to allow for flexible post-processing and re-scoring.
• Evaluations - Testing the ability of a model to create or modify images accurately based on provided text descriptions.
• Generative Model Evaluation - Assesses how accurately a model creates or modifies visuals based on text descriptions.
• Multimodal Performance Toolkits - Optimizes attention speed and provides tools for analyzing raw model outputs during large-scale testing.
• Prediction Persistence Layers - Saves raw model predictions to structured local files for offline analysis and auditing.
• Prompt Template Injection - Dynamically injects dataset questions into model-specific templates to ensure consistent input formatting.
• Inference Batching - Implements inference batching by grouping image and text pairs into tensors to maximize hardware throughput.
• Unified Model Wrappers - Provides a unified interface to standardize diverse vision-language model APIs for consistent benchmarking.
• Model Evaluation - Evaluation toolkit specifically for vision-language models.
• Model Evaluation and Benchmarking - Evaluation toolkit for large vision-language models.

Istoric stele