open-compass/VLMEvalKit
3,824 stars·638 forks·Python·apache-2.0·14 viewshuggingface.co/spaces/opencompass/open_vlm_leaderboard↗
VLMEvalKit
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.
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.
Star history
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