Introduction

ImagenHub is a centralized framework to standardize the evaluation of conditional image generation models by curating unified datasets, building an inference library and a benchmark that align with real life applications. This is continuous effort to publish leaderboard to help everyone track the progress in the field.

Why ImagenHub?

What sets #ImagenHub apart?

  1. Unified Datasets: We’ve meticulously curated evaluation datasets for 7 critical tasks in image generation research. This ensures comprehensive testing of models across diverse scenarios.

  2. Inference Library: Say goodbye to inconsistent comparisons. Our unified inference pipeline ensures that every model is evaluated on a level playing field with full transparency.

  3. Human-centric Evaluation: Beyond traditional metrics, we’ve innovated with human evaluation scores that measure Semantic Consistency & Perceptual Quality. This aligns evaluations closer to human perceptions, while better than existing Human-preference evaluation methods.

Why should you use #ImagenHub?

  1. Streamlined Research: We’ve taken the guesswork out of research by defining clear tasks and providing curated datasets. Objective Evaluation: Our framework ensures a bias-free, standardized evaluation, giving a true measure of a model’s capabilities.

  2. Experiment Transparency: By standardizing the human-evaluation dataset, human evaluation results would be come far more convincing with the experiment transparency.

  3. Collaborative Spirit: We believe in the power of community. Our platform is designed to foster collaboration, idea exchange, and innovation in the realm of image generation.

  4. Comprehensive Functionality: From common GenAI metrics to visualization tools, we’ve got you covered. Also stay tuned for our upcoming Amazon Mechanical Turk Templates!

  5. Engineering Excellence: We emphasize good engineering practice. Documentations, type hints, and (coming soon!) extensive code coverage.