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license: apache-2.0
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# EvalAlign Model Card
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EVALALIGN, a metric characterized by its accuracy, stability, and fine granularity.
## Model Details
### Model Description
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The recent advancements in text-to-image generative models have been remarkable.Yet, the field suffers from a lack of evaluation metrics that accurately reflect the
performance of these models, particularly lacking fine-grained metrics that can guide the optimization of the models. In this paper, we propose EVALALIGN, a
metric characterized by its accuracy, stability, and fine granularity. Our approach leverages the capabilities of Multimodal Large Language Models (MLLMs) pre-
trained on extensive datasets. We develop evaluation protocols that focus on two key dimensions: image faithfulness and text-image alignment. Each protocol
comprises a set of detailed, fine-grained instructions linked to specific scoring options, enabling precise manual scoring of the generated images. We Supervised
Fine-Tune (SFT) the MLLM to align closely with human evaluative judgments, resulting in a robust evaluation model. Our comprehensive tests across 24 text-to-
image generation models demonstrate that EVALALIGN not only provides superior metric stability but also aligns more closely with human preferences than existing
metrics, confirming its effectiveness and utility in model assessment.
- **Model Release Date:** [June 2024]
### Model Sources [optional]
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- **Repository:** [https://github.com/SAIS-FUXI/EvalAlign]
- **Paper [optional]:**
## How to Get Started with the Model
Refer to our GitHub.https://github.com/SAIS-FUXI/EvalAlign
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