Visual Question Answering
Transformers
Safetensors
English
idefics2
text-classification
text-generation-inference
Instructions to use TIGER-Lab/VideoScore with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TIGER-Lab/VideoScore with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("visual-question-answering", model="TIGER-Lab/VideoScore")# Load model directly from transformers import AutoProcessor, AutoModelForSequenceClassification processor = AutoProcessor.from_pretrained("TIGER-Lab/VideoScore") model = AutoModelForSequenceClassification.from_pretrained("TIGER-Lab/VideoScore") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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@@ -36,7 +36,7 @@ averaged among all the evaluation aspects as indicator.
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For GenAI-Bench and VBench, which include human preference data among two or more videos,
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we employ the model's output to predict preferences and use pairwise accuracy as the performance indicator.
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| metric | Final Sum Score | VideoEval-test | EvalCrafter | GenAI-Bench | VBench |
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| MantisScore (reg) | 278.3 | 75.7 | 51.1 | 78.5 | 73.0 |
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| MantisScore (gen) | 222.4 | 77.1 | 27.6 | 59.0 | 58.7 |
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| Gemini-1.5-Pro | 158.8 | 22.1 | 22.9 | 60.9 | 52.9 |
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For GenAI-Bench and VBench, which include human preference data among two or more videos,
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we employ the model's output to predict preferences and use pairwise accuracy as the performance indicator.
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| metric | Final Sum Score | VideoEval-test | EvalCrafter | GenAI-Bench | VBench |
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| MantisScore (reg) | 278.3 | 75.7 | 51.1 | 78.5 | 73.0 |
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| MantisScore (gen) | 222.4 | 77.1 | 27.6 | 59.0 | 58.7 |
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| Gemini-1.5-Pro | 158.8 | 22.1 | 22.9 | 60.9 | 52.9 |
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