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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| Idefics2 | 73.0 | 6.5 | 0.3 | 34.6 | 31.7 |
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| SSIM-dyn | 42.5 | -5.5 | -17.0 | 28.4 | 36.5 |
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| MES-dyn | 36.7 | -12.9 | -26.4 | 31.4 | 44.5 |
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## Usage
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### Installation
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| Idefics2 | 73.0 | 6.5 | 0.3 | 34.6 | 31.7 |
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| SSIM-dyn | 42.5 | -5.5 | -17.0 | 28.4 | 36.5 |
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| MES-dyn | 36.7 | -12.9 | -26.4 | 31.4 | 44.5 |
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| Kosmos-2 | - | - | - | - | - |
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The best in MantisScore series is in bold and the best in baselines is underlined.
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"-" means the answer of MLLM is meaningless or in wrong format.
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## Usage
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### Installation
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