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  ## Performance
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  This model has reached 75.12\%(*12\% better than previous version*)/74.98\%(*8.5\% better than previous version*) on Q-Bench A1 *dev/test* (multi-choice questions).
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  It also outperforms the following close-source models with much larger model capacities:
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  | Model | *dev* | *test* |
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  | ---- | ---- | ---- |
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- | Co-Instruct-Preview (mPLUG-Owl2) | **75.12\%** | **74.98\%** |
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  | \*GPT-4V-Turbo | 74.41\% | 74.10\% |
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  | \*Qwen-VL-**Max** | 73.63\% | 73.90\% |
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  | \*GPT-4V (Nov. 2023) | 71.78\% | 73.44\% |
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  \*: Proprietary Models.
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  We are also constructing multi-image benchmark sets (image pairs, triple-quadruple images), and the results on multi-image benchmarks will be released soon!
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  ## Load Model
 
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  ## Performance
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+ ### Low-level Question-Answering
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  This model has reached 75.12\%(*12\% better than previous version*)/74.98\%(*8.5\% better than previous version*) on Q-Bench A1 *dev/test* (multi-choice questions).
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  It also outperforms the following close-source models with much larger model capacities:
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  | Model | *dev* | *test* |
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  | ---- | ---- | ---- |
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+ | **Co-Instruct-Preview (mPLUG-Owl2) (This Model)** | **75.12\%** | **74.98\%** |
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  | \*GPT-4V-Turbo | 74.41\% | 74.10\% |
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  | \*Qwen-VL-**Max** | 73.63\% | 73.90\% |
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  | \*GPT-4V (Nov. 2023) | 71.78\% | 73.44\% |
 
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  \*: Proprietary Models.
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+ #### Image/Video Quality Assessment
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+
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+ | Model | live | agi | livec | test_spaq | csiq | test_kadid | test_koniq | konvid | maxwell_test |
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+ |--------------------------|--------------|--------------|-------------|-------------|-------------|-------------|-------------|-------------|--------------|
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+ |**Co-Instruct-Preview (mPLUG-Owl2) (This Model)** | **0.771/0.751** | **0.727/0.749** | **0.861/0.865** | **0.946/0.938** | **0.735/0.748** | **0.782/0.770** | **0.908/0.941** | **0.818/0.790** | **0.735/0.714** |
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+ | Q-Instruct (mPLUG-Owl2, Nov. 2023) | 0.749/0.747 | 0.710/0.753 | 0.781/0.791 | 0.921/0.917 | 0.693/0.723 | 0.670/0.665 | 0.904/0.921 | 0.766/0.738 | 0.650/0.649 |
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  We are also constructing multi-image benchmark sets (image pairs, triple-quadruple images), and the results on multi-image benchmarks will be released soon!
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  ## Load Model