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README.md
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Our dataset is built upon the Uground, Jedi, and additional public paper-style academic document screenshots.
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**NOTE**: Before you use this dataset, make sure you understand the logic of absolute coordinates and [image processor](https://github.com/QwenLM/Qwen2.5-VL/blob/d2240f11656bfe404b9ba56db4e51cd09f522ff1/qwen-vl-utils/src/qwen_vl_utils/vision_process.py#L60) for [Qwen2.5-VL](https://arxiv.org/abs/2502.13923).
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This dataset is set with the image processor max tokens to be 2700, a.k.a max_pixels=2700x14x14x2x2 , the coordinates were resized to be smaller and you have to resize the image as well within max_pixels=2700x14x14x2x2 via image processor to make them align.
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Make sure you also follow it in your training procedure, otherwise the performance will not be as expected.
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Our dataset is built upon the Uground, Jedi, and additional public paper-style academic document screenshots.
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Project webpage: https://osu-nlp-group.github.io/GUI-Drag
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**NOTE**: Before you use this dataset, make sure you understand the logic of absolute coordinates and [image processor](https://github.com/QwenLM/Qwen2.5-VL/blob/d2240f11656bfe404b9ba56db4e51cd09f522ff1/qwen-vl-utils/src/qwen_vl_utils/vision_process.py#L60) for [Qwen2.5-VL](https://arxiv.org/abs/2502.13923).
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This dataset is set with the image processor max tokens to be 2700, a.k.a max_pixels=2700x14x14x2x2 , the coordinates were resized to be smaller and you have to resize the image as well within max_pixels=2700x14x14x2x2 via image processor to make them align.
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Make sure you also follow it in your training procedure, otherwise the performance will not be as expected.
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