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README.md
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# VisualOverload
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<p align="center">
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<img src="https://github.com/paulgavrikov/visualoverload/blob/main/assets/logo.jpg?raw=true" width="400">
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</p>
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Is basic image understanding really solved in state-of-the-art VLMs? We present VisualOverload, a slightly different visual question answering (VQA) benchmark comprising 2,720 question–answer pairs, with privately held ground-truth responses. Unlike prior VQA datasets that typically focus on near global image understanding, VisualOverload challenges models to perform simple, knowledge-free visual understanding and reasoning of details in densely populated (or, *overloaded*) scenes. Our dataset consists of high-resolution scans of public-domain paintings that are populated with multiple figures, actions, and unfolding subplots set against elaborately detailed backdrops. Questions were handcrafted to probe for a thorough understanding of the scene.
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## 📂 Load the dataset
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The easiest way to load the dataset is to use HuggingFace's `datasets`.
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---
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# VisualOverload
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<p align="center">
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<img src="https://github.com/paulgavrikov/visualoverload/blob/main/assets/logo.jpg?raw=true" width="400">
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</p>
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Is basic image understanding really solved in state-of-the-art VLMs? We present VisualOverload, a slightly different visual question answering (VQA) benchmark comprising 2,720 question–answer pairs, with privately held ground-truth responses. Unlike prior VQA datasets that typically focus on near global image understanding, VisualOverload challenges models to perform simple, knowledge-free visual understanding and reasoning of details in densely populated (or, *overloaded*) scenes. Our dataset consists of high-resolution scans of public-domain paintings that are populated with multiple figures, actions, and unfolding subplots set against elaborately detailed backdrops. Questions were handcrafted to probe for a thorough understanding of the scene.
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## 📂 Load the dataset
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The easiest way to load the dataset is to use HuggingFace's `datasets`.
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