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README2.md

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+---
+dataset_info:
+  features:
+  - name: question_id
+    dtype: string
+  - name: qtype
+    dtype: string
+  - name: figure_path
+    dtype: image
+  - name: visual_figure_path
+    list: image
+  - name: question
+    dtype: string
+  - name: answer
+    dtype: string
+  - name: instructions
+    dtype: string
+  - name: prompt
+    dtype: string
+  - name: options
+    list: string
+  splits:
+  - name: info
+    num_bytes: 9389294399.0
+    num_examples: 55091
+  - name: plain
+    num_bytes: 15950918129.0
+    num_examples: 55091
+  - name: visual_metaphor
+    num_bytes: 144053150.0
+    num_examples: 450
+  - name: visual_basic
+    num_bytes: 1254942699.466
+    num_examples: 7297
+  download_size: 20376840742
+  dataset_size: 26739208377.466
+configs:
+- config_name: default
+  data_files:
+  - split: info
+    path: data/info-*
+  - split: plain
+    path: data/plain-*
+  - split: visual_metaphor
+    path: data/visual_metaphor-*
+  - split: visual_basic
+    path: data/visual_basic-*
+---
+
+
+
+# InfoChartQA:  Benchmark for Multimodal Question Answering on Infographic Charts
+
+
+
+![xbhs3](teaser.jpg)🤗[Dataset](https://huggingface.co/datasets/Jietson/InfoChartQA)
+
+# Dataset 
+
+You can find our dataset on huggingface: 🤗[InfoChartQA Dataset](https://huggingface.co/datasets/Jietson/InfoChartQA)
+
+# Usage
+
+Each question entry is arranged as:
+
+```
+--question_id: int
+--qtype: int
+--figure_path: image
+--visual_figure_path: list of image
+--question: str
+--answer: str
+--instructions: str
+--prompt: str
+--options: list of dict ("A/B/C/D":"option_content")
+```
+
+Each question is built as:
+
+```
+image_input: figure_path, visual_figure_path_1...visual_figure_path_n (if any)
+text_input: prompt (if any) + question + options (if any) + instructions (if any)
+```
+
+# Evaluate
+
+You should store and evaluate model's response as:
+
+```python
+# Example code for evaluate
+def build_question(query):#to build the question
+    question = ""
+    if "prompt" in query:
+    	question = question + f"{query["prompt"]}\n"
+    question = question + f"{query["question"]}\n"
+    if "options" in query:
+        for _ in query["options"]:
+            question = question + f"{_} {query['options'][_]}\n"
+	if "instructions" in query:
+    	question = question + query["instructions"]
+    return question
+
+with open("visual_basic.json","r",encode="utf-8") as f:
+	queries = json.load(f)
+
+for idx in range(queries):
+    question = build_question(queries[idx])
+    figure_path = [queries[idx]['figure_path']]
+    visual_figure_path = queries[idx]['visual_figure_path']
+    response = model.generate(question, [figure_path, visual_figure_path])# generate model's response based on 
+    queries[idx]["response"] = reponse
+
+with open("model_reponse.json","w",encode="utf-8") as f:
+	json.dump(queries, f)
+from checker import evaluate
+evaluate("model_reponse.json", "path_to_save_the_result")
+```
+
+Or simply use after your answer is generated:
+
+```python
+python -c "import checker; checker.evaluate(sys.argv[1], sys.argv[2])" PATH_TO_INPUT_FILE PATH_TO_INPUT_FILE 
+```
+
+# LeaderBoard
+
+| Model                    | Infographic | Plain   | Δ     | Basic   | Metaphor | Avg.  |
+| ------------------------ | ----------- | ------- | ----- | ------- | -------- | ----- |
+| **Baselines**            |             |         |       |         |          |       |
+| Human                    | 95.35\*     | 96.28\* | 0.93  | 93.17\* | 88.69    | 90.93 |
+| **Proprietary Models**   |             |         |       |         |          |       |
+| OpenAI O4-mini           | 79.41       | 94.61   | 15.20 | 92.12   | 54.76    | 73.44 |
+| GPT-4o                   | 66.09       | 81.77   | 15.68 | 81.77   | 47.19    | 64.48 |
+| Claude 3.5 Sonnet        | 65.67       | 83.11   | 17.44 | 90.36   | 55.33    | 72.85 |
+| Gemini 2.5 Pro Preview   | 83.31       | 93.88   | 10.07 | 90.01   | 60.42    | 75.22 |
+| Gemini 2.5 Flash Preview | 71.91       | 84.66   | 12.75 | 82.02   | 56.28    | 69.15 |
+| **Open-Source Models**   |             |         |       |         |          |       |
+| Qwen2.5-VL-72B           | 62.06       | 78.47   | 16.41 | 77.34   | 54.64    | 65.99 |
+| Llama-4 Scout            | 67.41       | 84.84   | 17.43 | 81.76   | 51.89    | 66.83 |
+| Intern-VL3-78B           | 66.38       | 82.18   | 15.80 | 79.46   | 51.52    | 65.49 |
+| Intern-VL3-8B            | 56.82       | 73.50   | 16.68 | 74.26   | 49.57    | 61.92 |
+| Janus Pro                | 29.61       | 45.29   | 15.68 | 41.18   | 42.21    | 41.69 |
+| DeepSeek VL2             | 39.81       | 47.01   | 7.20  | 58.72   | 44.54    | 51.63 |
+| Phi-4                    | 46.20       | 66.97   | 20.77 | 61.87   | 38.31    | 50.09 |
+| LLaVA OneVision Chat 78B | 47.78       | 63.66   | 15.88 | 62.11   | 50.22    | 56.17 |
+| LLaVA OneVision Chat 7B  | 38.41       | 54.43   | 16.02 | 61.03   | 45.67    | 53.35 |
+| Pixtral                  | 44.70       | 60.88   | 16.11 | 64.23   | 50.87    | 57.55 |
+| Ovis1.6-Gemma2-9B        | 50.56       | 64.52   | 13.98 | 60.96   | 34.42    | 47.69 |
+| ChartGemma               | 19.99       | 33.81   | 13.82 | 30.52   | 33.77    | 32.15 |
+| TinyChart                | 26.34       | 44.73   | 18.39 | 14.72   | 9.03     | 11.88 |
+| ChartInstruct-LLama2     | 20.55       | 27.91   | 7.36  | 33.86   | 33.12    | 33.49 |
+
+ # License
+
+Our original data contributions (all data except the charts) are distributed under the [CC BY-SA 4.0](https://github.com/princeton-nlp/CharXiv/blob/main/data/LICENSE) license. Our code is licensed under [Apache 2.0](https://github.com/princeton-nlp/CharXiv/blob/main/LICENSE) license. The copyright of the charts belong to the original authors.
+
+##  Cite
+
+If you use our work and are inspired by our work, please consider cite us (available soon):
+
+```
+@misc{lin2025infochartqabenchmarkmultimodalquestion,
+      title={InfoChartQA: A Benchmark for Multimodal Question Answering on Infographic Charts}, 
+      author={Minzhi Lin and Tianchi Xie and Mengchen Liu and Yilin Ye and Changjian Chen and Shixia Liu},
+      year={2025},
+      eprint={2505.19028},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2505.19028}, 
+}
+```
+