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[Fix] fix MathCanvas-Bench md5 & summarize_mathcanvas_results #1502

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[Fix] fix MathCanvas-Bench md5 & summarize_mathcanvas_results #1502

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2 changes: 1 addition & 1 deletion vlmeval/dataset/image_vqa.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4048,7 +4048,7 @@ class MathCanvas(ImageBaseDataset):
"https://huggingface.co/datasets/shiwk24/MathCanvas-Bench/resolve/main/MathCanvas_Bench_VLMEvalKit.tsv"
}
DATASET_MD5 = {
"MathCanvas-Bench": "9fd0b783ca416dbb20ecfb04d2711411"
"MathCanvas-Bench": "827dd1b1ce9c17d2b8338af6a13b6791"
}

HINT = (
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7 changes: 3 additions & 4 deletions vlmeval/dataset/utils/mathcanvas.py
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Expand Up @@ -233,15 +233,14 @@ def summarize_mathcanvas_results(all_results: List[Dict]):
stats['overall']['completely_correct_count'] += 1

# 2. Image Presence Stats
q_images = [p for p in item.get("question_interleave", []) if p['type'] == 'image']
q_images = item.get("image", [])
image_presence_key = "Has Image" if len(q_images) > 0 else "No Image"
_update_stats(stats['by_question_image_count'], image_presence_key, score)

# 3. Knowledge Stats
# Use the primary knowledge area for categorization.
knowledges = item.get("knowledges", [])
knowledge_key = knowledges[0] if knowledges else "Unknown"
_update_stats(stats['by_knowledge'], knowledge_key, score)
category = item.get("category", "Unknown")
_update_stats(stats['by_knowledge'], category, score)

# --- Final Report Generation ---
def calculate_accuracy(data_dict):
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