Datasets:
Align domain labels with benchmark taxonomy
Browse files- README.md +1 -1
- loc_2x2.jsonl +0 -0
- loc_3x3.jsonl +0 -0
- mcq.jsonl +0 -0
- open_qa.jsonl +0 -0
- sel_2x2.jsonl +0 -0
- sel_3x3.jsonl +0 -0
README.md
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@@ -71,7 +71,7 @@ Annotations are stored as JSONL, with one evaluation instance per line. Each rec
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The evaluation code provides prompt templates for Grid-Sel and Grid-Loc.
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`bbox_norm` uses `[x1, y1, x2, y2]` coordinates normalized to [0, 1000]. Record metadata includes the visual domain and taxonomy depth.
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Single-image records also contain `image_url` and `image_url_type`. The `image` field specifies where to save the retrieved image locally. Records requiring an upstream dataset additionally include its identifier, relative path and acquisition filename.
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The evaluation code provides prompt templates for Grid-Sel and Grid-Loc.
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`bbox_norm` uses `[x1, y1, x2, y2]` coordinates normalized to [0, 1000]. Record metadata includes the visual domain and taxonomy depth. The `meta.domain` label follows the ground-truth category in the taxonomy: `animal`, `plant`, `fungus`, `artifact`, `transport`, `food`, `scene`, or `other`.
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Single-image records also contain `image_url` and `image_url_type`. The `image` field specifies where to save the retrieved image locally. Records requiring an upstream dataset additionally include its identifier, relative path and acquisition filename.
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loc_2x2.jsonl
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loc_3x3.jsonl
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mcq.jsonl
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open_qa.jsonl
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sel_2x2.jsonl
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sel_3x3.jsonl
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