Datasets:
Upload LookBench v20251201
Browse files- v20251201/aigen_streetlook/gallery.parquet +2 -2
- v20251201/aigen_streetlook/query.parquet +2 -2
- v20251201/aigen_studio/gallery.parquet +2 -2
- v20251201/aigen_studio/query.parquet +2 -2
- v20251201/noise/noise_00000.parquet +2 -2
- v20251201/noise/noise_00001.parquet +2 -2
- v20251201/noise/noise_00002.parquet +2 -2
- v20251201/noise/noise_00003.parquet +2 -2
- v20251201/noise/noise_00004.parquet +2 -2
- v20251201/noise/noise_00005.parquet +2 -2
- v20251201/noise/noise_00006.parquet +2 -2
- v20251201/noise/noise_00007.parquet +2 -2
- v20251201/noise/noise_00008.parquet +2 -2
- v20251201/real_streetlook/gallery.parquet +2 -2
- v20251201/real_streetlook/query.parquet +2 -2
- v20251201/real_studio_flat/gallery.parquet +2 -2
- v20251201/real_studio_flat/query.parquet +2 -2
- v20251201/version_info.json +17 -2
v20251201/aigen_streetlook/gallery.parquet
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v20251201/aigen_streetlook/query.parquet
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v20251201/aigen_studio/gallery.parquet
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v20251201/aigen_studio/query.parquet
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v20251201/noise/noise_00000.parquet
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v20251201/noise/noise_00001.parquet
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v20251201/noise/noise_00002.parquet
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v20251201/noise/noise_00003.parquet
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v20251201/noise/noise_00004.parquet
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v20251201/noise/noise_00005.parquet
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v20251201/noise/noise_00006.parquet
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v20251201/noise/noise_00007.parquet
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v20251201/noise/noise_00008.parquet
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v20251201/real_streetlook/gallery.parquet
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v20251201/real_streetlook/query.parquet
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v20251201/real_studio_flat/gallery.parquet
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v20251201/real_studio_flat/query.parquet
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v20251201/version_info.json
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{
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"version": "v20251201",
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"created_at": "2025-12-
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"statistics": {
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"total_query_samples": 2345,
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"total_gallery_samples": 8779,
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"real_streetlook",
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"real_studio_flat"
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],
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"includes_noise": true
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}
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{
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"version": "v20251201",
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"created_at": "2025-12-11T07:04:21.945330",
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"statistics": {
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"total_query_samples": 2345,
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"total_gallery_samples": 8779,
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"real_streetlook",
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"real_studio_flat"
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],
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"includes_noise": true,
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"columns": [
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"image",
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"raw_image",
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"category",
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"main_attribute",
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"other_attributes",
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"bbox",
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"item_ID",
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"task",
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"difficulty"
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],
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"notes": {
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"image": "Cropped product image",
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"raw_image": "Full image before crop (None for noise data)"
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}
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}
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