Datasets:
Update dataset card
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README.md
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@@ -71,21 +71,21 @@ python prepare.py --dataset-dir data --pipeline extract_images
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python prepare.py --dataset-dir data --pipeline make_tag_labels
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```
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`labels/inclusive/stylized.txt`
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```text
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0/gist_7_4.jpg 이글
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0/gist_7_5.jpg 피플
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```
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`labels/inclusive/stylized.jsonl`
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```json
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{"filename":"0/gist_7_4.jpg","text":"이글","width":87,"height":74}
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{"filename":"0/gist_7_5.jpg","text":"피플","width":104,"height":202}
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```
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`labels/exclusive/stylized.txt` and `labels/exclusive/stylized.jsonl` use the same two formats.
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## License
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**Inclusive** counts crops where that tag is true, including crops with other true tags. **Exclusive** counts crops whose only true tag is that tag.
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python prepare.py --dataset-dir data --pipeline make_tag_labels
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```
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`./K_STR_Bench/labels/inclusive/stylized.txt`
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```text
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0/gist_7_4.jpg 이글
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0/gist_7_5.jpg 피플
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```
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`./K_STR_Bench/labels/inclusive/stylized.jsonl`
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```json
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{"filename":"0/gist_7_4.jpg","text":"이글","width":87,"height":74}
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{"filename":"0/gist_7_5.jpg","text":"피플","width":104,"height":202}
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```
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`./K_STR_Bench/labels/exclusive/stylized.txt` and `./K_STR_Bench/labels/exclusive/stylized.jsonl` use the same two formats.
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## License
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**Inclusive** counts crops where that tag is true, including crops with other true tags. **Exclusive** counts crops whose only true tag is that tag.
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<table>
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<thead>
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<tr>
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<th rowspan="2">Tag</th>
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<th colspan="2">K-STR-Bench</th>
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<th colspan="2">Union14M-General</th>
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<th colspan="2">Union14M-Benchmark</th>
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</tr>
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<tr>
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<th>Inclusive</th>
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<th>Exclusive</th>
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<th>Inclusive</th>
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<th>Exclusive</th>
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<th>Inclusive</th>
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<th>Exclusive</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<td>Crops</td>
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<td colspan="2">20,086</td>
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<td colspan="2">9,994</td>
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<td colspan="2">7,888</td>
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</tr>
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<tr><td>Stylized</td><td>2,273</td><td>821</td><td>791</td><td>171</td><td>1,727</td><td>311</td></tr>
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<tr><td>Inconsistent size</td><td>602</td><td>88</td><td>253</td><td>25</td><td>599</td><td>19</td></tr>
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<tr><td>Incomplete</td><td>767</td><td>193</td><td>844</td><td>133</td><td>759</td><td>82</td></tr>
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<tr><td>Low visibility</td><td>1,352</td><td>550</td><td>6,984</td><td>4,743</td><td>307</td><td>25</td></tr>
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<tr><td>Noisy</td><td>2,844</td><td>1,466</td><td>696</td><td>109</td><td>1,540</td><td>143</td></tr>
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<tr><td>Distracted</td><td>3,019</td><td>457</td><td>880</td><td>31</td><td>3,733</td><td>127</td></tr>
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<tr><td>Perspective distortion</td><td>3,268</td><td>181</td><td>1,231</td><td>33</td><td>676</td><td>4</td></tr>
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<tr><td>Non-single-line</td><td>118</td><td>34</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>
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<tr><td>Non-straight</td><td>479</td><td>60</td><td>155</td><td>6</td><td>2,731</td><td>203</td></tr>
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<tr><td>Rotated</td><td>4,554</td><td>1,052</td><td>2,127</td><td>214</td><td>4,928</td><td>536</td></tr>
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</tbody>
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</table>
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