<annotation>
<folder>JPEGImages</folder>
<filename>ia_100004661.jpg</filename>
<path>/home/ycc/darknet-master/CE/laji/JPEGImages/ia_100004661.jpg</path>
<source>
<database>Unknown</database>
</source>
<size>
<width>800</width>
<height>640</height>
<depth>3</depth>
</size>
<segmented>0</segmented>
<object>
<name>belt</name>
<pose>Unspecified</pose>
<truncated>1</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>347</xmin>
<ymin>586</ymin>
<xmax>429</xmax>
<ymax>640</ymax>
</bndbox>
</object>
<object>
<name>belt</name>
<pose>Unspecified</pose>
<truncated>0</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>672</xmin>
<ymin>565</ymin>
<xmax>753</xmax>
<ymax>620</ymax>
</bndbox>
</object>
<object>
<name>belt</name>
<pose>Unspecified</pose>
<truncated>1</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>186</xmin>
<ymin>591</ymin>
<xmax>272</xmax>
<ymax>640</ymax>
</bndbox>
</object>
<object>
<name>belt</name>
<pose>Unspecified</pose>
<truncated>0</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>649</xmin>
<ymin>339</ymin>
<xmax>691</xmax>
<ymax>386</ymax>
</bndbox>
</object>
<object>
<name>belt</name>
<pose>Unspecified</pose>
<truncated>0</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>521</xmin>
<ymin>148</ymin>
<xmax>590</xmax>
<ymax>216</ymax>
</bndbox>
</object>
<object>
<name>belt</name>
<pose>Unspecified</pose>
<truncated>0</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>589</xmin>
<ymin>424</ymin>
<xmax>628</xmax>
<ymax>467</ymax>
</bndbox>
</object>
<object>
<name>belt</name>
<pose>Unspecified</pose>
<truncated>1</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>753</xmin>
<ymin>547</ymin>
<xmax>800</xmax>
<ymax>591</ymax>
</bndbox>
</object>
<object>
<name>belt</name>
<pose>Unspecified</pose>
<truncated>1</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>750</xmin>
<ymin>429</ymin>
<xmax>800</xmax>
<ymax>471</ymax>
</bndbox>
</object>
<object>
<name>belt</name>
<pose>Unspecified</pose>
<truncated>0</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>61</xmin>
<ymin>543</ymin>
<xmax>133</xmax>
<ymax>576</ymax>
</bndbox>
</object>
<object>
<name>belt</name>
<pose>Unspecified</pose>
<truncated>0</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>417</xmin>
<ymin>555</ymin>
<xmax>493</xmax>
<ymax>605</ymax>
</bndbox>
</object>
<object>
<name>belt</name>
<pose>Unspecified</pose>
<truncated>0</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>86</xmin>
<ymin>481</ymin>
<xmax>140</xmax>
<ymax>511</ymax>
</bndbox>
</object>
<object>
<name>belt</name>
<pose>Unspecified</pose>
<truncated>0</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>529</xmin>
<ymin>506</ymin>
<xmax>578</xmax>
<ymax>540</ymax>
</bndbox>
</object>
<object>
<name>belt</name>
<pose>Unspecified</pose>
<truncated>0</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>220</xmin>
<ymin>475</ymin>
<xmax>269</xmax>
<ymax>497</ymax>
</bndbox>
</object>
<object>
<name>belt</name>
<pose>Unspecified</pose>
<truncated>0</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>449</xmin>
<ymin>490</ymin>
<xmax>492</xmax>
<ymax>512</ymax>
</bndbox>
</object>
<object>
<name>belt</name>
<pose>Unspecified</pose>
<truncated>0</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>571</xmin>
<ymin>510</ymin>
<xmax>619</xmax>
<ymax>546</ymax>
</bndbox>
</object>
<object>
<name>belt</name>
<pose>Unspecified</pose>
<truncated>0</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>686</xmin>
<ymin>426</ymin>
<xmax>709</xmax>
<ymax>457</ymax>
</bndbox>
</object>
<object>
<name>belt</name>
<pose>Unspecified</pose>
<truncated>0</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>710</xmin>
<ymin>235</ymin>
<xmax>777</xmax>
<ymax>277</ymax>
</bndbox>
</object>
<object>
<name>belt</name>
<pose>Unspecified</pose>
<truncated>0</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>23</xmin>
<ymin>461</ymin>
<xmax>66</xmax>
<ymax>495</ymax>
</bndbox>
</object>
<object>
<name>belt</name>
<pose>Unspecified</pose>
<truncated>0</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>262</xmin>
<ymin>498</ymin>
<xmax>310</xmax>
<ymax>526</ymax>
</bndbox>
</object>
<object>
<name>belt</name>
<pose>Unspecified</pose>
<truncated>0</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>228</xmin>
<ymin>526</ymin>
<xmax>286</xmax>
<ymax>566</ymax>
</bndbox>
</object>
<object>
<name>belt</name>
<pose>Unspecified</pose>
<truncated>0</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>401</xmin>
<ymin>517</ymin>
<xmax>449</xmax>
<ymax>540</ymax>
</bndbox>
</object>
<object>
<name>belt</name>
<pose>Unspecified</pose>
<truncated>1</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>1</xmin>
<ymin>586</ymin>
<xmax>75</xmax>
<ymax>635</ymax>
</bndbox>
</object>
<object>
<name>belt</name>
<pose>Unspecified</pose>
<truncated>1</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>739</xmin>
<ymin>500</ymin>
<xmax>800</xmax>
<ymax>534</ymax>
</bndbox>
</object>
<object>
<name>belt</name>
<pose>Unspecified</pose>
<truncated>0</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>84</xmin>
<ymin>579</ymin>
<xmax>141</xmax>
<ymax>623</ymax>
</bndbox>
</object>
<object>
<name>belt</name>
<pose>Unspecified</pose>
<truncated>1</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>750</xmin>
<ymin>429</ymin>
<xmax>800</xmax>
<ymax>469</ymax>
</bndbox>
</object>
<object>
<name>belt</name>
<pose>Unspecified</pose>
<truncated>1</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>1</xmin>
<ymin>454</ymin>
<xmax>34</xmax>
<ymax>481</ymax>
</bndbox>
</object>
<object>
<name>belt</name>
<pose>Unspecified</pose>
<truncated>0</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>689</xmin>
<ymin>510</ymin>
<xmax>731</xmax>
<ymax>563</ymax>
</bndbox>
</object>
<object>
<name>belt</name>
<pose>Unspecified</pose>
<truncated>0</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>536</xmin>
<ymin>417</ymin>
<xmax>587</xmax>
<ymax>451</ymax>
</bndbox>
</object>
<object>
<name>belt</name>
<pose>Unspecified</pose>
<truncated>1</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>741</xmin>
<ymin>154</ymin>
<xmax>800</xmax>
<ymax>232</ymax>
</bndbox>
</object>
</annotation>
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垃圾检测数据集,包括垃圾袋,垃圾桶,瓶子,金属,纸张,果皮,纸团,食品包装袋,纸盒,烟头,瓶盖,杯子等检测目标.详情:https://blog.csdn.net/qq_34717531/article/details/123844312?spm=1001.2014.3001.5502 该数据集分为二个部分,JPEGImages和Annotations.JPEGImages文件夹中有1000+张路边的垃圾图像,共有2800+个垃圾标注框。 并对每张图片使用labelimg做了人工标注,标注对应的xml文件放在了Annotations文件夹中. 本数据集图片清晰,场景广泛,精心挑选,人工标注.适用于任意场景,可作为垃圾检测的模板数据集. 应用特定场景时,只需加入部分特定场景数据,即可满足对特定场景垃圾检测的检测. 免去了收集,挑选,标注垃圾图片的时间,可直接进行工程化应用.
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