<annotation>
<folder>VOC</folder>
<filename>000000333654.jpg</filename>
<source>
<database>Unknown</database>
</source>
<owner>
<flickrid>NULL</flickrid>
<name>company</name>
</owner>
<size>
<width>640</width>
<height>480</height>
<depth>3</depth>
</size>
<segmented>0</segmented>
<object>
<name>truck</name>
<pose>Unspecified</pose>
<truncated>0</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>161</xmin>
<ymin>308</ymin>
<xmax>183</xmax>
<ymax>330</ymax>
</bndbox>
</object>
<object>
<name>truck</name>
<pose>Unspecified</pose>
<truncated>0</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>175</xmin>
<ymin>306</ymin>
<xmax>212</xmax>
<ymax>333</ymax>
</bndbox>
</object>
<object>
<name>truck</name>
<pose>Unspecified</pose>
<truncated>0</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>232</xmin>
<ymin>287</ymin>
<xmax>258</xmax>
<ymax>308</ymax>
</bndbox>
</object>
<object>
<name>truck</name>
<pose>Unspecified</pose>
<truncated>0</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>271</xmin>
<ymin>271</ymin>
<xmax>291</xmax>
<ymax>291</ymax>
</bndbox>
</object>
<object>
<name>truck</name>
<pose>Unspecified</pose>
<truncated>0</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>275</xmin>
<ymin>288</ymin>
<xmax>290</xmax>
<ymax>302</ymax>
</bndbox>
</object>
<object>
<name>truck</name>
<pose>Unspecified</pose>
<truncated>0</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>293</xmin>
<ymin>355</ymin>
<xmax>316</xmax>
<ymax>375</ymax>
</bndbox>
</object>
<object>
<name>truck</name>
<pose>Unspecified</pose>
<truncated>0</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>352</xmin>
<ymin>308</ymin>
<xmax>374</xmax>
<ymax>341</ymax>
</bndbox>
</object>
<object>
<name>truck</name>
<pose>Unspecified</pose>
<truncated>0</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>392</xmin>
<ymin>297</ymin>
<xmax>406</xmax>
<ymax>308</ymax>
</bndbox>
</object>
<object>
<name>truck</name>
<pose>Unspecified</pose>
<truncated>0</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>339</xmin>
<ymin>233</ymin>
<xmax>350</xmax>
<ymax>244</ymax>
</bndbox>
</object>
<object>
<name>truck</name>
<pose>Unspecified</pose>
<truncated>0</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>306</xmin>
<ymin>330</ymin>
<xmax>333</xmax>
<ymax>354</ymax>
</bndbox>
</object>
<object>
<name>truck</name>
<pose>Unspecified</pose>
<truncated>0</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>152</xmin>
<ymin>300</ymin>
<xmax>177</xmax>
<ymax>322</ymax>
</bndbox>
</object>
<object>
<name>truck</name>
<pose>Unspecified</pose>
<truncated>0</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>320</xmin>
<ymin>262</ymin>
<xmax>332</xmax>
<ymax>276</ymax>
</bndbox>
</object>
<object>
<name>truck</name>
<pose>Unspecified</pose>
<truncated>0</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>448</xmin>
<ymin>263</ymin>
<xmax>458</xmax>
<ymax>274</ymax>
</bndbox>
</object>
<object>
<name>truck</name>
<pose>Unspecified</pose>
<truncated>0</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>15</xmin>
<ymin>220</ymin>
<xmax>491</xmax>
<ymax>400</ymax>
</bndbox>
</object>
</annotation>
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大卡车数据集5763张含voc(xml)格式+yolo(txt)格式+json格式三种标签.zip
共2000个文件
txt:5764个
xml:5763个
json:5763个
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道路卡车识别检测数据集(课程作业、设计、比赛所用) 【实际项目应用】: 道路行驶大卡车识别检测、卡车计数等 【数据集说明】: 道路卡车识别检测数据集,一共5763张图片,数据拍摄场景丰富,目标大小、种类丰富且分布均匀,数据整体多样性充足,标签包含voc(xml)、yolo(txt)、json三种格式,多种目标检测算法可直接使用。标注精准无误,算法拟合不错,数据质量可靠。
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大卡车数据集5763张含voc(xml)格式+yolo(txt)格式+json格式三种标签.zip (2000个子文件)
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- hanpengpeng3298262023-04-03简直是宝藏资源,实用价值很高,支持!onnx2023-07-06加油!感谢认可!
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