FindVehicle: A NER dataset in transportation to extract keywords describing vehicles on the road
🔥🔥🔥FindVehicle: The 🔥first🔥 NER dataset in traffic domain for natural language-based vehicle retrieval
🎉🎉🎉VehicleFinder A text-image cross-modal vehicle retrieval system link
Data Link 1: Baidu Cloud Disk Password: xp9o
Data Link 2: Google Drive
FindVehicle has 2 data formats, CoNLL-style and jsonlines.
CoNLL-style Example (Flat Entity)
I O
am O
looking O
for O
a O
white B-vehicle_color
sedan B-vehicle_type
. O
CoNLL-style Example (Overlapped Entity)
I O
am O
looking O
for O
a O
white B-vehicle_color
Audi B-vehicle_brand
Q7 B-vehicle_model
. OI O
am O
looking O
for O
a O
white B-vehicle_color
Audi B-vehicle_type-suv
Q7 E-vehicle_type-suv
. O
Install jsonlines, then you could read it.
pip install jsonlines
jsonlines Example
{
"id": 41628,
"data": "Let the clever boy help find out the Silver XPeng G3 and lemon yellow Chevrolet Trailblazer in the Bottom Left of the image that driven left .",
"ner_label": [
["vehicle_color", 37, 43, "Silver", 8, 9, ["Silver"]], ### label, char span start index, char span end index, char span check, token span start index, token > > span end index, token span check
["vehicle_brand", 44, 49, "XPeng", 9, 10, ["XPeng"]],
["vehicle_model", 50, 52, "G3", 10, 11, ["G3"]],
["vehicle_color", 57, 69, "lemon yellow", 12, 14, ["lemon", "yellow"]],
["vehicle_brand", 70, 79, "Chevrolet", 14, 15, ["Chevrolet"]],
["vehicle_model", 80, 91, "Trailblazer", 15, 16, ["Trailblazer"]],
["vehicle_location", 99, 110, "Bottom Left", 18, 20, ["Bottom", "Left"]],
["vehicle_orientation", 99, 105, "Bottom", 18, 19, ["Bottom"]]],
"re_label": [[0, 1, 2, 6, 7], [3, 4, 5, 6, 7]]
### the indexes 0,1,2,6,7 refer to one target, indexes 3,4,5,6,7 refer to one target. }
@misc{guan2023findvehicle,
title={FindVehicle and VehicleFinder: A NER dataset for natural language-based vehicle retrieval and a keyword-based cross-modal vehicle retrieval system},
author={Runwei Guan and Ka Lok Man and Feifan Chen and Shanliang Yao and Rongsheng Hu and Xiaohui Zhu and Jeremy Smith and Eng Gee Lim and Yutao Yue},
year={2023},
eprint={2304.10893},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
Notes: Any problem please send them in Issues.