image imagewidth (px) 138 390 | objects dict | split stringclasses 2
values |
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{
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} | Training | |
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} | Training | |
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} | Training | |
{
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} | Training | |
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} | Training | |
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} | Training | |
{
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} | Training | |
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} | Training | |
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} | Training | |
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} | Training | |
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} | Training | |
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} | Training | |
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} | Training | |
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} | Training | |
{
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{
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{
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} | Training |
End of preview. Expand in Data Studio
Arecanut Xray Grade Detection
This dataset provides real X-ray images of arecanut (betel nut) samples captured in a controlled laboratory environment. Images were acquired using a fixed X-ray machine and CR reader, offering non-invasive internal views for quality assessment. It supports object detection tasks focused on automated grading of arecanut based on internal structural features. The dataset contains 916 images with 923 bounding box annotations across 3 categories.
This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.
The original train/test/val split has been preserved in the split column.
Citation
@article{naik2025x,
title={X-ArecaNet: Dataset of arecanut X-ray images for deep learning applications},
author={Naik, Praveen M. and Rudra, Bhawana},
journal={Data in Brief},
volume={61},
pages={111721},
year={2025},
publisher={Elsevier}
}
Naik, Praveen; Rudra, Bhawana (2024), “X-ray Image dataset of Arecanut”, Mendeley Data, V1, doi: 10.17632/sk5n6xdcmm.1
This dataset was reformatted from its original format to match HuggingFace standards.
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