Cross-domain Few-annotation Industrial Dataset

Citation Author(s):
Yuhang
Huang
Shilong
Zou
Xinwang
Liu
Kai
Xu
Submitted by:
Shilong Zou
Last updated:
Wed, 09/25/2024 - 05:43
DOI:
10.21227/8jmr-gc98
Data Format:
*.jpg; *.json
License:
Creative Commons Attribution
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Categories:
Artificial Intelligence
Keywords:
Cross-domain Object detection, few-annotation object detection
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Abstract 

This dataset, mentioned in paper "MS2A: Memory Storage-to-Adaptation for Cross-domain Few-annotation Object Detection" and prepared for Cross-domain Few-annotation Object Detection task, consists of two cross-domain scenarios: Indus-S to Indus-T1 and Indus-S to Indus-T2. In detail, Indus-S consists of 4614 images for training and 1153 images for validation; Indus-T1 and Indus-T2 have 269 and 432 images for validation respectively. For the training data of Indus-T1 and Indus-T2, we introduce three different settings: 10-anno, 30-anno and 50-anno. This dataset was collected from different factories with different domains and labeled using LabelMe. The objects were annotated as the part class.

Dataset Link:

Baidu Netdisk link: https://pan.baidu.com/s/1QIVEVO5n1RYEGndHPe6aRg?pwd=cfod

Instructions: 

The main directory of the dataset includes three different scenarios: Indus-S, Indus-T1, and Indus-T2. Each scenario file contains four files: annotations, train2017, val2017 and test2017. Specifically, the folder train2017, and val2017 are used for training and validation respectively, and the corresponding labels in COCO format are stored in the annotations folder.

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Submitted by mehmet akyurt on Sun, 09/22/2024 - 03:41

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