The orchid flower dataset was selected from the northern part of Thailand. The dataset contains Thai native orchid flowers, and each class contains at least 20 samples. The orchid dataset including 52 species and the visual characteristics of the flower are varying in terms of shape, color, texture, size, and the other parts of the orchid plant like a leaf, inflorescence, roots, and surroundings. All images are taken from many devices such as a digital camera, a mobile phone, and other equipment. The orchids dataset contains 3,559 images from 52 categories.
Download links:
Test - https://drive.google.com/open?id=1AGYAHLJFS4qiLyNLznHDKtWZx0d4RCK1
Train - https://drive.google.com/open?id=1AHwLH3-P8eWAXgXMs-FU2Ni6b2YMO5yY
This dataset is only for research purposes.
Please remember cited correctly the paper: "Orchids Classification Using Spatial Transformer Network with Adaptive Scaling"
BibTeX:
@inproceedings{sarachai2019orchids,
title={Orchids Classification Using Spatial Transformer Network with Adaptive Scaling},
author={Sarachai, Watcharin and Bootkrajang, Jakramate and Chaijaruwanich, Jeerayut and Somhom, Samerkae},
booktitle={International Conference on Intelligent Data Engineering and Automated Learning – IDEAL 2019},
pages={1--10},
DOI={978-3-030-33607-3_1},
year={2019},
organization={Springer International Publishing}
}
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Features Extracted from BraTS 2012-2013
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This dataset contains the comparison results on the 'Euroc' public dataset of DVIO, VINS-Mono, and ROVIO.
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This dataset comes up as a benchmark dataset for machines to automatically recognizing the handwritten assamese digists (numerals) by extracting useful features by analyzing the structure. The Assamese language comprises of a total of 10 digits from 0 to 9. We have collected a total of 516 handwritten digits from 52 native assamese people irrespective of their age (12-86 years), gender, educational background etc. The digits are captured in .jpeg format using a paint mobile application developed by us which automatically saves the images in the internal storage of the mobile.
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An accurate and reliable image-based quantification system for blueberries may be useful for the automation of harvest management. It may also serve as the basis for controlling robotic harvesting systems. Quantification of blueberries from images is a challenging task due to occlusions, differences in size, illumination conditions and the irregular amount of blueberries that can be present in an image. This paper proposes the quantification per image and per batch of blueberries in the wild, using high definition images captured using a mobile device.
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The Contest: Goals and Organisation
The 2019 Data Fusion Contest, organized by the Image Analysis and Data Fusion Technical Committee (IADF TC) of the IEEE Geoscience and Remote Sensing Society (GRSS), the Johns Hopkins University (JHU), and the Intelligence Advanced Research Projects Activity (IARPA), aimed to promote research in semantic 3D reconstruction and stereo using machine intelligence and deep learning applied to satellite images.
- Participants to the benchmark are intended to submit:
- 2D semantic maps and nDSM/disparity/DSM maps in raster format (similar to the tif file of the training set) for Tracks 1, 2, and 3
- 3D semantic predictions in ASCII text files (similar to the text file of the training set) for Track 4
These results will be submitted to the Codalab competition websites for evaluation:
- Ranking among the participants will be based on:
- mIoU-3 for Tracks 1, 2, and 3
- mIoU for Track 4
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