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opencc-by-4.0Aug 2024View details →

EOL computer vision pipelines: Object Detection for Image Cropping: Chiroptera

<p>Produced by EOL Chiroptera Object Detection Model. Automatically crops images of bats (Chiroptera) to square dimensions centered around animal(s). Model available in the <a href="https://www.kaggle.com/models/eolorg/chiroptera-crops-thumbnails" target="_blank" rel="noopener">EOL Model Zoo on Kaggle</a>.</p> <p> </p> <p>17,401 rows</p>

opencc-by-4.0Aug 2024View details →

EOL computer vision pipelines: Object Detection for Image Cropping: Lepidoptera

<p>Produced by EOL Lepidoptera Object Detection Model. Automatically crops images of butterflies and moths (Lepidoptera) to square dimensions centered around animal(s). Model available in the <a href="https://www.kaggle.com/models/eolorg/lepidoptera-crops-thumbnails" target="_blank" rel="noopener">EOL Model Zoo on Kaggle</a>.</p> <p> </p> <p>608,163 rows</p> <p> </p>

Fig. 3 in Spodoptera frugiperda (Lepidoptera: Noctuidae) Females Can Detect the Sex Pheromone Emitted by Conspecific Females

Fig. 3. Percentage of female and male Spodoptera frugiperda that landed on different concentrations of the extract of the sex pheromone septum. No moths landed on the control (methanol). Bars of the same color with different letters indicate that there is a significant difference, n = 20 (χ2; P <0.05).

Fig. 2 in Spodoptera frugiperda (Lepidoptera: Noctuidae) Females Can Detect the Sex Pheromone Emitted by Conspecific Females

Fig. 2. Percentage of female and male Spodoptera frugiperda that landed on the female glandular extract. Bars of different colors with different letters for the same extract concentrations indicate a significant difference, n = 20 (χ2; P <0.05).

Fig. 5 in Spodoptera frugiperda (Lepidoptera: Noctuidae) Females Can Detect the Sex Pheromone Emitted by Conspecific Females

Fig. 5. Genital structure of female Spodoptera frugiperda. (A) Confocal image of the bursa copulatrix, frontal view. View of spermatophores within the corpus bursae (BC = bursa copulatrix; SI = signum; CB = corpus bursae; BA = bursae appendix; OS = ostium (exit); ESD = exit to a seminal duct; AA = anterior apophysis; AN = antrum; BD = bursal duct). (B) Micrograph of bursa copulatrix in zenith angle, observing the length and width measurements of the structure (length = 5.38 mm; width = 2.066 mm). (C) Stereoscopic image presenting a frontal view of the genital structure (S = spermatophores). (D) Micrograph of the terminal abdominal (PVL = postvaginal lamella; AVL = antevaginal lamella; OS = ostium; AP = anal papilla).

Fig. 1 in Spodoptera frugiperda (Lepidoptera: Noctuidae) Females Can Detect the Sex Pheromone Emitted by Conspecific Females

Fig. 1. (A) Virgin female abdomen 3 to 5 d old Spodoptera frugiperda females, black circle is location of sex pheromone gland; (B) sex pheromone-producing gland in female S. frugiperda.

Fig. 4 in Spodoptera frugiperda (Lepidoptera: Noctuidae) Females Can Detect the Sex Pheromone Emitted by Conspecific Females

Fig. 4. Male (white bars) and female (gray bars) Spodoptera frugiperda caught by traps with sex pheromone septa (Q1 <Median <Q3). Different letters for Trap 1, Trap 2, Trap 3, or Trap 4 indicate significant differences (Mann-Whitney Test U; n = 34; P <0.05).

opencc-by-4.0May 2023View details →

Data archive for 'Convolutional neural networks facilitate river barrier detection and evidence severe habitat fragmentation in the Mekong River biodiversity hotspot'

<p>This repository contains the code and databases used in the paper 'Convolutional neural networks facilitate river barrier detection and evidence severe habitat fragmentation in the Mekong River biodiversity hotspot'. </p> <p>The 'Mekong River Barrier Database (MRBD)' folder contains the basin-scale barrier database developed in this study. This database contains more than 13,000 unique barriers, which were identified by using the convolutional neural networks-based object detection method from Google Earth’s satellite imagery.</p> <p>The 'FCOS' folder contains the barrier detection model (FCOS ResNext-101-FPN), trained for detecting river barriers from remotely sensed images within the MMDetection framework. The 'FCOS_x101_v2' folder contains the enhanced FCOS model.</p> <p>The 'R_script' folder contains R files used in the paper. Coordinate.R was used to extract coordinates from bounding boxes in each TIF image. CAFI.R was used to calculate the CAFI index in each sub-catchment.</p> <p>The 'Barrier image training set' folder contains over 10,000 river barrier satellite images and their associated JSON files, forming the 'training, validation, and test datasets' used during the model training process. This dataset is made available to the user community in raw, in the hope that others will contribute to its future development, thereby enhancing its use and utility.</p> <p>For more information on the MMDetection framework, refer to the following GitHub repository: <a href="https://github.com/open-mmlab/mmdetection">https://github.com/open-mmlab/mmdetection</a></p>

Figure 8 in Neutron imaging investigation of fossil woods: non-destructive characterization of microstructure and detection of in situ changes as occurring in museum cabinets

Figure 8. Evolution of I(q) vs. q upon drying for (a) Rivecourt and (b) Angeac. The inset is a close-up of the area between 0.01 and 0.07 µm−1.

Figure 5 in Neutron imaging investigation of fossil woods: non-destructive characterization of microstructure and detection of in situ changes as occurring in museum cabinets

Figure 5. Evolution of I(q) vs. q upon wetting for (a) Rivecourt and (b) Angeac. The inset is a close-up of the area between 0.01 and 0.07 µm−1.

Figure 3 in Neutron imaging investigation of fossil woods: non-destructive characterization of microstructure and detection of in situ changes as occurring in museum cabinets

Figure 3. Evolution with time of average grey levels in the wetting experiment for (a) Rivecourt and (b) Angeac. (c) Evolution with time of normalized grey levels. Blue: Rivecourt sample. Red: Angeac sample (see text for details).

Figure 2 in Neutron imaging investigation of fossil woods: non-destructive characterization of microstructure and detection of in situ changes as occurring in museum cabinets

Figure 2. Radiographic images of sample upon sorption experiments. The three pictures on the top are from the Angeac sample, while the four on the bottom are that of Rivecourt. The scale represents 1 cm. The marked areas correspond to the zones used for measuring average grey levels.

Figure 1 in Neutron imaging investigation of fossil woods: non-destructive characterization of microstructure and detection of in situ changes as occurring in museum cabinets

Figure 1. (a) Picture of neutron guide through experimental chamber. (b) Sorption experiment setup and radiographic image obtained. Wood samples were placed in an aluminum cup filled with water. Water appears dark, while aluminum is transparent to neutrons. (c) Desorption experiment setup and radiographic image obtained. Wood samples were wrapped in aluminum foils and placed in a tube, with direct air input (plastic tube, on top).

Figure 7 in Neutron imaging investigation of fossil woods: non-destructive characterization of microstructure and detection of in situ changes as occurring in museum cabinets

Figure 7. Evolution with time of average grey levels in the drying experiment for (a) Rivecourt and (b) Angeac. (c) Evolution with time of normalized grey levels. Blue: Rivecourt sample. Red: Angeac sample (see text for details). (d) Evolution of average grey levels in the drying experiments plotted as a function of the square root of time. Blue: Rivecourt sample. Red: Angeac sample.

Figure 6 in Neutron imaging investigation of fossil woods: non-destructive characterization of microstructure and detection of in situ changes as occurring in museum cabinets

Figure 6. Radiographic images of sample upon desorption experiments. The Angeac sample is on the top, while the Rivecourt sample is on the bottom. The scale represents 1 cm. The marked areas correspond to the zones used for measuring average grey levels. (For Rivecourt, it was done on another sample due to implosion of the sample.)

Figure 4 in Neutron imaging investigation of fossil woods: non-destructive characterization of microstructure and detection of in situ changes as occurring in museum cabinets

Figure 4. Evolution of average grey levels in the wetting experiments plotted as a function of the square root of time. (a) Rivecourt and (b) Angeac.

Replication Package for "PyTraceBERT: Python Traceback-based Language Model for Detecting Compatibility Issues in Deep Learning Systems"

<p>This package contains the traceback data, pre-trained models, and static word embeddings used in the paper, PyTraceBERT: Python Traceback-based Language Model for Detecting Compatibility Issues in Deep Learning Systems.</p>

Detection of Areas with Human Vulnerability Using Public Satellite Images and Deep Learning (Dataset)

<div> <h2>Overview</h2> <a href="https://github.com/fbvidal/HumanVulnerabilityDetectionDL#overview"></a></div> <p>This repository contains the code and resources for the project titled <strong>"Detection of Areas with Human Vulnerability Using Public Satellite Images and Deep Learning"</strong>. The goal of this project is to identify regions where individuals are living under precarious conditions and facing neglected basic needs, a situation often seen in Brazil. This concept is referred to as "human vulnerability" and is exemplified by families living in inadequate shelters or on the streets in both urban and rural areas.</p> <p>Focusing on the Federal District of Brazil as the research area, this project aims to develop two novel public datasets consisting of satellite images. The datasets contain imagery captured at 50m and 100m scales, covering regions of human vulnerability, traditional areas, and improperly disposed waste sites.</p> <p>The project also leverages these datasets for training deep learning models, including <strong>YOLOv7</strong> and other state-of-the-art models, to perform image segmentation. A comparative analysis is conducted between the models using two training strategies: training from scratch with random weight initialization and fine-tuning using pre-trained weights through <strong>transfer learning</strong>.</p> <div> <h3>Key Achievements</h3> <a href="https://github.com/fbvidal/HumanVulnerabilityDetectionDL#key-achievements"></a></div> <ul> <li>Two new satellite image datasets focusing on human vulnerability and improperly disposed waste sites, available in public domains.</li> <li>Comparison of image segmentation models, including <strong>YOLOv7</strong> and <strong>Segmentation Models</strong>, with performance metrics.</li> <li>Best F1-scores: 0.55 for <strong>YOLOv7</strong> and 0.64 for <strong>Segmentation Models</strong>.</li> </ul> <p>This repository provides the code, models, and data pipelines used for training, evaluation, and performance comparison of these deep learning models.<br><br></p> <div> <h2>Citation (Bibtex)</h2> <a href="https://github.com/fbvidal/HumanVulnerabilityDetectionDL?tab=readme-ov-file#citation-bibtex"></a></div> <pre><code>@TECHREPORT {TechReport-Julia-Laura-HumanVulnerability-2024, author = "Julia Passos Pontes, Laura Maciel Neves Franco, Flavio De Barros Vidal", title = "Detecção de Áreas com Atividades de Vulnerabilidade Humana utilizando Imagens Públicas de Satélites e Aprendizagem Profunda", institution = "University of Brasilia", year = "2024", type = "Undergraduate Thesis", address = "Computer Science Department - University of Brasilia - Asa Norte - Brasilia - DF, Brazil", month = "aug", note = "People living in precarious conditions and with their basic needs neglected is an unfortunate reality in Brazil. This scenario will be approached in this work according to the concept of \"human vulnerability\" and can be exemplified through families who live in inadequate shelters, without basic structures and on the streets of urban or rural centers. Therefore, assuming the Federal District as the research scope, this project proposes to develop two new databases to be made available publicly, considering the map scales of 50m and 100m, and composed by satellite images of human vulnerability areas, regions treated as traditional and waste disposed inadequately. Furthermore, using these image bases, trainings were done with the YOLOv7 model and other deep learning models for image segmentation. By adopting an exploratory approach, this work compares the results of different image segmentation models and training strategies, using random weight initialization (from scratch) and pre-trained weights (transfer learning). Thus, the present work was able to reach maximum F1 score values of 0.55 for YOLOv7 and 0.64 for other segmentation models." } </code></pre> <div> </div> <div> <h2>License</h2> <a href="https://github.com/fbvidal/HumanVulnerabilityDetectionDL?tab=readme-ov-file#license"></a></div> <p>This project is licensed under the MIT License - see the LICENSE file for details.</p>

FRGADB - FIRST Radio Galaxy Anomaly Detection Benchmark

<p>This dataset is a combination of samples from the MiraBest, FRGMRC and LRG catalogues. It is intended to serve as a benchmark for models' performance with respect to anomalous source detection in radio astronomy.</p>

On-chip phonon-enhanced IR near-field detection of molecular vibrations

<p>This dataset contains the source data and raw interferograms associated with our manuscript, <em>'On-chip phonon-enhanced IR near-field detection of molecular vibrations</em>' by A. Bylinkin et al. The source data, provided in an .xlsx file, includes the datasets used to generate the figures in both the main text and the Supplementary Information. The raw interferogram dataset, located in the <em>'Interferograms</em>' folder, was used to calculate the experimental spectra shown in Fig. 3b, f, and Suppl. Fig. 13. This dataset was acquired using a NeaSNOM microscope (attocube AG).</p> <p>The data processing procedure for calculating spectra from the interferograms is described in the Methods section of our manuscript.</p>