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

Produced by an detection model pretrained on MS COCO 2017. Automatically crops images of birds (Aves) to square dimensions centered around animal(s).  388,166 rows

EOL computer vision pipelines: Object Detection for Image Cropping: Multi-taxon

Produced by EOL Multitaxon Object Detection Model. Automatically crops images of snakes & lizards (Squamata), beetles (Coleoptera), frogs (Anura), and carnivores (Carnivora) to square dimensions centered around animal(s). Model available in the <a href="https://www.kaggle.com/models/eolorg/multitaxa-crops-thumbnails" target="_blank" rel="noopener">EOL Model Zoo on Kaggle</a>. <ul> <li>Anura = 42646 rows</li> <li>Coleoptera = 115276 rows</li> <li>Squamata = 132680 rows</li> <li>Carnivora = 31132 rows</li> </ul>

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

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>.   17,401 rows

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

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>.   608,163 rows

opencc-by-4.0Oct 2022View details →

A deep learning dataset for underwater object detection of tropical freshwater fish species in northern Australia

This dataset includes 44,112 images with 82,904 bounding box annotations for 23 tropical freshwater fish taxa from northern Australia.  Images were derived from Remote Underwater Video (RUV) deployments in deep channel and shallow lowland billabongs, Kakadu National Park, Northern Territory Australia. RUV deployments were conducted during the <a href="https://www.dcceew.gov.au/science-research/supervising-scientist">Supervising Scientists</a> annual fish monitoring program in the 2016, 2017 and 2018 recessional flow period (dry season). More information can be found <a href="https://www.dcceew.gov.au/sites/default/files/documents/ss-atr-2020-21.pdf">here</a>. <ul> <li>All images are in .jpg format and are 1920x1080 in dimension.</li> <li>Bounding box annotations are in COCO format. </li> </ul> Two .zip files are included: <ul> <li><a href="https://zenodo.org/api/files/990412db-e633-4f82-9b32-6990ef439ccd/202210-KakaduFishAI-CompactModel.zip">202210-KakaduFishAI-CompactModel.zip</a>: includes compact model weights in tensorflow format (.pb) trained using Azure's Custom Vision platform. This model is suitable for edge devices due to its reduced size. Code is provided to use the compact model for inferencing. </li> <li> <a href="https://zenodo.org/api/files/990412db-e633-4f82-9b32-6990ef439ccd/202210-KakaduFishAI-TrainingData.zip">202210-KakaduFishAI-TrainingData.zip</a>: includes all images and one COCO (.json) file with annotations. </li> </ul> Fish taxa include:  <ol> <li>Ambassis agrammus</li> <li>Ambassis macleayi</li> <li>Amniataba percoides</li> <li>Craterocephalus stercusmuscarum</li> <li>Denariusa bandata</li> <li>Glossamia aprion</li> <li>Glossogobius spp.</li> <li>Hephaestus fuliginosus</li> <li>Lates calcarifer</li> <li>Leiopotherapon unicolor</li> <li>Liza ordensis</li> <li>Megalops cyprinoides</li> <li>Melanotaenia nigrans</li> <li>Melanotaenia splendida inornata</li> <li>Mogurnda mogurnda</li> <li>Nemetalosa erebi</li> <li>Neoarius spp.</li> <li>Neosilurus spp.</li> <li>Oxyeleotris spp.</li> <li>Scleropages jardinii</li> <li>Strongylura kreffti</li> <li>Syncomistes butleri</li> <li>Toxotes chatareus</li> </ol> If you use this data for your own deep learning project we'd love to hear about how you used this dataset: andrew.jansen@environment.gov.au.

opencc-by-4.0Feb 2023View details →

Crowd simulation (CrowdSim2) for tracking and object detection

CrowdSim2 is an extension of crowd simulation tool designed in Unity for the purpose of generation massive synthetic data. Such a generated data from crowd simulation enables to validate various methods in terms of tracking multiple people and detect objects (in that example pedestrians and cars).   <table align="center"> <caption>Information summarizing number of folders, seconds and frames of data for different weather conditions</caption> <thead> <tr> <th scope="col">Condition</th> <th scope="col">Folders</th> <th scope="col">Seconds</th> <th scope="col">Frames</th> </tr> </thead> <tbody> <tr> <td>Sun</td> <td>2899</td> <td>86 970</td> <td>2 174 250</td> </tr> <tr> <td>Rain</td> <td>1633</td> <td>48 990</td> <td>1 224 750</td> </tr> <tr> <td>Fog</td> <td>1653</td> <td>49 590</td> <td>1 239 750</td> </tr> <tr> <td>Snow</td> <td>1646</td> <td>49 380</td> <td>1 234 500</td> </tr> </tbody> </table> Due to the limitations of the Zenodo platform, we could only include part of data here. If you are interested in the entire collection - please visit the project website: <a href="https://crowdsim.aei.polsl.pl/">CrowdSim</a> Acknowledgments  This work was supported by: European Union funds awarded to Blees Sp. z o.o. under grant POIR.01.01.01-00-0952/20-00 “Development of a system for analysing vision data captured by public transport vehicles interior monitoring, aimed at detecting undesirable situations/behaviours and passenger counting (including their classification by age group) and the objects they carry”); EC H2020 project “AI4media: a Centre of Excellence delivering next generation AI Research and Training at the service of Media, Society and Democracy” under GA 951911; research project (RAU-6, 2020) and projects for young scientists of the Silesian University of Technology (Gliwice, Poland); research project INAROS (INtelligenza ARtificiale per il mOnitoraggio e Supporto agli anziani), Tuscany POR FSE CUP B53D21008060008. Publication supported under the Excellence Initiative - Research University program implemented at the Silesian University of Technology, year 2022. This research was supported by the European Union from the European Social Fund in the framework of the project ”Silesian University of Technology as a Center of Modern Education based on research and innovation” POWR.03.05.00- 00-Z098/17 We are thankful for students participating in design of Crowd Simulator: Piotr Bartosz, Stanisław Wróbel, Marcin Wola, Angelika Gluch and Marek Matuszczyk.    Citing the Crowdsim 2 The Crowdsim 2 is released under a Creative Commons Attribution license, so please cite the Crowdsim 2 if it is used in your work in any form. Published academic papers should use the academic paper citation for our Crowdsim 2 paper, where we evaluated several pre-trained state-of-the-art object detectors focusing on the detection of the overboard people <pre><code class="language-markdown">TBA: Article citations using this dataset will appear here</code></pre> and this Zenodo Dataset <pre><code class="language-markdown">@dataset{crowdsim2_zenodo, title={Crowd simulation (CrowdSim2) for tracking and object detection}, DOI={10.5281/zenodo.7262220}, publisher={Zenodo}, author={Agnieszka Szczęsna and Paweł Foszner and Adam Cygan and Bartosz Bizoń and Michał Cogiel and Dominik Golba and Luca Ciampi and Nicola Messina and Elżbieta Macioszek and Michał Staniszewski}, year={2023}, month={Feb} }</code></pre>

opencc-by-4.0Dec 2022View details →

Image Dataset for Object Detection of Small Size Construction Tools

This is an image dataset established as input data for object detection model of small-sized construction tools. In the dataset, there are 12 classes of target tools  (bucket, cutter, drill, grinder, hammer, knife, saw, shovel, spanner, tacker, trowel, and wrench) which are typically used at indoor construction sites. 25,084 sets of image and the corresponding label data have been established and shared.   The diversity of objects in the images of the 12 small tools was considered by photographing tools of various shapes, sizes, and colors. In addition, to improve the model performance, images were also captured with various changes (e.g., image resolution, occlusion, lighting, and background). Among the 25,084 images in the dataset, 6,258 (25%) were obtained from the actual construction site.   Object annotations in each image were done by bounding boxes and were saved into a text file. The coordinates of the bounding box have the form of (Class, Center X, Center Y, Width, Height). Class refers to one of 12 construction tool types. Center X and Center Y are the center coordinates of the bounding box for an object from an image when the resolution of the image has min-max normalized. Width and Height are the width and height of the bounding box for an object, respectively, also from the image with the min-max normalized resolution.   The peer-reviewed publication for this dataset has now been published in " KSCE Journal of Civil Engineering" a Springer journal as follows: * Lee, K., Jeon, C., and Shin, D. (2023, In press) "Small Tool Image Database and Object Detection Approach for Indoor Construction Site Safety" KSCE Journal of Civil Engineering. DOI: https://doi.org/10.1007/s12205-022-1011-7 Please cite this reference when using the dataset.

opencc-zeroFeb 2023View details →

HIT-UAV: A high-altitude infrared thermal dataset for Unmanned Aerial Vehicle-based object detection

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opencc-zeroFeb 2023View details →

Object detection annotations on images from the Rijksmuseum

A dataset containing annotations made on images of cultural heritage digital objects from the Rijksmuseum . The annotations resulted from the application of automatic object detection techniques. This dataset was used for the evaluation of the object detection model developed by the project Saint George on a Bike. The service achieved a precision of 79.4% and a recall of 65.7% in this dataset. The dataset contains: <ul> <li>Object detection annotations (SgoaB-Rijksmuseum-objectDetection.zip): A ZIP archive containing the enrichments (as annotations) created by the SGoaB project. It contains 792 annotations on 315 images.</li> <li>Human-validated object detection annotations (SgoaB-Rijksmuseum-objectDetection-validatedSubset.zip): A ZIP archive containing the enrichments (as annotations) created by the SGoaB project. This data dump includes only the subset of the annotations that were considered correct after human validation. It contains 506 annotations on 283 images.</li> </ul>

opencc-by-4.0Sep 2023View details →

Data from: Automatic patient-level recognition of four Plasmodium species on thin blood smear by a Real Time Detector Transformer (RT-DETR) object detection algorithm: a proof-of-concept and evaluation

Automatic patient-level recognition of four Plasmodium species on thin blood smear by a Real Time Dectector Transformer (RT-DETR) object detection algorithm: a proof-of-concept and evaluation Emilie Guemas, Baptiste Routier, Théo Ghelfenstein-Ferreira, Camille Cordier, Sophie Hartuis, Bénédicte Marion, Sébastien Bertout, Emmanuelle Varlet-Marie, Damien Costa, Grégoire Pasquier Abstract: Malaria remains a global health problem with 247 million cases and 619,000 deaths in 2021. Diagnostic of Plasmodium species is important for administering the appropriate treatment. The gold-standard diagnosis from accurate species identification remains the thin blood smear. Nevertheless, this method is time-consuming and requires highly skilled and trained microscopists. To overcome these issues, new diagnostic tools based on deep learning are emerging. This study aimed to evaluate the performances of a RT-DETR (Real-Time Detection Transformer)object detection algorithm to discriminate Plasmodium species on thin blood smears images. The algorithm was trained and validated on a dataset consisting in 24,720 images from 475 thin blood smears corresponding to 2,002,597 labels. Performances were calculated with a test dataset of 4,508 images from 170 smears corresponding to 358,825labels coming from six French university hospital. At the patient level, the RT-DETR algorithm exhibited an overall accuracy of 79.4% (135/170) with a recall of 74% (40/54) and 81.9% (95/116) for negative and positive smears, respectively. Among Plasmodium positive smears, the global sensitivity was 82.7% (91/110) with a sensitivity of 90% (38/42), 81.8% (18/22) and 76.1% (35/46) for P. falciparum, P. malariae and P. ovale/vivax, respectively. The YOLOv5 model achieved a World Health Organization (WHO) competence level 2 for species identification. Besides, the RT-DETR algorithm may be run in real-time on low-cost devices such as a smartphone and could be suitable for deployment in low-resource setting areas where microscopy experts are lacking. Data collection: The training and validation dataset included 24,720 pictures taken from 475 manually May Grunwald-Giemsa (MGG)-stained thin blood smears from the Montpellier University Hospital collection and for a smaller part from the Toulouse University Hospital collection. In Montpellier, the pictures were taken with a Flexcam C1 microscope camera (Leica) attached to a Leica DM 2000 microscope and Leica DF450C microscope camera adapted with a Leica DM2500 microscope at X1000 magnification. Labelling of pictures was performed manually, and then automatically with manual correction with a Computer Visual Annotation Tools (CVAT) free software. Nine categories of labels were used: white blood cells (n=3,338), red blood cells (n=1,887,781), platelets (n=48,520), Trypanosoma brucei (n=2,773), and red blood cells infected by P. falciparum (n=43,545), P. ovale (n=4,651), P. vivax (n=4,115), P. malariae (n=2,849) and Babesia divergens (n=5,142). The test dataset included 4,508 pictures taken from 170 thin blood smears from the same number of patients from the Parasitology laboratories of University Hospitals of Montpellier, Toulouse, Rouen, Lille, Nantes and Saint-Louis in Paris (Table 1). Among these 170 patients, 54 were not infected, including two patients with Howell-Jolly bodies, and 116 were infected with hematozoa. For each patient, between 20 and 30 photos were taken from one thin blood smear with at least one hematozoan parasite per picture for infected patients. Accurate species diagnostic was made by a senior parasitologist, and for recent smears, it was confirmed by specific PCR, either performed locally (Toulouse) or at the Malaria French National Reference Center (Montpellier, Saint Louis, Rouen, Lille, Nantes).

openmit-licenseApr 2023View details →

ÖWF-OD - ÖWF Object Detection Dataset

This dataset contains object detection annotations and quality metadata of historic film with scientific and educational content. The content is from the collection <a href="https://www.mediathek.at/wissenschaft-als-film/die-sammlung-des-oewf/">"Österreichische Bundesinstitut für den Wissenschaftlichen Film (ÖWF)"</a> of the <a href="https://www.mediathek.at">Austrian Mediathek</a>. This is a snapshot of the dataset's repository at https://github.com/TailoredMediaProject/OEWF_ObjectDetection

openapache2.0Oct 2023View details →

Turfgrass Divot Dataset (Synthetic ) for divot detection object detection system

The dataset provided below has been synthetically created using Blender. A fundamental analysis on this data was conducted utilizing the YOLO V3 object detection technique to identify divots or areas of damage. Used for paper:  Advancing Turfgrass Maintenance with Synthetic Data for Divot Detection https://github.com/stevefoy/Turfgrass-Divot-Object-Detection <pre>@inproceedings{IMVIP2024, author = {Stephen Foy and Simon McLoughlin}, title = {Advancing Turfgrass Maintenance with Synthetic Data for Divot Detection}, booktitle = {Irish Machine Vision and Image Processing Conference (IMVIP)}, year = {2024} }</pre> Contents of the Zip File: <ul> <li> synthDivot_416x416 Folder: <ul> <li>Train and validation subfolders</li> <li>1200 RGB PNG images</li> <li>Corresponding masks for each image</li> <li>Bounding box data in YOLO <code>.txt</code> format</li> </ul> </li> <li> synthDivot_608x608 Folder: <ul> <li>Train and validation subfolders</li> <li>1200 RGB PNG images</li> <li>Bounding box data in YOLO <code>.txt</code> format</li> </ul> </li> </ul>

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Annotated spiral ganglion neuron training data for object detection

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publicJun 2025View details →

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Data from: Illusory speeding-up and slowing-down of objects moving at constant speed emerges from natural motion detection algorithms

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publicMar 2025View details →

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Common guillemots in the Baltic Sea studied with video surveillance and object detection: raw data, annotations, model, and model outputs

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publicDec 2022View details →

opencc-by-4.0Mar 2019View details →

Fluorescence Microscopy Data for Cellular Detection using Object Detection Networks.

This data accompanies work from the paper entitled:  Object Detection Networks and Augmented Reality for Cellular Detection in Fluorescence Microscopy Acquisition and Analysis. Waithe D1*,2,, Brown JM3, Reglinski K4,6,7,  Diez-Sevilla I5, Roberts D5, Christian Eggeling1,4,6,8 1 Wolfson Imaging Centre Oxford and 2 MRC WIMM Centre for Computational Biology and 3 MRC Molecular Haematology Unit and 4 MRC Human Immunology Unit, Weatherall Institute of Molecular Medicine, University of Oxford, OX3 9DS, Oxford, United Kingdom. 5 Nuffield Division of Clinical Laboratory Sciences, Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Headley Way, Oxford, OX3 9DU. 6 Institute of Applied Optics and Biophysics, Friedrich-Schiller-University Jena, Max-Wien Platz 4, 07743 Jena, Germany. 7 University Hospital Jena (UKJ), Bachstraße 18, 07743 Jena, Germany. 8 Leibniz Institute of Photonic Technology e.V., Albert-Einstein-Straße 9, 07745 Jena, Germany. Further details of these datasets can be found in the methods section of the above paper. Erythroblast DAPI (+glycophorin A): erythroblast cells were stained with DAPI and for glycophorin A protein (CD235a antibody, JC159 clone,  Dako) and with Alexa Fluor 488 secondary antibody (Invitrogen). DAPI staining was performed through using VectaShield Hard Set mounting solution with DAPI (Vector Lab). Num. of images used for training: 80 and testing: 80. Average number of cells per image: 4.5. Neuroblastoma phalloidin (+DAPI): images of neuroblastoma cells (N1E115) stained with phalloidin and DAPI were acquired from the Cell Image Library [26]. Cell images in the original dataset were acquired with a larger field of view than our system and so we divided each image into four sub-images and also created ROI bounding boxes for each of the cells in the image. The images were stained for FITC-phalloidin and DAPI. Num. of images used for training: 180, testing: 180. Average number of cells per image: 11.7. Fibroblast nucleopore: fibroblast (GM5756T) cells were stained for a nucleopore protein (anti-Nup153 mouse antibody, Abcam) and detected with anti-mouse Alexa Fluor 488. Num. of images for training: 26 and testing: 20. Average number of cells per image: 4.8. Eukaryote DAPI: eukaryote cells were stained with DAPI and fixed and mounted in Vectashield (Vector Lab). Num. of images for training: 40 and testing: 40. Average number of cells per image: 8.9. C127 DAPI: C127 cells were initially treated with a technique called RASER-FISH[27], stained with DAPI and fixed and mounted in Vectashield (Vector Lab). Num. of images for training: 30 and testing: 30. Average number of cells per image: 7.1. HEK peroxisome All: HEK-293 cells expressing peroxisome-localized GFP-SCP2 protein. Cells were transfected with GFP-SCP2 protein, which contains the PTS-1 localization signal, which redirects the fluorescently tagged protein to the actively importing peroxisomes[28]. Cells were fixed and mounted. Num. of images for training: 55 and testing: 55. Additionally we sub-categorised the cells as ‘punctuate’ and ‘non-punctuate’, where ‘punctuate’ would represent cells that have staining where the peroxisomes are discretely visible and ‘non-punctuate’ would be diffuse staining within the cell. The ‘HEK peroxisome All’ dataset contains ROI for all the cells: average number of cells per image: 7.9. The ‘HEK peroxisome’ dataset contains only those cells with punctuate fluorescence: average number of punctuate cells per image: 3.9. Erythroid DAPI All: Murine embryoid body-derived erythroid cells, differentiated from mES cells. Stained with DAPI and fixed and mounted in Vectashield (Vector Lab). Num. of images for training: 51 and testing: 50. Multinucleate cells are seen with this differentiation procedure. There is a variation in size of the nuclei (nuclei become smaller as differentiation proceeds). The smaller, 'late erythroid' nuclei contain heavily condensed DNA and often have heavy ‘blobs’ of heterochromatin visible. Apoptopic cells are also present, with apoptotic bodies clearly present. The ‘Erythroid DAPI All’ dataset contains ROI for all the cells in the image. Average number of cells per image: 21.5. The subset ‘Erythroid DAPI’ contains non-apoptotic cells only: average number of cells per image: 11.9 COS-7 nucleopore. Slides were acquired from GATTAquant. GATTA-Cells 1C are single color COS-7 cells stained for Nuclear pore complexes (Anti-Nup) and with Alexa Fluor 555 Fab(ab’)2 secondary stain. GATTA-Cells are embedded in ProLong Diamond. Num. of images for training: 50 and testing: 50. Average number of cells per image: 13.2 COS-7 nucleopore 40x. Same GATTA-Cells 1C slides (GATTAquant) as above but imaged on Nikon microscope, with 40x NA 0.6 objective. Num. of images for testing: 11.  Average number of cells per image: 31.6. COS-7 nucleopore 10x. Same GATTA-Cells 1C slides (GATTAquant)  as above but imaged on Nikon microscope, with 10x NA 0.25 objective. Num. of images for testing: 20. Average number of cells per image: 24.6 Dataset Annotation Datasets were annotated by a skilled user. These annotations represent the ground-truth of each image with bounding boxes (regions) drawn around each cell present within the staining. Annotations were produced using Fiji/ImageJ [29] ROI Manager and also through using the OMERO [30] ROI drawing interface (<a href="https://www.openmicroscopy.org/omero/">https://www.openmicroscopy.org/omero/</a>). The dataset labels were then converted into a format compatible with Faster-RCNN (Pascal), YOLOv2, YOLOv3 and also RetinaNet.  The scripts used to perform this conversion are documented in the repository (<a href="https://github.com/dwaithe/amca">https://github.com/dwaithe/amca</a>/scripts/).

openmit-licenseJan 2021View details →

Experimental Data for the Paper 'Hierarchical Fusion and Divergent Activation Based Weakly Supervised Learning for Object Detection from Remote Sensing Images'

Experimental Data for the Paper 'Hierarchical Fusion and Divergent Activation Based Weakly Supervised Learning for Object Detection from Remote Sensing Images' In this repository, we provide the implementation of the algorithms developed in the paper 'Hierarchical Fusion and Divergent Activation Based Weakly Supervised Learning for Object Detection from Remote Sensing Images' along with the experimental results, and the methods used for comparison. The goal is to provide the elements needed to validate and reproduce our research work as well as all the tools needed to reach the same conclusions as we did. The data used in our experiments that we have the copyright of [<a href="http://doi.org/10.1109/ACCESS.2020.3019956">A</a>],[<a href="http://doi.org/10.5281/zenodo.3843229">B</a>] is already published <a href="http://doi.org/10.5281/zenodo.3843229">on zenodo</a>. The licences valid for the elements of this repository are discussed under point "3. Licenses" below. 1. Structure The repository contains the following items: <ol> <li>"CODE_AND_RESULTS.zip" with the source codes and results of our method and the comparison methods,</li> <li>"README" - this text here.</li> <li>"LICENSE" - the <a href="https://mit-license.org/">MIT License</a></li> </ol> We now focus on the structure of the file CODE_AND_RESULTS.zip. It contains the following items: <ol> <li>The directory "new_methods" contains the source code and results of the new methods proposed in our paper.</li> <li>The directory "comparison" contains the source code of the two approaches used for comparison: ACoL [<a href="https://doi.org/10.1109/CVPR.2018.00144">A</a>] and DANet [<a href="http://doi.org/10.1109/ICCV.2019.00669">B</a>].</li> <li>The folder "tools_and_metrics" holds additional libraries, software tools, and metrics using in our experiments. </li> <li>"README" - this text here.</li> <li>"LICENSE" - the <a href="https://mit-license.org/">MIT License</a></li> </ol> Inside the folder "new_methods," the following sub-folders are provided: <ol> <li>"data" includes data loading code and code for how organizing the input data of the neural network.</li> <li>"expr" includes training code.</li> <li>"model" includes neural network model, basic network and additional modules, depending on the file name, including improved network, and comparison model.</li> <li>"utils" includes some used library functions and test codes when testing, including image segmentation, searching for the largest connected area and data visualization, etc. Verification on the WSADD dataset is done via test_airplane.py and on the DIOR dataset via val_model.py.</li> </ol> In our experiments, we used two datasets: "WSADD" [<a href="http://doi.org/10.1109/ACCESS.2020.3019956">A</a>],[<a href="http://doi.org/10.5281/zenodo.3843229">B</a>], which is already published <a href="http://doi.org/10.5281/zenodo.3843229">on zenodo</a> under the <a href="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</a> license. The "<a href="https://doi.org/10.1109/CVPR.2018.00144">DIOR</a>" proposed in [<a href="http://doi.org/10.1016/j.isprsjprs.2019.11.023">C</a>]. 2. References [<a href="http://doi.org/10.1109/ACCESS.2020.3019956">A</a>] Z.-Z. Wu, T. Weise, Y. Wang, Y. Wang, Convolutional neural network based weakly supervised learning for aircraft detection from remote sensing image, IEEE Access 8 (2020) 158097-158106. doi:<a href="http://doi.org/10.1109/ACCESS.2020.3019956">10.1109/ACCESS.2020.3019956</a>.    [<a href="http://doi.org/10.5281/zenodo.3843229">B</a>] Z.-Z. Wu. Weakly Supervised Airplane Detection Dataset: WSADD. May 2020. zenodo.org. doi:<a href="http://doi.org/10.5281/zenodo.3843229">10.5281/zenodo.3843229</a>. [<a href="http://doi.org/10.1016/j.isprsjprs.2019.11.023">C</a>] K. Li, G. Wan, G. Cheng, L. Meng, J. Han, Object detection in optical remote sensing images: A survey and a new benchmark, ISPRS Journal of Photogrammetry and Remote Sensing 159 (2020) 296-307. doi:<a href="http://doi.org/10.1016/j.isprsjprs.2019.11.023">10.1016/j.isprsjprs.2019.11.023</a>.    [<a href="https://doi.org/10.1109/CVPR.2018.00144">D</a>] X. Zhang, Y. Wei, J. Feng, Y. Yang, T. S. Huang, Adversarial complementary learning for weakly supervised object localization, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR'18), Jun. 18-22, 2018, Salt Lake City, UT, USA, IEEE Computer Society, 2018, pp. 1325-1334. doi:<a href="https://doi.org/10.1109/CVPR.2018.00144">10.1109/CVPR.2018.00144</a>.    [<a href="http://doi.org/10.1109/ICCV.2019.00669">E</a>] H. Xue, C. Liu, F. Wan, J. Jiao, X. Ji, Q. Ye, DANet: Divergent activation for weakly supervised object localization, in: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV'19), Oct. 27-Nov. 2, 2019, Seoul, Korea, IEEE, 2019, pp. 6588-6597. doi:<a href="http://doi.org/10.1109/ICCV.2019.00669">10.1109/ICCV.2019.00669</a>. 3. Licenses The following licenses apply for the files and folders in the archive "CODE_AND_RESULTS.zip": <ul> <li>The files in the folder `new_methods` are under the <a href="https://mit-license.org/">MIT License</a>.</li> <li>The files in the folder `comparison/ACoL` have been obtained from https://github.com/xiaomengyc/ACoL, which is under the <a href="https://mit-license.org/">MIT License</a>.</li> <li>We put our code and data under the </li> <li>The files in the folder "comparison/DANet" have been obtained from <a href="https://github.com/xuehaolan/DANet">https://github.com/xuehaolan/DANet</a>, which is an open source project without associated license at the time of this writing. They will therefore remain under the copyright of the user <a href="https://github.com/xuehaolan/">https://github.com/xuehaolan/</a>.</li> <li>The files in the folder "tools_and_metrics/detections_DIOR" are related to the repository <a href="https://github.com/rafaelpadilla/Object-Detection-Metrics">https://github.com/rafaelpadilla/Object-Detection-Metrics</a>, which is under the <a href="https://mit-license.org/">MIT License</a>, and therefore are under the same license.</li> <li>The files in the folder "tools_and_metrics/Nest-pytorch" are based on the repository <a href="https://github.com/ZhouYanzhao/Nest">https://github.com/ZhouYanzhao/Nest</a>, which is under the <a href="https://mit-license.org/">MIT License</a>.</li> <li>The files in the folder "tools_and_metrics/PRM-pytorch" are based on the repository <a href="https://github.com/ZhouYanzhao/PRM">https://github.com/ZhouYanzhao/PRM</a>, which is an open source project without associated license at the time of this writing. They will therefore remain under the copyright of the user <a href="https://github.com/ZhouYanzhao/">https://github.com/ZhouYanzhao/</a>.</li> </ul> The <a href="https://mit-license.org/">MIT License</a> is included here as file "LICENSE". 4. Contact 1. Dr. <a href="http://iao.hfuu.edu.cn/146">Zhize WU</a>, wuzz@hfuu.edu.cn 2. Dr. <a href="http://iao.hfuu.edu.cn/5">Thomas WEISE</a>, tweise@hfuu.edu.cn, tweise@ustc.edu.cn Institute of Applied Optimization,    School of Artificial Intelligence and Big Data,    Hefei University, South Campus 2, Jinxiu Dadao 99,    Hefei Economic and Technological Development Area,    Shushan District, Hefei 230601, Anhui, China

opencc-by-nc-nd-4.0Oct 2023View details →

Himax Dataset for Object Detection of Bottles and Tin-Cans

Object detection Dataset was collected with a Himax HM01B0 greyscale camera. The datasets contain QVGA images of Bottles and Tin-Cans and their respective labels. The labels follow the PascalVOC format specification. The dataset also include tfrecod files for ease of use with tensorflow. This dataset was used in our paper Bio-inspired Autonomous Exploration Policies with CNN-based Object Detection on Nano-drones

opencc-by-4.0Sep 2024View details →

Tiny Robotics Dataset and Benchmark for Continual Object Detection

Dataset for TiROD: Tiny Robotics Dataset and Benchmark for Continual Object Detection Official Website -> <a href="https://pastifra.github.io/TiROD/">https://pastifra.github.io/TiROD/</a> Code -> <a href="https://github.com/pastifra/TiROD_code">https://github.com/pastifra/TiROD_code</a> Video -> <a href="https://www.youtube.com/watch?v=e76m3ol1i4I">https://www.youtube.com/watch?v=e76m3ol1i4I</a> Paper -> <a href="https://arxiv.org/abs/2409.16215">https://arxiv.org/abs/2409.16215</a>