The aim of this competition is to predict the localization of plant and animal species.
\nThis competition is held jointly as part of:
\nPredict the presence of species using observations paired with aerial images and environmental features.
\nStart Date - 10 March 2021
\nEnd Date - 28th May 2021
\nKaggle URL - https://www.kaggle.com/c/geolifeclef-2021
The goal of this competition is to push the state of the art in supervised image classification for real world data that features a large number of fine-grained categories.
\nStart Date - 10th March 2021
\nEnd Date - 31st May 2021
\nKaggle URL - https://www.kaggle.com/c/semi-inat-2021
The Metropolitan Museum of Art in New York, also known as The Met, has a diverse collection of over 1.5M objects of which over 200K have been digitized with imagery. Can you help find the significant attributes to identify a specific work of art? Help advance this research in this notebook competition.
\nStart Date - 10th March 2021
\nEnd Date - 31st May 2021
\nKaggle URL - https://www.kaggle.com/c/imet-2021-fgvc8
As part of the FGVC8 workshop at CVPR 2021 we are conducting the iMaterialist - Fashion track 2021 apparel instance segmentation & fine-grained attribute classification competition. The goal of this competition is to push the state of the art of the novel fine-grained segmentation task by joining forces between the fashion and computer vision communities.
\nStart Date - Coming Soon
\nEnd Date - Coming Soon
\nKaggle URL - Coming Soon
Through this competition, we challenge machine learning researchers and practitioners to develop algorithms that will help with an important step in understanding patterns of behavior in trafficking scenarios, and explore the ability of algorithms for real-world use cases with a large number of classes.
\nStart Date - 10th March 2021
\nEnd Date - 26th May 2021
\nKaggle URL - https://www.kaggle.com/c/hotel-id-2021-fgvc8
There are approximately 400,000 known vascular plant species with an estimated 80,000 still to be discovered. Herbaria worldwide contain an overwhelming amount of unnamed and new specimens, and with the threats of climate change, we need new tools to quicken the pace of species discovery.
\nStart Date - 10th March 2021
\nEnd Date - 26th May 2021
\nKaggle URL - https://www.kaggle.com/c/herbarium-2021-fgvc8
Camera Traps (or Wild Cams) enable the automatic collection of large quantities of image data. Biologists all over the world use camera traps to monitor biodiversity and population density of animal species. We have recently been making strides towards automatic species classification in camera trap images, and in iWildCam 2020 we focused on generalization to novel camera locations across the globe. However, in order to estimate the abundance and density of species in camera trap data, biologists need to know how many of each species was seen.
\nStart Date - 10 March 2021
\nEnd Date - 28th May 2021
\nKaggle URL - https://www.kaggle.com/c/iwildcam2021-fgvc8
The main objectives of the competition is to develop machine learning-based models; 1) To accurately classify the given leaf image from the test dataset to particular disease category, 2) To develop an algorithm for accurate estimation of disease severity of each image/disease category; and 3) To identify individual disease from multiple disease symptoms on a single leaf image.
\nStart Date - March 15, 2021
\nEnd Date - May 26, 2021
\nKaggle URL - https://www.kaggle.com/c/plant-pathology-2021-fgvc8
The aim of this competition is to predict the localization of plant and animal species.
\nThis competition is held jointly as part of:
\nPredict the presence of species using observations paired with aerial images and environmental features.
\nStart Date - 10 March 2021
\nEnd Date - 28th May 2021
\nKaggle URL - https://www.kaggle.com/c/geolifeclef-2021
The goal of this competition is to push the state of the art in supervised image classification for real world data that features a large number of fine-grained categories.
\nStart Date - 10th March 2021
\nEnd Date - 31st May 2021
\nKaggle URL - https://www.kaggle.com/c/semi-inat-2021
The Metropolitan Museum of Art in New York, also known as The Met, has a diverse collection of over 1.5M objects of which over 200K have been digitized with imagery. Can you help find the significant attributes to identify a specific work of art? Help advance this research in this notebook competition.
\nStart Date - 10th March 2021
\nEnd Date - 31st May 2021
\nKaggle URL - https://www.kaggle.com/c/imet-2021-fgvc8
As part of the FGVC8 workshop at CVPR 2021 we are conducting the iMaterialist - Fashion track 2021 apparel instance segmentation & fine-grained attribute classification competition. The goal of this competition is to push the state of the art of the novel fine-grained segmentation task by joining forces between the fashion and computer vision communities.
\nStart Date - Coming Soon
\nEnd Date - Coming Soon
\nKaggle URL - Coming Soon
Through this competition, we challenge machine learning researchers and practitioners to develop algorithms that will help with an important step in understanding patterns of behavior in trafficking scenarios, and explore the ability of algorithms for real-world use cases with a large number of classes.
\nStart Date - 10th March 2021
\nEnd Date - 26th May 2021
\nKaggle URL - https://www.kaggle.com/c/hotel-id-2021-fgvc8
There are approximately 400,000 known vascular plant species with an estimated 80,000 still to be discovered. Herbaria worldwide contain an overwhelming amount of unnamed and new specimens, and with the threats of climate change, we need new tools to quicken the pace of species discovery.
\nStart Date - 10th March 2021
\nEnd Date - 26th May 2021
\nKaggle URL - https://www.kaggle.com/c/herbarium-2021-fgvc8
Camera Traps (or Wild Cams) enable the automatic collection of large quantities of image data. Biologists all over the world use camera traps to monitor biodiversity and population density of animal species. We have recently been making strides towards automatic species classification in camera trap images, and in iWildCam 2020 we focused on generalization to novel camera locations across the globe. However, in order to estimate the abundance and density of species in camera trap data, biologists need to know how many of each species was seen.
\nStart Date - 10 March 2021
\nEnd Date - 28th May 2021
\nKaggle URL - https://www.kaggle.com/c/iwildcam2021-fgvc8
The main objectives of the competition is to develop machine learning-based models; 1) To accurately classify the given leaf image from the test dataset to particular disease category, 2) To develop an algorithm for accurate estimation of disease severity of each image/disease category; and 3) To identify individual disease from multiple disease symptoms on a single leaf image.
\nStart Date - March 15, 2021
\nEnd Date - May 26, 2021
\nKaggle URL - https://www.kaggle.com/c/plant-pathology-2021-fgvc8
Thank you @gpreda for sharing. This is an important toolset. I will also like it if you can share good Geospatial data handling resources.
", "rawMarkdown": "Thank you @gpreda for sharing. This is an important toolset. I will also like it if you can share good Geospatial data handling resources.", "votes": null } ], "comments": [ { "id": 1252346, "author_name": "olusesiadebisi", "author_url": "", "post_date": "03/25/2021 15:52:04", "content": "Thank you @gpreda for sharing. This is an important toolset. I will also like it if you can share good Geospatial data handling resources.
", "votes": null, "replies": [] } ], "raw_markdown_by_id": { "1247166": "### Introduction\n\nThe aim of this competition is to predict the localization of plant and animal species.\n\nThis competition is held jointly as part of:\n\n* [LifeCLEF 2021](https://www.imageclef.org/LifeCLEF2021) lab of the [CLEF 2021](http://clef2021.clef-initiative.eu/) conference;\n* [FGVC8](https://sites.google.com/view/fgvc8) workshop organized in conjunction with [CVPR 2021](http://cvpr2021.thecvf.com/) conference.\n\n\n### Previous **GeoLifeCLEF** Competitions\n\n* 2010: [ImageCLEF 2010](https://www.imageclef.org/2010)\n* 2011: [ImageCLEF 2011](https://www.imageclef.org/2011)\n* 2012: [ImageCLEF 2012](https://www.imageclef.org/2012)\n* 2013: [ImageCLEF 2013](https://www.imageclef.org/2013)\n* 2014: [LifeCLEF 2014 - ImageCLEF/LifeCLEF - Multimedia Retrieval in CLEF](https://www.imageclef.org/2014)\n* 2015: [LifeCLEF 2015 - ImageCLEF/LifeCLEF - Multimedia Retrieval in CLEF](https://www.imageclef.org/lifeclef/2015)\n* 2016: [LifeCLEF 2016 - ImageCLEF/LifeCLEF - Multimedia Retrieval in CLEF](https://www.imageclef.org/lifeclef/2016)\n* 2017: [Location Based Species Recommendation - GeoLifeCLEF 2017](https://www.imageclef.org/lifeclef/2017/GeoLifeCLEF)\n* 2018: [GeoLifeCLEF 2018 - ImageCLEF, LifeCLEF - Multimedia Retrieval in CLEF](https://www.imageclef.org/node/229)\n* 2019: [Location Based Species Recommendation - GeoLifeCLEF 2019](https://www.imageclef.org/GeoLifeCLEF2019)\n* 2020: [Location Based Species Recommendation - GeoLifeCLEF 2020](https://www.imageclef.org/GeoLifeCLEF2020)\n\n\n### FGVC8 Competitions\n\n##### GeoLifeCLEF2021\nPredict the presence of species using observations paired with aerial images and environmental features.\nStart Date - 10 March 2021\nEnd Date - 28th May 2021\nKaggle URL - https://www.kaggle.com/c/geolifeclef-2021 \n\n##### Semi-Supervised iNaturalist Challenge\nThe goal of this competition is to push the state of the art in supervised image classification for real world data that features a large number of fine-grained categories.\nStart Date - 10th March 2021\nEnd Date - 31st May 2021\t\nKaggle URL - https://www.kaggle.com/c/semi-inat-2021\n\n\n##### iMet2021\nThe Metropolitan Museum of Art in New York, also known as The Met, has a diverse collection of over 1.5M objects of which over 200K have been digitized with imagery. Can you help find the significant attributes to identify a specific work of art? Help advance this research in this notebook competition.\nStart Date - 10th March 2021\nEnd Date - 31st May 2021\t\nKaggle URL - https://www.kaggle.com/c/imet-2021-fgvc8\n\n\n##### iMat Fashion 2021\nAs part of the FGVC8 workshop at CVPR 2021 we are conducting the iMaterialist - Fashion track 2021 apparel instance segmentation & fine-grained attribute classification competition. The goal of this competition is to push the state of the art of the novel fine-grained segmentation task by joining forces between the fashion and computer vision communities.\nStart Date - Coming Soon\nEnd Date - Coming Soon\nKaggle URL - Coming Soon\n\n\n##### Hotel-ID 2021\nThrough this competition, we challenge machine learning researchers and practitioners to develop algorithms that will help with an important step in understanding patterns of behavior in trafficking scenarios, and explore the ability of algorithms for real-world use cases with a large number of classes.\nStart Date - 10th March 2021\nEnd Date - 26th May 2021\nKaggle URL - https://www.kaggle.com/c/hotel-id-2021-fgvc8\n\n##### Herbarium 2021: Half-Earth Challenge\nThere are approximately 400,000 known vascular plant species with an estimated 80,000 still to be discovered. Herbaria worldwide contain an overwhelming amount of unnamed and new specimens, and with the threats of climate change, we need new tools to quicken the pace of species discovery.\nStart Date - 10th March 2021\nEnd Date - 26th May 2021\nKaggle URL - https://www.kaggle.com/c/herbarium-2021-fgvc8\n\n\n##### iWildCam 2021\nCamera Traps (or Wild Cams) enable the automatic collection of large quantities of image data. Biologists all over the world use camera traps to monitor biodiversity and population density of animal species. We have recently been making strides towards automatic species classification in camera trap images, and in iWildCam 2020 we focused on generalization to novel camera locations across the globe. However, in order to estimate the abundance and density of species in camera trap data, biologists need to know how many of each species was seen. \nStart Date - 10 March 2021\nEnd Date - 28th May 2021\nKaggle URL - https://www.kaggle.com/c/iwildcam2021-fgvc8\n\n\n##### Plant Pathology Challenge 2021\nThe main objectives of the competition is to develop machine learning-based models; 1) To accurately classify the given leaf image from the test dataset to particular disease category, 2) To develop an algorithm for accurate estimation of disease severity of each image/disease category; and 3) To identify individual disease from multiple disease symptoms on a single leaf image. \nStart Date - March 15, 2021\nEnd Date - May 26, 2021\nKaggle URL - https://www.kaggle.com/c/plant-pathology-2021-fgvc8", "1252346": "Thank you @gpreda for sharing. This is an important toolset. I will also like it if you can share good Geospatial data handling resources." }, "source": "meta" }