{ "id": 290353, "title": "Useful External Datasets, Models and Resources ", "url": "/competitions/tensorflow-great-barrier-reef/discussion/290353", "author_name": "", "post_date": "2021-11-24T08:28:53.988699300Z", "votes": 90, "comment_count": 3, "views": 0, "content": "

Papers, Models, Datasets, and other resources on Underwater Image/starfish/reefs, etc.

\n

A. General Underwater Image Object Detection/Classification

\n\n

B. Coral-reef Underwater Image Object Detection

\n\n

D. Fish Underwater Image Object Detection

\n\n

Underwater Image Enhancement, Color Correction/Restoration

\n\n

Underwater Image Segmentation

\n\n

Underwater Salient Object Detection

\n\n

The original Source of this Collection is: https://github.com/xahidbuffon/Awesome_Underwater_Datasets
\nCredit: @xahidbuffon

", "messages": [ { "id": "1593754", "postDate": "11/24/2021 08:28:53", "content": "

Papers, Models, Datasets, and other resources on Underwater Image/starfish/reefs, etc.

\n

A. General Underwater Image Object Detection/Classification

\n\n

B. Coral-reef Underwater Image Object Detection

\n\n

D. Fish Underwater Image Object Detection

\n\n

Underwater Image Enhancement, Color Correction/Restoration

\n\n

Underwater Image Segmentation

\n\n

Underwater Salient Object Detection

\n\n

The original Source of this Collection is: https://github.com/xahidbuffon/Awesome_Underwater_Datasets
\nCredit: @xahidbuffon

", "rawMarkdown": "Papers, Models, Datasets, and other resources on Underwater Image/starfish/reefs, etc. \n\n#### A. General Underwater Image Object Detection/Classification\n- MOUSS data: [Data](https://www.viametoolkit.org/cvpr-2018-workshop-data-challenge/challenge-data-description/). (CVPR 2018 workshop challenge)\n- MBARI databse: [Data](https://www.mbari.org/products/data-repository/).\n- HabCam database: [Data](https://habcam.whoi.edu/).\n- OUC-vision: [Paper](https://ieeexplore.ieee.org/abstract/document/8019324).\n- MARIS project: [Data](http://rimlab.ce.unipr.it/Maris.html).\n- NOAA data: [Data](https://marineresearchpartners.com/nmfs_aiasi/DataSets.html).\n- Aqualoc dataset: [Data](http://www.lirmm.fr/aqualoc/), [Paper](https://arxiv.org/abs/1910.14532). (visual-inertial-pressure localization)\n- Brackish dataset: [Data](https://www.kaggle.com/aalborguniversity/brackish-dataset/data), [Paper](https://www.researchgate.net/publication/333972548_Detection_of_Marine_Animals_in_a_New_Underwater_Dataset_with_Varying_Visibility).\n- SUN database (underwater scenes): [Data](http://groups.csail.mit.edu/vision/SUN/).\n\n\n#### B. Coral-reef Underwater Image Object Detection\n- Moorea corals (UCSD): [Data](http://vision.ucsd.edu/content/moorea-labeled-corals), [Paper](https://ieeexplore.ieee.org/abstract/document/6247798). \n- Coral-reef Puerto Rico: [Data](https://web.whoi.edu/singh/underwater-imaging/datasets/coral-reef-puerto-rico/).\n- Coral-Net: [Data](https://coralnet.ucsd.edu/).\n\n\n#### D. Fish Underwater Image Object Detection\n- WildFish database: [Data](https://github.com/PeiqinZhuang/WildFish), [Paper](https://dl.acm.org/citation.cfm?id=3240616).\n- Labeled fishes: [Data](https://swfscdata.nmfs.noaa.gov/labeled-fishes-in-the-wild/), [Paper](https://ieeexplore.ieee.org/abstract/document/7046815).\n- Fish4Knowledge data: [Data](http://homepages.inf.ed.ac.uk/rbf/Fish4Knowledge/).\n- Fish database: [Data](http://www.fishdb.co.uk/).\n- AQUALIFEIMAGES database: [Data](http://www.aqualifeimages.com/).\n- Rockfish: [Data](https://web.whoi.edu/singh/underwater-imaging/datasets/rockfish/).\n- Fish recognition data: [Data](http://groups.inf.ed.ac.uk/f4k/GROUNDTRUTH/RECOG/), [Paper](https://homepages.inf.ed.ac.uk/rbf/PAPERS/PID2432553.pdf).\n- Oceanwide images: [Data](http://www.oceanwideimages.com/).\n- Fish detection and tracking: [Data](http://www.perceivelab.com/index-dataset.php?name=Fish_Detection), [Paper](http://groups.inf.ed.ac.uk/f4k/PAPERS/MTAP-Perla.pdf).\n- Fish trajectory detection: [Data](http://groups.inf.ed.ac.uk/f4k/GROUNDTRUTH/BEHAVIOR/), [Paper](http://www.bmva.org/bmvc/2013/Papers/paper0021/paper0021.pdf).\n\n#### Underwater Image Enhancement, Color Correction/Restoration\n- EUVP dataset: [Data](http://irvlab.cs.umn.edu/resources/euvp-dataset), [Paper](https://arxiv.org/abs/1903.09766), [Code](https://github.com/xahidbuffon/funie-gan). (paired and unpaired data; FUnIE-GAN)\n- Underwater imagenet: [Data](http://irvlab.cs.umn.edu/resources/), [Paper](https://ieeexplore.ieee.org/document/8460552), [Code](https://github.com/cameronfabbri/Underwater-Color-Correction). (paired data; UGAN)\n- UIEBD dataset: [Data](https://li-chongyi.github.io/proj_benchmark.html), [Paper](https://arxiv.org/abs/1901.05495), [Code](https://github.com/Li-Chongyi/Water-Net_Code). (Water-Net)\n- SQUID dataset: [Data](http://csms.haifa.ac.il/profiles/tTreibitz/datasets/ambient_forwardlooking/index.html), [Paper](https://arxiv.org/abs/1811.01343), [Code](https://github.com/danaberman/underwater-hl). (Underwater-HL)\n- U-45: [Data](https://github.com/IPNUISTlegal/underwater-test-dataset-U45-), [Paper](https://arxiv.org/abs/1906.06819). (UDAE)\n- RUIE benchmark: [Data](https://github.com/dlut-dimt/Realworld-Underwater-Image-Enhancement-RUIE-Benchmark), [Paper](https://arxiv.org/abs/1901.05320). (RUIE-Net)\n- Jamaica port royal: [Data](https://github.com/kskin/data), [Paper](https://arxiv.org/abs/1702.07392), [Code](https://github.com/kskin/WaterGAN/). (Water-GAN)\n- Virtual periscope: [Data](http://webee.technion.ac.il/~yoav/research/random_distort.html), [Paper](https://ieeexplore.ieee.org/abstract/document/7448905).\n- Color correction: [Data](https://web.whoi.edu/singh/underwater-imaging/datasets/color-correction/).\n- Color restoration: [Data](http://csms.haifa.ac.il/profiles/tTreibitz/datasets/ambient_forwardlooking/index.html), [Paper](https://arxiv.org/abs/1811.01343), [Code](https://github.com/danaberman/underwater-hl).\n- TURBID data: [Data](http://amandaduarte.com.br/turbid/), [Paper](https://ieeexplore.ieee.org/abstract/document/7485524).\n- OceanDark dataset: [Data](https://sites.google.com/view/oceandark/home), [Paper](https://www.mdpi.com/2313-433X/5/10/79).\n\n#### Underwater Image Segmentation\n- SUIM: [Data](http://irvlab.cs.umn.edu/resources/suim-dataset), [Paper](https://arxiv.org/pdf/2004.01241.pdf), [Code](https://github.com/xahidbuffon/SUIM-Net). (SUIM-Net)\n- Coral-Net: [Data](https://coralnet.ucsd.edu/), [Paper](https://onlinelibrary.wiley.com/doi/full/10.1002/rob.21915), [Code](https://github.com/Shathe/CoralSeg). (Coral-Seg)\n- Eilat dataset: [Data](https://sites.google.com/a/unizar.es/semanticseg/), [Paper](https://www.nature.com/articles/srep23166.pdf).\n- Change detection: [Data](http://underwaterchangedetection.eu/index.html), [Paper](https://ieeexplore.ieee.org/document/7761129).\n\n#### Underwater Salient Object Detection\n- UFO-120: [Data](http://irvlab.cs.umn.edu/resources/ufo-120-dataset), [Paper](https://arxiv.org/pdf/2002.01155.pdf), [Code](https://github.com/xahidbuffon/Deep-SESR)\n- MUED database: [Data-1](https://zenodo.org/record/2542305#.X0YMt3UzY5k), [Data-2](https://zenodo.org/record/2542307#.X0YM53UzY5k), [Paper](https://www.sciencedirect.com/science/article/abs/pii/S1568494619302169)\n\n\n\nThe original Source of this Collection is: https://github.com/xahidbuffon/Awesome_Underwater_Datasets\nCredit: @xahidbuffon", "votes": null }, { "id": "1599424", "postDate": "11/29/2021 12:29:28", "content": "

Wow! This is great for researching. Thank you!

", "rawMarkdown": "Wow! This is great for researching. Thank you!", "votes": null }, { "id": "1617951", "postDate": "12/14/2021 14:21:02", "content": "

Did you add external data?

", "rawMarkdown": "Did you add external data?", "votes": null }, { "id": "1618163", "postDate": "12/14/2021 17:54:24", "content": "

No. So far I am trying to find best CV split.

", "rawMarkdown": "No. So far I am trying to find best CV split.", "votes": null } ], "comments": [ { "id": 1599424, "author_name": "remekkinas", "author_url": "", "post_date": "11/29/2021 12:29:28", "content": "

Wow! This is great for researching. Thank you!

", "votes": null, "replies": [ { "id": 1617951, "author_name": "dragonzhang", "author_url": "", "post_date": "12/14/2021 14:21:02", "content": "

Did you add external data?

", "votes": null, "replies": [] }, { "id": 1618163, "author_name": "remekkinas", "author_url": "", "post_date": "12/14/2021 17:54:24", "content": "

No. So far I am trying to find best CV split.

", "votes": null, "replies": [] } ] } ], "raw_markdown_by_id": { "1593754": "Papers, Models, Datasets, and other resources on Underwater Image/starfish/reefs, etc. \n\n#### A. General Underwater Image Object Detection/Classification\n- MOUSS data: [Data](https://www.viametoolkit.org/cvpr-2018-workshop-data-challenge/challenge-data-description/). (CVPR 2018 workshop challenge)\n- MBARI databse: [Data](https://www.mbari.org/products/data-repository/).\n- HabCam database: [Data](https://habcam.whoi.edu/).\n- OUC-vision: [Paper](https://ieeexplore.ieee.org/abstract/document/8019324).\n- MARIS project: [Data](http://rimlab.ce.unipr.it/Maris.html).\n- NOAA data: [Data](https://marineresearchpartners.com/nmfs_aiasi/DataSets.html).\n- Aqualoc dataset: [Data](http://www.lirmm.fr/aqualoc/), [Paper](https://arxiv.org/abs/1910.14532). (visual-inertial-pressure localization)\n- Brackish dataset: [Data](https://www.kaggle.com/aalborguniversity/brackish-dataset/data), [Paper](https://www.researchgate.net/publication/333972548_Detection_of_Marine_Animals_in_a_New_Underwater_Dataset_with_Varying_Visibility).\n- SUN database (underwater scenes): [Data](http://groups.csail.mit.edu/vision/SUN/).\n\n\n#### B. Coral-reef Underwater Image Object Detection\n- Moorea corals (UCSD): [Data](http://vision.ucsd.edu/content/moorea-labeled-corals), [Paper](https://ieeexplore.ieee.org/abstract/document/6247798). \n- Coral-reef Puerto Rico: [Data](https://web.whoi.edu/singh/underwater-imaging/datasets/coral-reef-puerto-rico/).\n- Coral-Net: [Data](https://coralnet.ucsd.edu/).\n\n\n#### D. Fish Underwater Image Object Detection\n- WildFish database: [Data](https://github.com/PeiqinZhuang/WildFish), [Paper](https://dl.acm.org/citation.cfm?id=3240616).\n- Labeled fishes: [Data](https://swfscdata.nmfs.noaa.gov/labeled-fishes-in-the-wild/), [Paper](https://ieeexplore.ieee.org/abstract/document/7046815).\n- Fish4Knowledge data: [Data](http://homepages.inf.ed.ac.uk/rbf/Fish4Knowledge/).\n- Fish database: [Data](http://www.fishdb.co.uk/).\n- AQUALIFEIMAGES database: [Data](http://www.aqualifeimages.com/).\n- Rockfish: [Data](https://web.whoi.edu/singh/underwater-imaging/datasets/rockfish/).\n- Fish recognition data: [Data](http://groups.inf.ed.ac.uk/f4k/GROUNDTRUTH/RECOG/), [Paper](https://homepages.inf.ed.ac.uk/rbf/PAPERS/PID2432553.pdf).\n- Oceanwide images: [Data](http://www.oceanwideimages.com/).\n- Fish detection and tracking: [Data](http://www.perceivelab.com/index-dataset.php?name=Fish_Detection), [Paper](http://groups.inf.ed.ac.uk/f4k/PAPERS/MTAP-Perla.pdf).\n- Fish trajectory detection: [Data](http://groups.inf.ed.ac.uk/f4k/GROUNDTRUTH/BEHAVIOR/), [Paper](http://www.bmva.org/bmvc/2013/Papers/paper0021/paper0021.pdf).\n\n#### Underwater Image Enhancement, Color Correction/Restoration\n- EUVP dataset: [Data](http://irvlab.cs.umn.edu/resources/euvp-dataset), [Paper](https://arxiv.org/abs/1903.09766), [Code](https://github.com/xahidbuffon/funie-gan). (paired and unpaired data; FUnIE-GAN)\n- Underwater imagenet: [Data](http://irvlab.cs.umn.edu/resources/), [Paper](https://ieeexplore.ieee.org/document/8460552), [Code](https://github.com/cameronfabbri/Underwater-Color-Correction). (paired data; UGAN)\n- UIEBD dataset: [Data](https://li-chongyi.github.io/proj_benchmark.html), [Paper](https://arxiv.org/abs/1901.05495), [Code](https://github.com/Li-Chongyi/Water-Net_Code). (Water-Net)\n- SQUID dataset: [Data](http://csms.haifa.ac.il/profiles/tTreibitz/datasets/ambient_forwardlooking/index.html), [Paper](https://arxiv.org/abs/1811.01343), [Code](https://github.com/danaberman/underwater-hl). (Underwater-HL)\n- U-45: [Data](https://github.com/IPNUISTlegal/underwater-test-dataset-U45-), [Paper](https://arxiv.org/abs/1906.06819). (UDAE)\n- RUIE benchmark: [Data](https://github.com/dlut-dimt/Realworld-Underwater-Image-Enhancement-RUIE-Benchmark), [Paper](https://arxiv.org/abs/1901.05320). (RUIE-Net)\n- Jamaica port royal: [Data](https://github.com/kskin/data), [Paper](https://arxiv.org/abs/1702.07392), [Code](https://github.com/kskin/WaterGAN/). (Water-GAN)\n- Virtual periscope: [Data](http://webee.technion.ac.il/~yoav/research/random_distort.html), [Paper](https://ieeexplore.ieee.org/abstract/document/7448905).\n- Color correction: [Data](https://web.whoi.edu/singh/underwater-imaging/datasets/color-correction/).\n- Color restoration: [Data](http://csms.haifa.ac.il/profiles/tTreibitz/datasets/ambient_forwardlooking/index.html), [Paper](https://arxiv.org/abs/1811.01343), [Code](https://github.com/danaberman/underwater-hl).\n- TURBID data: [Data](http://amandaduarte.com.br/turbid/), [Paper](https://ieeexplore.ieee.org/abstract/document/7485524).\n- OceanDark dataset: [Data](https://sites.google.com/view/oceandark/home), [Paper](https://www.mdpi.com/2313-433X/5/10/79).\n\n#### Underwater Image Segmentation\n- SUIM: [Data](http://irvlab.cs.umn.edu/resources/suim-dataset), [Paper](https://arxiv.org/pdf/2004.01241.pdf), [Code](https://github.com/xahidbuffon/SUIM-Net). (SUIM-Net)\n- Coral-Net: [Data](https://coralnet.ucsd.edu/), [Paper](https://onlinelibrary.wiley.com/doi/full/10.1002/rob.21915), [Code](https://github.com/Shathe/CoralSeg). (Coral-Seg)\n- Eilat dataset: [Data](https://sites.google.com/a/unizar.es/semanticseg/), [Paper](https://www.nature.com/articles/srep23166.pdf).\n- Change detection: [Data](http://underwaterchangedetection.eu/index.html), [Paper](https://ieeexplore.ieee.org/document/7761129).\n\n#### Underwater Salient Object Detection\n- UFO-120: [Data](http://irvlab.cs.umn.edu/resources/ufo-120-dataset), [Paper](https://arxiv.org/pdf/2002.01155.pdf), [Code](https://github.com/xahidbuffon/Deep-SESR)\n- MUED database: [Data-1](https://zenodo.org/record/2542305#.X0YMt3UzY5k), [Data-2](https://zenodo.org/record/2542307#.X0YM53UzY5k), [Paper](https://www.sciencedirect.com/science/article/abs/pii/S1568494619302169)\n\n\n\nThe original Source of this Collection is: https://github.com/xahidbuffon/Awesome_Underwater_Datasets\nCredit: @xahidbuffon", "1599424": "Wow! This is great for researching. Thank you!", "1617951": "Did you add external data?", "1618163": "No. So far I am trying to find best CV split." }, "source": "meta" }