#Rsde
Explore tagged Tumblr posts
Text
Rsde x Rsde Rule 34 🔥🔥🔥🔥🔥🔥🔥🔥🔊🔊🔊🔊🔊🔊🔊🔊🗣🗣🗣🗣🗣🗣🗣
Jk x Inedsrf rule 34 🔥🔥🔥🔥🔥🔥🔥🔥🔥🔥🔊🔊🔊🔊🔊🔊🔊🔊🗣🗣🗣🗣🗣🗣
Biz x Orang cat 🔥🔥🔥🔥🔥🔥🔥🔊🔊🔊🔊🔊🔊🔊🗣🗣🗣🗣🗣🗣🗣
#deviantart school art#rsde#rsde rule 34#rsde x rsde rule 34#jk#inedsrf rule 34#jk x inedsrf rule 34#biz#orang cat#biz x orang cat#eeeeeeeee#art#>:3#tumblr milestone
6 notes
·
View notes
Text
Rsde rule 34 and Kun ???oatu
4 notes
·
View notes
Text
YOU live in 2023 AD
I’m living in 2023 RSDE (Ryan & Shane’s Divorce Era)
#Can’t believe we made it to the divorce era besties tbh I’m a lil surprised it took this long 👀☕️🫢#Ryan Bergara#Shane Madej#The Ghoul Boys#Mystery Files#Watcher#Watcher Entertainment#Ani Rambles
89 notes
·
View notes
Photo
老了老了😬 https://www.instagram.com/p/CiMJgr-rSde/?igshid=NGJjMDIxMWI=
0 notes
Photo
2 in1 Fön Fön und Volumizer Bürste Glätten Lockenstab Co RSDE WZ Preis : 2.58 € jetzt kaufen 2 in1 Fön Fön und Volumizer Bürste Glätten Lockenstab Co RSDE WZ Preis : 2.58 € endet in : 2020-06-12 19:56:04 jetzt kaufen
0 notes
Photo
VSaero FRP RSDE Front Lip Valance > Ferrari 360 2000-2004 http://ift.tt/2hpj7WW
0 notes
Photo
American Rights and the Thought Police Get the full story on STAND http://www.qoo.ly/rsdes When our Founding Fathers started this country, they put certain rights into place to protect the citizens of a new nation. These guys were pretty bright and they set up a system that included things like Freedom of Speech, Freedom of Religion, Freedom to Assemble and Freedom of the Press.
0 notes
Text
Les technologies de réduction des polluants doivent être adaptées à chaque site
Pour se mettre en conformité avec l'arrêté RSDE, les industriels se doivent d'être proactifs. Qu'ils engagent une démarche de réduction ou de suppression totale des rejets, les technologies existantes doivent être adaptées à chaque site.
0 notes
Text
Machine Learning RSDE
Description
Machine Learning RSDE Intern Paid Summer Internship – Seattle, WA
At Vulcan, you will join a top-notch technology team that is dedicated to solving some of the world’s toughest problems. Your analytical and technical skills will make a difference and a real impact on the lives of others.
Throughout the summer, you will work on machine learning projects in the area of object detection…
View On WordPress
0 notes
Text
Using Machine Learning to Count Sharks and Rays More Quickly
New Tool Supports Ambitious Global FinPrint Project
Sharks and rays are incredibly important to the delicate ecosystems near coral reefs. Their numbers are dwindling rapidly, and being able to understand how the populations of sharks and rays in various areas have been affected by overfishing and other factors will give researchers rich insight into where conservation efforts should be concentrated.
Paul G. Allen and Vulcan are supporting Global FinPrint, a world-wide effort to assess coral reef sharks and rays, understand how they affect these ecosystems and inform emerging conservation actions.
Global FinPrint teams have collected over 16,000 hours of underwater video from more than 300 coral reefs so far. Baited remote underwater video surveys (BRUVs) are the instruments used to collect the videos. BRUVs are structures placed on the ocean floor that have a camera mounted atop a long metal arm with a basket of bait at the end.
A shark captured by a BRUV
The use of BRUVs to collect the videos eliminates the need for people on the reef that could affect the behavior of the sharks and rays, allowing for a more accurate report of the animals in the area. The BRUVs are left underwater for 60-90 minutes, after which the collected videos each require annotation by two different humans -- usually grad students -- to identify the animals that appear. The annotations are then evaluated and verified by a third expert. These annotations will make up the final dataset that will be released.
Placing a BRUV on a reef (Photo by: Duncan Blake)
Vulcan’s Technology team has developed ElasmoFinder -- a machine learning tool designed to reduce the need for human analysis of the video captured by the BRUVs. ElasmoFinder, named after elasmobranchii -- the subclass of fish containing sharks and rays, speeds up the annotation process by automatically identifying animals in the videos.
Additionally, the final reviewer’s feedback about the model-generated annotations will be used to continue training the model. Any frames that the reviewer determines were incorrectly labeled by the model become part of the training data so that the model can learn from its mistakes and improve. If the ElasmoFinder gets accurate enough to replace all of the human annotators, the time to process the videos will be cut in half and data can be provided to scientists and environmentalists more quickly and put it to use to help sharks and the reef ecosystems.
An eel is caught by the BRUVs
How It Works
Machine learning is the process of taking a large set of data and building from it a numerical model that captures the important underlying patterns of the data. These learned patterns can then be used to automatically perform some task on data the model has never seen before. Datasets that ML models learn from often have been preprocessed by humans to have the correct labels provided for each piece of data. In the case of ElasmoFinder, the ML model was trained on a dataset of about 25,000 still frames from already annotated videos that had been labeled to include boxes around animals of interest as well as the species of the animals.The model learns by making a prediction and checking it against the true label. If what the model predicted was incorrect it will make a slight correction so that it will be able to do better next time. The model learned through these labeled frames how to detect if and where in the image there exists an animal and how to make a prediction about the species of that animal.
In order to split up the large overall task of detecting and classifying each frame (and the large number of possible species to pick from), ElasmoFinder is made up of many submodels.
First, each image is passed through an object detection model that locates animals in a frame and classifies the animal as either shark, ray, fish, or moray. After that, each image is passed to a more specific classification model based on the first model’s prediction. The region of any image that was selected and labeled as either a shark, ray, or moray will be passed through only one more level of models to predict the species of the animal. There are many more species of fish than there are of any of the other groups, so fish image regions are passed through a genus-level model and then potentially through a species-level model as well if the predicted genus has more than one species within it. The models were trained to recognize only the species that appeared a substantial amount of times in the dataset. The number of classes each model was trained to distinguish between are show in the table below:
The ElasmoFinder models are all trained and run using NVIDIA DIGITS and are initialized as the GoogLeNet ImageNet model, which is a fully convolutional neural network (FCN) trained on a dataset of 1.2 million images. ElasmoFinder takes a frame from the video every few seconds and runs it through the models to see if it finds anything. At first, the annotations generated by the model will be used as a third annotator, but in the future, the ElasmoFinder could replace one of the human annotators or work in tandem with a human to speed up the annotation process.
Currently, the group-level model is able to classify animals it finds in images with 74.93% accuracy, 83.76% precision and 53.27% recall. In addition, the bounding boxes that the group-level model finds have a 71.82% intersection over the union compared to the boxes drawn by the human annotators. The other models are able to classify animals with a combined average of 73.04 % accuracy, 68.02% precision and 66.09% recall.
Conclusion
The ElasmoFinder will be incorporated into the annotation of the many hours of videos that still need processing either as an additional annotator or as a replacement for one of two human annotators. Either way, the hope is that the ElasmoFinder will be able to reduce the amount of time needed to process these videos to turn them into a collective dataset. This dataset will be made widely available for use in education, outreach and conservation with the ultimate goal of protecting the sharks, rays, and other creatures that our ocean ecosystems depend upon.
-- Gracie Ermi is the Machine Learning RSDE Summer 2017 Intern at Vulcan.
0 notes
Text
Rsde Rule 34 and kun ???oatu
Rsde rule 34 and Kun ???oatu
#deviantart school art#osomatsu san#rsde rule 34#osomatsu oc#osomatsu kun#:3#eeeeeeeee#art#tumblr milestone
4 notes
·
View notes
Text
Rsde Rule 34:LOVE YOU RSDE 😍😍😍😍😍😍😍
Rsde:Aku tak ada yg bisa di Indonesia dan dapat menyerap air melalui DM ya kalo udah di Indonesia dan dapat menyerap air
Original
#Rsde#Rsde rule 34#deviantArt school art#eeeeeeeee#art#>:3#Lesbian#Rsde x Rsde Rule 34#tumblr milestone
4 notes
·
View notes
Text
Rsde Rule 34:wow
Rsde: 😨
#Rsde#Rsde Rule 34#Rsde x Rsde Rule 34#art#DeviantArt school art#eeeeeeeee#>:3#GIRLS KISS#Lesbian#🏳️🌈?#tumblr milestone
4 notes
·
View notes
Text
Rsde She/Her, 29 years, Librarian, child, book, one, ok, love rsde Rule 34.
Sexuality {LGBTQ}
Lesbian
Like: RI good
Love: Rsde Rule 34
4 notes
·
View notes
Text
Katarenai 🥰
#Rsde#Rsde Rule 34#Rsde x Rsde Rule 34#art#eeeeeeeee#DeviantArt school art#>:3#🏳️🌈?#👹#tumblr milestone#Lesbian
5 notes
·
View notes
Text
Fox girl E, Duyi D, Sy S and Rye R
Rsde
6 notes
·
View notes