Diario de ahospers

ere’s the little R script I used to make that fig from the iNat API
library(jsonlite)
options(timeout= 4000000)
dat <- fromJSON("http://api.inaturalist.org/v1/observations/histogram?date_field=created&interval=month&verifiable=true")
x = c()
y = c()
for(i in 1:(length(dat$results$month))){
x = c(x,names(dat$results$month[i]))
y = c(y,dat$results$month[[i]])
}

par(mfrow=c(1,1))
xx = (1:length(y))
plot(xx,y, type="l", axes=FALSE, xlab="", ylab="Observations per month", xlim=c(40,length(xx)))
points(xx,y, type="l", lty=2)
axis(2)
axis(1,which(strptime(x,"%Y-%m-%d")$yday==0),(2019-length(which(strptime(x,"%Y-%m-%d")$yday==0))+1):2019)
abline(h=1000000, lty=2)

Here’s the little R script I used to make that fig from the iNat API

https://twitter.com/search?q=pypsa Ik dacht dat ergens door betere voorspellingen negatieve prizjen bijna zouden verdwijn maar ik kan he net vindne

https://zenodo.org/record/7050651/files/Naturalis%20Biodiversity%20Center%20%282022%29.%20Eindrapportage%20project%20Automatische%20beeldherkenning%20voor%20museumcollecties.pdf?download=1

Naturalis Biodiversity Center (2022). Eindrapportage project Automatische beeldherkenning voor museumcollecties.pdf

https://www.nlbif.nl/verborgen-biodata/ wist je dit...geleid door gallenman

https://twitter.com/hashtag/EnergyTwitterVrijMiBo?src=hashtag_click

https://www.researchgate.net/publication/341278898_Big_Data_Ja_Natuurlijk

https://www.researchgate.net/publication/349378026_Diet_composition_of_the_golden_jackal_Canis_aureus_in_south-east_Europe_-a_review

https://www.knawonderwijsprijs.nl/bestandenafbeeldingen/2016/modelleren-van-vegetatiepatronen.pdf

https://www.geologienederland.nl/wp-content/uploads/2022/09/NGV_THM_CRB_Themadag_Noordzeebodem_2022_-_Samenvattingen_lezingen_versie_2.pdf

https://www.techthics.nl/wp-content/uploads/2022/02/erdincsacan-inclusieveartificialintelligence.pdf

https://arxiv.org/pdf/1906.10742.pdf

https://arxiv.org/pdf/2103.10703.pdf

https://www.tmgonline.nl/article/10.18146/tmg.815/

https://github.com/joergmlpts

https://forum.inaturalist.org/t/computer-vision-update-july-2021/24728 1

https://www.inaturalist.org/blog/63931-the-latest-computer-vision-model-updates 1
https://forum.inaturalist.org/t/new-computer-vision-model-released/31030 1

https://forum.inaturalist.org/t/new-vision-model-training-started/27378 1
https://www.inaturalist.org/blog/59122-new-vision-model-training-started 1

https://github.com/inaturalist/inatVisionTraining

The file https://github.com/inaturalist/inatVisionTraining/blob/main/nets/nets.py 1 appears to have the relevant code for instantiating models. The main chunk of the model is Xception which involves something called “depthwise separable convolutions” (I have not read the paper yet). The output of Xception is then put through a global average pooling layer, then a dropout layer, then a dense layer (i.e. like you would find in a perceptron model), and then a softmax layer.

With some further reading of the paper, I think the Github repo will have given me a much clearer picture of what the computer vision model

This project was inspired by the amazing progress in identifying plants, animals and mushrooms in photos that has been made by iNaturalist in the past years. The iNaturalist team has trained machine learning models with their vast collection of photos and research-grade identifications. In 2019, iNaturalist released Seek by iNaturalist which identifies photos offline on the phone and identifies to a higher level than species when an identification to species cannot be made.

Google provides three models that have been trained with iNaturalist data - classification models for plants, birds, and insects. These Google models can be downloaded and used with Google's TensorFlow and TensorFlow Lite tools.

This code is based on the trained models that Google provides. It has been written to experiment with identification of species from photos and to give Seek's approach a try and compute probabilities across the taxonomic hierarchy.

This tool nature_id.py has been tested on Linux and Windows. It likely works on macOS as well.

This is a command-line tool. It is called with photos or directories that contain photos and identifies the plants, birds, and insects in these pictures.
Here is an example. This is the command for Linux and macOS:

./nature_id.py -m plants plant_images/Persicaria_amphibia.jpg
On Windows the command is:

python .\nature_id.py -m plants plant_images\Persicaria_amphibia.jpg

https://github.com/joergmlpts/nature-id