EcoAssist

Simplifying camera trap image analysis with AI

Open Source

Every line of code is open source

User-friendly

Automated install and intuitive interface

Offline

Does not need any internet connection after installation

Video support

Works on both images and videos

Integration

Export results to the Timelapse Image Analyser

Human-in-the-loop

Option to human verify model predictions

GPU acceleration

Runs automatically on NVIDIA and Apple Silicon GPU

Post-process

Separate, visualise, crop, label or export results

EcoAssist is an application designed to streamline the work of ecologists dealing with camera trap images. It’s an AI platform that allows you to analyse images on your local computer and use machine learning models for automatic detection and identification, offering ecologists a way to save time and focus on conservation efforts.

It has the open-source MegaDetector model incorporated, which can filter out images containing animals, people, and vehicles. You can select one of the species recognition model listed below to further identify the animals. Addax will be adding more identification models in the future. Do you have a model that you would like to make open-source? Or do you want Addax to develop a model specifically for your project? Get in touch!

Species identification models

The following species models are incorporated into EcoAssist. Please keep in mind that these models were developed for a specific project. Whether or not a model may also be useful for other projects depends on factors such as the species pool, ecosystem, camera setup, and image resolution. Therefore, it is crucial to thoroughly test the models on your own data before use.

Namibian Desert

Developer

Addax Data Science

Description

Model to identify 30 species or higher level taxons present in the desert biome of the Skeleton Coast National Park, North Namibia. The model was trained on a set of more than 850,000 images.

Classes

aardwolf
african wild cat
baboon
bird
brown hyaena
caracal
cattle
cheetah
donkey
elephant
fox
gemsbok
genet
giraffe
hare
honey badger
hyrax
jackal
klipspringer
kudu
leopard
lion
mongoose
ostrich
porcupine
rhinoceros
spotted hyaena
springbok
steenbok
zebra

Links

Europe

Developer

The DeepFaune initiative

Model version

v1.1

Description

The Deepfaune initiative aims at developing 'artificial intelligence' models to automatically classify species in images and videos collected using camera-traps. The initiative is led by a core academic team from the French 'Centre National de la Recherche Scientifique' (CNRS), in collaboration with more than 50 European partners involved in wildlife research, conservation and management. The Deepfaune models can be run through a custom software freely available on the website, or through other software packages or platforms like EcoAssist. New versions of the model are published regularly, increasing classification accuracy or adding new species to the list of species that can be recognized. More information is available at: https://www.deepfaune.cnrs.fr.

Classes

badger
ibex
red deer
chamois
cat
goat
roe deer
dog
squirrel
equid
genet
hedgehog
lagomorph
wolf
lynx
marmot
micromammal
mouflon
sheep
mustelid
bird
bear
nutria
fox
wild boar
cow

Links

New Zealand Invasives

Owner

New Zealand Department of Conservation

Developer

Addax Data Science

Description

Model to identify 17 species or higher-level taxons present in New Zealand. The model was trained on a set of approximately 2 million camera trap images from various projects across the country. These projects were run by multiple organizations and took place in a diverse range of habitats, using a variety of trail camera brands and models. The model has an overall validation accuracy, precision, and recall of 98%. When tested on an out-of-sample test set, the model scored 95%, 96%, and 94%, respectively. The model was designed to expedite the monitoring of New Zealand's invasive species (deer, possum, pig, cat, rodent, and mustelid).

Classes

caprid
cat
cow
deer
dog
hedgehog
kea
kiwi
lagomorph
mustelid
other bird
pig
possum
rodent
sealion
wallaby
weka

Links

Columbian Amazon

Developer & Owner

AI For Good Lab, Microsoft

Description

The model is trained to classify animals into their genus taxonomic group. The training data are collected by the Department of Biological sciences at Universidad de los Andes. All the images come from the 'Magdalena Medio' region in Colombia. The dataset contains 41,904 images across 36 labeled genera, and it is distributed into 33,569 images in the training set and 8,335 images in the validation set. In our inference of the Amazon Rainforest dataset, we implement a 98% confidence threshold as part of a human-in-the-loop procedure. We observe that the model predicts 90% of the data with recognition confidence exceeding this threshold. Furthermore, within this high-confidence subset, the model achieves an average classification accuracy of 92%. This means that, after filtering out empty images with MegaDetector, only 10% of the detected animal objects require human validation.

Classes

Dasyprocta
Bos
Pecari
Mazama
Cuniculus
Leptotila
Human
Aramides
Tinamus
Eira
Crax
Procyon
Capra
Dasypus
Sciurus
Crypturellus
Tamandua
Proechimys
Leopardus
Equus
Columbina
Nyctidromus
Ortalis
Emballonura
Odontophorus
Geotrygon
Metachirus
Catharus
Cerdocyon
Momotus
Tapirus
Canis
Furnarius
Didelphis
Sylvilagus
Unknown

Links

Iran

Developer

Addax Data Science

Description

Model to identify 13 species or higher-level taxons present in Iran. The model was trained on a set of approximately 1 million camera trap images. The model has an overall validation accuracy, precision, and recall of 95%, 93%, and 94%, respectively. The accuracy was not tested on an out-of-sample-dataset since local images were absent. The model was designed to expedite the monitoring of the Iranian Cheetah Society.

Classes

antilope
bird
camel
caracal
cat
cheetah
equid
fox
goat+sheep
hyena
leopard
porcupine
wolf+jackal

Links

Peruvian Amazon

Developer & Owner

San Diego Zoo Wildlife Alliance

Description

This model was trained by Mathias Tobler from the San Diego Zoo Wildlife Alliance. Information about the source of the training dataset and model metrics is not available.

Classes

Black-headed squirrel monkey
Brazilian rabbit
Brown agouti
Bush dog
Capybara
Coati
Collared anteater
Collared peccary
Common opossum
Crab-eating raccoon
Giant anteater
Giant armadillo
Gray four-eyed opossum
Great tinamou
Green acouchy
Grey brocket deer
Grey-fronted dove
Grison
Jaguar
Jaguarundi
Large-headed Capuchin
Long-nosed armadillo
Margay
Ocelot
Paca
Pale-winged trumpeter
Puma
Razor-billed curassow
Red brocket deer
Short-eared dog
Southern amazonian red squirrel
Southern naked-tailed armadillo
Spiny Rat
Spix's guan
Tapir
Tayra
Unknown bird
Unknown reptile
Unknown rodent
White-fronted capuchin
White-lipped peccary
Yellow-foot tortoise

Links

Peruvian Andes

Developer & Owner

San Diego Zoo Wildlife Alliance

Description

This model was trained by Kyra Swanson from the San Diego Zoo Wildlife Alliance and distinguishes between 53 species native to the Peruvian Andes. The training data was collected by SDZWA and comprises 201,943 images. They used a 70/20/10 Train/Val/Test split. The model reached an overall accuracy, precision, and recall of 88.9%, 88.6%, and 87.3% respectively on the test set.

Classes

andean bear
andean fox
andean guan
andean white-eared opossum
black agouti
bolivian squirrel
brown agouti
bush dog
collared peccary
common opossum
cow
domestic dog
dwarf brocket deer
empty
forest rabbit
giant anteater
giant armadillo
grison
highland coati
human
jaguar
jaguarundi
little red brocket deer
long-nosed armadillo
long-tailed weasel
lowland paca
lowland tapir
margay
molinas hog-nosed skunk
mountain paca
mountain tapir
northern pudu
ocelot
oncilla
other opossum
pacarana
pale-winged trumpeter
puma
razor-billed curassow
red brocket deer
reptile
short-eared dog
sickle-winged guan
small mammal
south american coati
southern naked-tailed armadillo
spixs guan
tamandua
tayra
tinamou
unknown bird
white-lipped peccary
white-tailed deer

Links

Southwest USA

Developer & Owner

San Diego Zoo Wildlife Alliance

Description

This model distinguishes between 27 species native to the Southwest United States. The training data was collected partially by SDZWA and the California Mountain Lion Project, and includes examples from the NACTI, and CCT training datasets. The training data corpus comprises 91662 images. We used a 70/20/10 Train/Val/Test split. The model reached an overall accuracy of 88% on the test set. Created by Kyra Swanson in 2023 (tswanson@sdzwa.org).

Classes

badger
beaver
bird
boar
bobcat
cat
corvid
cougar
cow
coyote
deer
dog
empty
fox
human
opossum
other
owl
rabbit
raccoon
raptor
reptile
rodent
skunk
squirrel
vehicle
weasel

Links

Workflow

Step 1: Import

Avoid the hassle of cloud uploads. Simply select a local folder with images and/or videos on your device.

Step 2: Analyse

Choose a MegaDetector version for detecting animals, humans, and vehicles and select a custom model for finer classification.

Step 3: Verify

Optionally, perform a human-in-the-loop session to confirm specific classes, confidence ranges, or subsets based on different selection methods. Export verified images to enlarge the training set for future model refinements.

Step 4: Post-process

Once satisfied, decide how to utilise the output. Options include sorting images into folders, cropping detections, drawing boxes, and exporting results to CSV files for further analysis. Custom features are always welcome.

Users

9821

Downloads

271

Affiliations

116

Universities

104

Countries

The affiliations are solely based on user interactions. We would love to hear more about the projects you are involved in! Feel free to email us.

EcoAssist is free

At Addax Data Science, we are dedicated to making conservation technology accessible to everyone—especially for those who may not have the funds to support it. Developing and maintaining EcoAssist requires considerable time and resources, all contributed by volunteers. If you or your organisation can contribute, your support will help keep EcoAssist available. Donations will directly fund platform maintenance, new features, and the development of species identification models.

Let us know if you have any questions or want to receive an invoice for tax-deduction purposes.

For individual supporters

For ongoing support

For organisations

Or choose your own amount via this link.

Install or update

Everything you need to get AI into your image workflow.

Learn more

Navigate to the pages below for more information.

Source code

Find the EcoAssist’s GitHub repository listed here

Learn More

Timelapse

Discover the integration with the Timelapse image analyser

Learn More

MegaDetector

Learn more about the engine behind EcoAssist

Learn More

CamTrap Pro

Import your results to the CamTrap Pro application

Learn More

Frequently Asked Questions

How can I cite EcoAssist?

Please use the following citations if you used EcoAssist in your research.

van Lunteren, P. (2023). EcoAssist: A no-code platform to train and deploy custom YOLOv5 object detection models. Journal of Open Source Software, 8(88), 5581.
Beery, S., Morris, D., & Yang, S. (2019). Efficient pipeline for camera trap image review. arXiv preprint arXiv:1907.06772.
Plus the citation of the species identification model used.

It doesn’t use my GPU. What can I do?

EcoAssist should automatically run on NVIDIA or Apple Silicon GPU if available. The appropriate CUDAtoolkit and cuDNN software is already included in the EcoAssist installation for Windows and Linux. If you have NVIDIA GPU available but it doesn't recognise it, make sure you have a recent driver installed, then reboot. An MPS compatible version of Pytorch is included in the installation for Apple Silicon users. The progress window will display whether EcoAssist is running on CPU or GPU. Email me if you need more assistance.

I found a bug or ran into a problem. What should I do?

It's always good practise to first run EcoAssist in debug mode, where it will print its output in a console window. That should point us in the right direction if there is an error. How to run it in debug mode depends on your operating system and can be found here. You can always email me if you if you need help with this.

Once you've opened EcoAssist in debug mode, you'll have to recreate the error so that the traceback will show up in the console window. You can copy-paste the output and email it to us, or raise an issue in the GitHub repository.

How can I contribute in terms of code?

Interested in contributing to this project? There are always things to do. Do you feel comfortable handling one of the tasks listed here?

EcoAssist is an open-source project, so please feel free to fork the EcoAssist GitHub repository and submit fixes, improvements or add new features. For more information, see the contribution guidelines.

Previous code contributors can be found here. Thank you!

What happened to the train feature in EcoAssist?

In previous versions of EcoAssist (v3.0 > v4.3) it was possible to train your own object detection models based on MegaDetector to detect your target species. Although this did work, it wasn't the best approach to develop a species recognition model. It required lots of training data, processing power, time, electricity and wasn't very accurate. Advancing insights revealed that better results can be obtained by using an object classification model to be used in conjunction with the results of MegaDetector. The animals will then be located by MegaDetector, and further classified by your custom model. EcoAssist > v4.2 does support the deployment of a classification model to be used in conjunction with MegaDetector, but training such a model is more complicated and hasn't been incorporated into EcoAssist > v4.4.

If you still want to use the training feature of v4.3, you can download the EcoAssist v4.3 install file below. The rest of the installation will be done as usual, as is described here.

We've placed a detailed tutorial on Medium that provides a step-by-step guide on annotating, training, evaluating, deploying, and postprocessing data with EcoAssist v4.3. You can find it here.

How can I uninstall EcoAssist?

All EcoAssist files are located in one folder, called 'EcoAssist_files'. See these instructions on where to find it. Windows users also need to remove the 'ecoassistcondaenv' conda environments. Let me know if you need help with that.