Automatic separation of objects in images containing multiple plankton organisms
Know more »»Welcome to the DEEP Open Catalog!
DEEP-Hybrid-DataCloud is delivering a comprehensive platform to easily develop, build, share and deploy Artificial Intelligence, Machine Learning and Deep Learning modules on top of distributed e-Infrastructures.
In the DEEP Open Catalog you can find ready to use modules in a variety of domains. These modules can be executed on your local laptop, on a production server or on top of computing e-Infrastructures supporting the DEEP-Hybrid-DataCloud stack.
Model
Automatic separation of objects in images containing multiple plankton organisms
Know more »»Model
Thunderstorm nowcast based on radar data (for agrometeorology)
Know more »»Model
Integration of DeepaaS API and litter assessment software
Know more »»Model | Trainable | Inference | Pre-trained
Train your own image classifier, object detection, or segmentation model with your custom dataset using the YOLOv8 model.
Know more »»Model | Trainable | Inference | Pre-trained
Object detection using FasterRCNN model(s) (fasterrcnn_pytorch_api)
Know more »»Model
WIP Identification of marine species from EMSO Azores deep-sea obervatory
Know more »»Model | Trainable | Inference | Pre-trained
We suggest a 2D image segmentation model based on UNET algorithm to segment images with blossoming apple tree
Know more »»Model
Semantic segmentation with Unet Deep Learning model applied to segment Cercospora Leaf Spot.
Know more »»Model
A toy application for demo and testing purposes. We just implement dummy inference, ie. we return the same inputs we are fed.
Know more »»Model | Trainable | Inference | Pre-trained
A module to apply artistic style transfer using pytorch.
Know more »»Model | Trainable | Inference | Pre-trained
Classify audio files among bird species from the Xenocanto dataset.
Know more »»Model | Trainable | Inference | Pre-trained
A trained Region Convolutional Neural Network (Faster RCNN) for object detection and classification.
Know more »»Model | Trainable | Inference | Pre-trained
2D semantic segmentation trained on the Vaihingen dataset
Know more »»Model | Trainable | Inference | Pre-trained
Train your own audio classifier with your custom dataset. It comes also pretrained on the 527 AudioSet classes.
Know more »»Train a speech classifier to classify audio files between different keywords.
Know more »»Deep learning for proactive network monitoring and security protection.
Know more »»Model | Trainable | Inference | Pre-trained
Classify conus images among 70 species.
Know more »»Model | Trainable | Inference | Pre-trained
Train your own image classifier with your custom dataset. It comes also pretrained on the 1K ImageNet classes.
Know more »»Model | Inference | Pre-trained | Trainable
Classify chest x-ray images in patological and non patological with this x-ray classifier.
Know more »»Model | Trainable | Inference | Pre-trained
Classify phytoplankton images among 60 classes.
Know more »»Model | Trainable | Inference | Pre-trained
Classify plant images among 10K species from the iNaturalist dataset.
Know more »»Model | Trainable | Inference | Pre-trained
Upscale (superresolve) low resolution bands to high resolution in multispectral satellite imagery.
Know more »»Model | Trainable | Inference | Pre-trained
Classify seeds images among 700K species.
Know more »»Model | Trainable | Inference | Pre-trained
Identify a dogs breed on the image (133 known breeds)
Know more »»Model | Trainable | Inference | Pre-trained
A Tensorflow model to classify Retinopathy.
Know more »»Tool
A logical, reasonably standardized, but flexible project structure for doing and sharing data science work. Based on more general data science template.
Know more »»Browse the list of available modules and pick the one of your choice.
The DEEP Generic Container is a good starting point to see how the API looks like (but it does not provide any functionality at all).
$ docker search deephdc
$ docker run -ti -p 5000:5000 deephdc/deep-oc-generic-container
No Docker possible? Try udocker instead!
$ udocker search deephdc
$ udocker run -p 5000:5000 deephdc/deep-oc-generic-container
Need GPU access?
Use nvidia-docker together with Docker:$ nvidia-docker run -ti -p 5000:5000 deephdc/deep-oc-generic-container
Or udocker way:
$ udocker pull deephdc/deep-oc-generic-container
$ udocker create --name=deep-oc-generic-container deephdc/deep-oc-generic-container
$ udocker setup --nvidia deep-oc-generic-container
$ udocker run -p 5000:5000 deep-oc-generic-container
http://127.0.0.1:5000/
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