Deepness Model ZOO

The Model ZOO is a collection of pre-trained, deep learning models in the ONNX format. It allows for an easy-to-use start with the plugin.

NOTE: the provided models are not universal tools and will perform well only on similar data as in the training datasets. If you notice the model is not perfroming well on your data, consider re-training (or fine-tuning) it on your data.

If you do not have machine learning expertise, feel free to contact the plugin authors for help or advice.

Segmentation models

Model	Input size	CM/PX	Description	Example image
Corn Field Damage Segmentation	512	3	PUT Vision model for Corn Field Damage Segmentation created on own dataset labeled by experts. We used the classical UNet++ model. It generates 3 outputs: healthy crop, damaged crop, and out-of-field area.	Image
Land Cover Segmentation	512	40	The model is trained on the LandCover.ai dataset. It provides satellite images with 25 cm/px and 50 cm/px resolution. Annotation masks for the following classes are provided for the images: building (1), woodland (2), water(3), road(4). We use DeepLabV3+ model with tu-semnasnet_100 backend and FocalDice as a loss function. NOTE: the dataset covers only the area of Poland, therefore the performance may be inferior in other parts of the world.	Image
Buildings Segmentation	256	40	Trained on the RampDataset dataset. Annotation masks for buildings and background. Xunet network. Val F1-score 81.0	Image
Land Cover Segmentation Sentinel-2	64	1000	Trained on the Eurosat dataset. Uses 13 spectral bands from Sentinel-2, with 10 classes. Model ConvNeXt.	Image
Agriculture segmentation RGB+NIR	256	30	Trained on the Agriculture Vision 2021 dataset. 4 channels input (RGB + NIR). 9 output classes within agricultural field (weed_cluster, waterway, …). Uses X-UNet.	Image
Fire risk assesment	384	100	Trained on the FireRisk dataset (RGB data). Classifies risk of fires (ver_high, high, low, …). Uses ConvNeXt XXL. Val F1-score 65.5.	Image
Roads Segmentation	512	21	The model segments the Google Earth satellite images into ‘road’ and ‘not-road’ classes. Model works best on wide car roads, crossroads and roundabouts.	Image
Solar PV Segmentation	512	20	Model trained by M Kleebauer et al. in “Multi-resolution segmentation of solar photovoltaic systems using deep learning on a diverse range of image data, spanning UAV, aerial, and satellite imagery at both native and aggregated resolutions of 0.1 m, 0.2 m, 0.3 m, 0.8 m, 1.6 m, and 3.2 m.	Image

Regression models

Recognition models

Model	Input size	CM/PX	Description	Example image
NAIP Place recognition	224	100	ConvNeXt nano trained using SimSiam onn NAIP imagery. Rank1-accuracy 75.0.	Image

Object detection models

Model	Input size	CM/PX	Description	Example image
Airbus Planes Detection	256	70	YOLOv7 tiny model for object detection on satellite images. Based on the Airbus Aircraft Detection dataset.	Image
Airbus Oil Storage Detection	512	150	YOLOv5-m model for object detection on satellite images. Based on the Airbus Oil Storage Detection dataset.	Image
Aerial Cars Detection	640	10	YOLOv7-m model for cars detection on aerial images. Based on the ITCVD.	Image
UAVVaste Instance Segmentation	640	0.5	YOLOv8-L Instance Segmentation model for litter detection on high-quality UAV images. Based on the UAVVaste dataset.	Image
Tree-Tops Detection	640	10	YOLOv9 model for treetops detection on aerial images. Model is trained on the mix of publicly available datasets.	Image

Super Resolution Models

Model	Input size	CM/PX	Scale Factor	Description	Example image
Residual Dense Network (RDN X2)	64	Trained on 10 cm/px images set it same as input data	X2	Model originally trained by H Zhang et. al. in “A Comparative Study on CNN-Based Single-Image Super-Resolution Techniques for Satellite Images“ converted to onnx format	Image from Massachusetts Roads Dataset Dataset in kaggle
Residual Dense Network (RDN X4)	64	Trained on 10 cm/px images set it same as input data	X4	Model originally trained by H Zhang et. al. in “A Comparative Study on CNN-Based Single-Image Super-Resolution Techniques for Satellite Images“ converted to onnx format	Image from Massachusetts Roads Dataset Dataset in kaggle

Contributing

PRs with models are welcome!

• Please follow the general model information.

• Use MODEL_ZOO tag in your PRs to make it easier to find them.

• If you need, you can check how to export the model to ONNX.

• And do not forget to add metadata to the ONNX model.

• You can host your model yourself or ask us to do it.