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 |
---|---|---|---|---|
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. |
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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 |
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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. |
Regression models¶
Object detection models¶
Model |
Input size |
CM/PX |
Description |
Example image |
---|---|---|---|---|
256 |
70 |
YOLOv7 tiny model for object detection on satellite images. Based on the Airbus Aircraft Detection dataset. |
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512 |
150 |
YOLOv5-m model for object detection on satellite images. Based on the Airbus Oil Storage Detection dataset. |
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640 |
10 |
YOLOv7-m model for cars detection on aerial images. Based on the ITCVD. |
Super Resolution Models¶
Model |
Input size |
CM/PX |
Scale Factor |
Description |
Example image |
---|---|---|---|---|---|
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 |
|
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.