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README.md
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The models are trained on 512x512px RGB image patches with spatial resolution between 3.9cm and 4.3 cm and with hand-annotated deadwood data based on visual inspection. The location of training dataset is in the vicinity of Hiidenportti national, Sotkamo, Finland, and the images were acquired during leaf-on season, on 16. and 17.7.2019. Most likely the models are most suitable to use with imagery from leaf-on season and with similar ground sampling distance.
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## Training data
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These models were trained on expert-annotated deadwood data, acquired on during leaf-on season 16.-17.7.2019 from Hiidenportti, Sotkamo, Eastern-Finland. The ground resolution for the data varied between 3.9 and 4.4cm. In addition, the model was tested with data collected from Evo, Hämeenlinna, Southern-Finland, acquired on 11.7.2018. The data from Evo was used only for testing the models.
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The models are trained on 512x512px RGB image patches with spatial resolution between 3.9cm and 4.3 cm and with hand-annotated deadwood data based on visual inspection. The location of training dataset is in the vicinity of Hiidenportti national, Sotkamo, Finland, and the images were acquired during leaf-on season, on 16. and 17.7.2019. Most likely the models are most suitable to use with imagery from leaf-on season and with similar ground sampling distance.
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Example app is running on [https://huggingface.co/spaces/mayrajeo/maskrcnn-deadwood](https://huggingface.co/spaces/mayrajeo/maskrcnn-deadwood), which uses R101 backbone without TTA.
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## Training data
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These models were trained on expert-annotated deadwood data, acquired on during leaf-on season 16.-17.7.2019 from Hiidenportti, Sotkamo, Eastern-Finland. The ground resolution for the data varied between 3.9 and 4.4cm. In addition, the model was tested with data collected from Evo, Hämeenlinna, Southern-Finland, acquired on 11.7.2018. The data from Evo was used only for testing the models.
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