Using DeepForest from R#

An R wrapper for DeepForest is available in the deepforestr package. Commands are very similar with some minor differences due to how the wrapping process using reticulate works.


deepforestr is an R wrapper for the Python package, DeepForest. This means that Python and the DeepForest Python package need to be installed first.

Basic Installation#

If you just want to use DeepForest from within R run the following commands in R. This will create a local Python installation that will only be used by R and install the needed Python package for you. If installing on Windows you need to install RTools before installing the R package.

install.packages('reticulate') # Install R package for interacting with Python
reticulate::install_miniconda() # Install Python
reticulate::py_install(c('gdal', 'rasterio', 'fiona')) # Install spatial dependencies via conda
reticulate::conda_remove('r-reticulate', packages = c('mkl')) # Remove package that causes conflicts on Windows (and maybe macOS)
reticulate::py_install('DeepForest', pip=TRUE) # Install the Python retriever package
devtools::install_github('weecology/deepforestr') # Install the R package for running the retriever
install.packages('raster') # For visualizing output for rasters

After running these commands restart R.

Advanced Installation for Python Users#

If you are using Python for other tasks you can use deepforestr with your existing Python installation (though the basic installation above will still work by creating a separate miniconda install and Python environment).

Install the DeepForest Python package#

Install the DeepForest Python package into your prefered Python environment using pip:

pip install DeepForest

Select the Python environment to use in R#

deepforestr will try to find Python environments with DeepForest (see the reticulate documentation on order of discovery for more details) installed. Alternatively you can select a Python environment to use when working with deepforestr (and other packages using reticulate).

The most robust way to do this is to set the RETICULATE_PYTHON environment variable to point to the preferred Python executable:

Sys.setenv(RETICULATE_PYTHON = "/path/to/python")

This command can be run interactively or placed in .Renviron in your home directory.

Alternatively you can select the Python environment through the reticulate package for either conda:


or virtualenv:


You can check to see which Python environment is being used with:


Install the deepforestr R package#

remotes::install_github("weecology/deepforestr") # development version from GitHub

Getting Started#

Load the current release model#


model = df_model()

Predict a single image#

Return the bounding boxes in a data frame#

image_path = get_data("OSBS_029.png") # Gets a path to an example image
bounding_boxes = model$predict_image(path=image_path, return_plot=FALSE)

Return an image for visualization#

image_path = get_data("OSBS_029.png") # Gets a path to an example image
predicted_image = model$predict_image(path=image_path, return_plot=TRUE)

Predict a tile#

Return the bounding boxes in a data frame#

raster_path = get_data("OSBS_029.tif") # Gets a path to an example raster tile
bounding_boxes = model$predict_tile(raster_path, return_plot=FALSE)

Return an image for visualization#

raster_path = get_data("OSBS_029.tif") # Gets a path to an example raster tile
predicted_raster = model$predict_tile(raster_path, return_plot=TRUE, patch_size=300L, patch_overlap=0.25)

Note at patch_size is an integer value in Python and therefore needs to have the L at the end of the number in R to make it an integer.

Predict a set of annotations#

csv_file = get_data("testfile_deepforest.csv")
root_dir = get_data(".")
boxes = model$predict_file(csv_file=csv_file, root_dir = root_dir, savedir=".")


Set the training configuration#

annotations_file = get_data("testfile_deepforest.csv")

model$config$epochs = 1
model$config["save-snapshot"] = FALSE
model$config$train$csv_file = annotations_file
model$config$train$root_dir = get_data(".")

Optionally turn on fast_dev_run for testing and debugging:

model$config$train$fast_dev_run = TRUE

Train the model#



csv_file = get_data("OSBS_029.csv")
root_dir = get_data(".")
results = model$evaluate(csv_file, root_dir, iou_threshold = 0.4)

Saving & Loading Models#

Saving a model after training#


Loading a saved model#

new_model = df_model()
after = new_model$load_from_checkpoint("")
pred_after_reload = after$predict_image(path = img_path)

Note that when reloading models, you should carefully inspect the model parameters, such as the score_thresh and nms_thresh. These parameters are updated during model creation and the config file is not read when loading from checkpoint! It is best to be direct to specify after loading checkpoint.