How do I use a pretrained model to predict an image?
====================================================

.. code-block:: python

   from deepforest import main, get_data
   import matplotlib.pyplot as plt

   # Initialize the model
   model = main.deepforest()

   # Load a pretrained tree detection model from Hugging Face
   model.load_model(model_name="weecology/deepforest-tree", revision="main")

   # Get the sample image path and predict image
   sample_image_path = get_data("OSBS_029.png")
   img = model.predict_image(path=sample_image_path, return_plot=True)

   # predict_image returns plot in BlueGreenRed (opencv style), but matplotlib likes RedGreenBlue
   # Switch the channel order for correct display
   plt.imshow(img[:,:,::-1])
   plt.show()


.. image:: ../../../www/getting_started1.png
   :align: center

.. note::

   **Please note that this video was made before the deepforest-pytorch -> deepforest name change.**

.. raw:: html

   <div style="position: relative; padding-bottom: 56.25%; height: 0;">
     <iframe src="https://www.loom.com/embed/f80ed6e3c7bd48d4a20ae32167af3d8c"
     frameborder="0" webkitallowfullscreen mozallowfullscreen allowfullscreen
     style="position: absolute; top: 0; left: 0; width: 100%; height: 100%;">
     </iframe>
   </div>

For single images, ``predict_image`` can read an image from memory or file and return predicted bounding boxes.