# Multi-species models

DeepForest allows training on multiple species annotations.
When creating a deepforest model object, pass the designed number of classes and a label dictionary that maps each numeric class to a character label. The number of classes can be either be specified in the config, or using config_args during creation.

``` python
from deepforest import main
m = main.deepforest(config_args={"num_classes":2, "label_dict": {"Alive":0,"Dead":1}})
```

It is often, but not always, useful to start with a prebuilt model when trying to identify multiple species. This helps the model focus on learning the multiple classes and not waste data and time re-learning bounding boxes. To load the backbone and box prediction portions of the release model, but create a classification model for more than one species.

Here is an example using the alive/dead tree data stored in the package, but the same logic applies to other detectiors.

``` python
import os
from deepforest import main
from deepforest import get_data

# Initialize new Deepforest model ( the model that you will train ) with your classes.
#
# When you override the number of classes and label_dict, deepforest will
# automatically modify the pretrained model to have the correct classification head
# structure, while keeping the backbone the same.
m = main.deepforest(config_args={"model": {"name": "weecology/deepforest-tree"}, # or 'weecology/deepforest-bird'
                                 "num_classes":2,
                                 "label_dict": {"Alive":0, "Dead":1}})
assert m.model.num_classes == 2

# If you'd prefer to start with generic pretrained weights (typically from MS-COCO):
m = main.deepforest(config_args={"model": {"name": None},
                                 "num_classes":2,
                                 "label_dict": {"Tree":0, "Dead":1}})
assert m.model.num_classes == 2

m.config["train"]["csv_file"] = get_data("testfile_multi.csv")
m.config["train"]["root_dir"] = os.path.dirname(get_data("testfile_multi.csv"))
m.config["train"]["fast_dev_run"] = True
m.config["batch_size"] = 2

m.config["validation"]["csv_file"] = get_data("testfile_multi.csv")
m.config["validation"]["root_dir"] = os.path.dirname(get_data("testfile_multi.csv"))
m.config["validation"]["val_accuracy_interval"] = 1

m.create_trainer()
m.trainer.fit(m)
```

* For more on loading with state_dict: [Pytorch Docs](https://pytorch.org/tutorials/beginner/saving_loading_models.html#save-load-state-dict-recommended)