Enhance Multiclass documentation

docs/multi_species.md

@@ -3,22 +3,29 @@
 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
 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 backboard and box prediction portions of the release model, but create a classification model for more than one species.
+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 the bird detector. 
 
-```
+
+
+``` python
+# Initialize new Deepforest model ( the model that you will train ) with your classes
 m = main.deepforest(config_args={"num_classes":2}, label_dict={"Alive":0,"Dead":1})
+
+# Inatialize Deepforest model ( the model that you will modify its regression head ) 
 deepforest_release_model = main.deepforest()
-deepforest_release_model.use_release()
+deepforest_release_model.use_release() # or use_bird_release
 
 # Extract single class backbone that will have useful features for multi-class classification
 m.model.backbone.load_state_dict(deepforest_release_model.model.backbone.state_dict())
+
+# load regression head in the new model
 m.model.head.regression_head.load_state_dict(deepforest_release_model.model.head.regression_head.state_dict())
 
 m.config["train"]["csv_file"] = get_data("testfile_multi.csv") 
@@ -33,3 +40,5 @@ 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)