Training

The prebuilt models will always be improved by adding data from the target area. In our work, we have found that even one hour’s worth of carefully chosen hand-annotation can yield enormous improvements in accuracy and precision.

We expect that the prebuilt model will benefit from at least some fine-tuning for the vast majority of scientific applications. We have discovered that 5-10 epochs of training with the prebuilt model are adequate. The improvement of a retraining task after 10-30 epochs has never been observed, but it is theoretically feasible if there are very big datasets with extremely varied classes.

Consider an annotations.csv file in the following format

testfile_deepforest.csv

image_path, xmin, ymin, xmax, ymax, label
OSBS_029.jpg,256,99,288,140,Tree
OSBS_029.jpg,166,253,225,304,Tree
OSBS_029.jpg,365,2,400,27,Tree
OSBS_029.jpg,312,13,349,47,Tree
OSBS_029.jpg,365,21,400,70,Tree
OSBS_029.jpg,278,1,312,37,Tree
OSBS_029.jpg,364,204,400,246,Tree
OSBS_029.jpg,90,117,121,145,Tree
OSBS_029.jpg,115,109,150,152,Tree
OSBS_029.jpg,161,155,199,191,Tree

The config file specifies the path to the CSV file that we want to use when training. The images are located in the working directory by default, and a user can provide a path to a different image directory.

# Example run with short training
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"] = os.path.dirname(annotations_file)

model.create_trainer()

For debugging, its often useful to use the fast_dev_run = True from pytorch lightning

model.config["train"]["fast_dev_run"] = True

See config for full set of available arguments. You can also pass any additional pytorch lightning argument to trainer.

To begin training, we create a pytorch-lightning trainer and call trainer.fit on the model object directly on itself. While this might look a touch awkward, it is useful for exposing the pytorch lightning functionality.

model.trainer.fit(model)

For more, see Google colab demo on model training

Video walkthrough of colab

Reducing tile size

High resolution tiles may exceed GPU or CPU memory during training, especially in dense forests. To reduce the size of each tile, use preprocess.split_raster to divide the original tile into smaller pieces and create a corresponing annotations file.

For example, this sample data raster has size 2472, 2299 pixels.

"""Split raster into crops with overlaps to maintain all annotations"""
raster = get_data("2019_YELL_2_528000_4978000_image_crop2.png")
import rasterio
src = rasterio.open(raster)
/Users/benweinstein/.conda/envs/DeepForest/lib/python3.9/site-packages/rasterio/__init__.py:220: NotGeoreferencedWarning: Dataset has no geotransform, gcps, or rpcs. The identity matrix be returned.
  s = DatasetReader(path, driver=driver, sharing=sharing, **kwargs)
src.read().shape
(3, 2472, 2299)

With 574 trees annotations

annotations = utilities.xml_to_annotations(get_data("2019_YELL_2_528000_4978000_image_crop2.xml"))
annotations.shape
(574, 6)

#Write csv to file and crop
tmpdir = tempfile.gettempdir()
annotations.to_csv("{}/example.csv".format(tmpdir), index=False)
annotations_file = preprocess.split_raster(path_to_raster=raster,
                                           annotations_file="{}/example.csv".format(tmpdir),
                                           base_dir=tmpdir,
                                           patch_size=500,
                                           patch_overlap=0.25)

# Returns a 6 column pandas array
assert annotations_file.shape[1] == 6

Now we have crops and annotations in 500 px patches for training.

Negative samples

To include images with no annotations from the target classes create a dummy row specifying the image_path, but set all bounding boxes to 0

image_path, xmin, ymin, xmax, ymax, label
myimage.png, 0,0,0,0,"Tree"

Excessive use of negative samples may have a negative impact on model performance, but when used sparingly, they can increase precision. These samples are removed from evaluation and do not contribute to the precision or recall evaluation.

Model checkpoints

Pytorch lightning allows you to save a model at the end of each epoch. By default this behevaior is turned off. To restore model checkpointing

callback = ModelCheckpoint(dirpath='temp/dir',
                                 monitor='box_recall', 
                                 mode="max",
                                 save_top_k=3,
                                 filename="box_recall-{epoch:02d}-{box_recall:.2f}")
model.create_trainer(logger=TensorBoardLogger(save_dir='logdir/'), 
                                  callbacks=[callback])
model.trainer.fit(model)

Saving and loading models

import tempfile
import pandas as pd
tmpdir = tempfile.TemporaryDirectory()

model.use_release()

#save the prediction dataframe after training and compare with prediction after reload checkpoint 
img_path = get_data("OSBS_029.png")
model.create_trainer()
model.trainer.fit(model)
pred_after_train = model.predict_image(path = img_path)

#Create a trainer to make a checkpoint
model.trainer.save_checkpoint("{}/checkpoint.pl".format(tmpdir))

#reload the checkpoint to model object
after = main.deepforest.load_from_checkpoint("{}/checkpoint.pl".format(tmpdir))
pred_after_reload = after.predict_image(path = img_path)

assert not pred_after_train.empty
assert not pred_after_reload.empty
pd.testing.assert_frame_equal(pred_after_train,pred_after_reload)

---

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. If you want to save hyperparameters, edit the deepforest_config.yml directly. This will allow the hyperparameters to be reloaded on deepforest.save_model().

---

after.model.score_thresh = 0.3

Some users have reported a pytorch lightning module error on save

In this case, just saving the torch model is an easy fix.

torch.save(model.model.state_dict(),model_path)

and restore

model = main.deepforest()
model.model.load_state_dict(torch.load(model_path))

Note that if you trained on GPU and restore on cpu, you will need the map_location argument in torch.load.

New Augmentations

DeepForest uses the same transform for train/test, so you need to encode a switch for turning the ‘augment’ off. Wrap any new augmentations like so:

def get_transform(augment):
    """This is the new transform"""
    if augment:
        transform = A.Compose([
            A.HorizontalFlip(p=0.5),
            ToTensorV2()
        ], bbox_params=A.BboxParams(format='pascal_voc',label_fields=["category_ids"]))
        
    else:
        transform = ToTensorV2()
        
    return transform
        
m = main.deepforest(transforms=get_transform)

see https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/ for more options of augmentations.