deepforest_config.yml

### 
# Config file for DeepForest module
# The following arguments
###

### Training
### Batch size. If multi-gpu is > 1, this is the total number of images per batch across all GPUs. Must be evenly divisible by multi-gpu. 
batch_size: 1 
### Model weights to load before training. From keras.model.save_weights()
weights: None
### Retinanet backbone. See the keras-retinanet repo for options. Only resnet50 has been well explored.
backbone: resnet50
### Resize images to min size. Retinanet anchors may need to be remade if significantly reducing image size.
image-min-side: 800
##Number of GPUs to train
multi-gpu: 1
#Number of full cycles of the input data to train
epochs: 1
#Validation annotations. If training using fit_generator, these will be evaluated as a callback at the end of each epoch.
validation_annotations: None
###Freeze layers. Used for model finetuning, freeze the bottom n layers.
freeze_layers: 0
###Freeze resnet backbone entirely.
freeze_resnet: False

###Evaluation
###Score threshold, above which bounding boxes are included in evaluation predictions
score_threshold: 0.05

#Keras fit_generator methods, these do not apply to tfrecords input_type
multiprocessing: False 
workers: 1
max_queue_size: 10
random_transform: False

#save snapshot and images
###Whether to save snapshots at the end of each epoch
save-snapshot: False
#Save directory for images and snapshots
save_path: snapshots/
snapshot_path: snapshots/