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_config.yml
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#Config file for DeepLidar
#Data generation - the number of workers for dask session
num_hipergator_workers: 40
training_csvs:
- MLBS
- NIWO
- TEAK
- SJER
#LIDAR settings
min_density: 2
min_coverage: 30
#Keras
workers: 1
use_multiprocessing: False
max_queue_size: 20
num_GPUs: 2
save_image_path: /orange/ewhite/b.weinstein/retinanet/
save_snapshot_path: /orange/ewhite/b.weinstein/retinanet/
input_channels: 3
#Sliding Window Size and Ovelap
patch_size: 400
patch_overlap: 0.05
rgb_res: 0.1
#Preprocessing routines
preprocess:
zero_area: True
spatial_filter: True
field_data_path: /home/b.weinstein/DeepLidar/data/field_data.csv
#Select sites
hand_annotation_site:
- MLBS
pretraining_site:
- MLBS
evaluation_site:
- MLBS
#Training
training_images: All
training_proportion: 1
weights: /orange/ewhite/b.weinstein/retinanet/20190719_120823/resnet50_05.h5
imagenet_weights: True
batch_size: 40
epochs: 40
backbone: resnet50
shuffle_training: True
score_threshold: 0.15
#Evaluation
evaluation_images: 0
shuffle_evaluation: False
validation_percent: 5 #For hand annotation neon_MAP
#Non-max suppression, threshold for the IoU value to determine when a box should be suppressed. Higher allows more overlap.
nms_threshold: 0.1
#Data Paths
SJER:
h5: /ufrc/ewhite/b.weinstein/training_h5s/noblending/SJER/
hand_annotations:
RGB: /home/b.weinstein/DeepLidar/data/SJER/training/
LIDAR: /home/b.weinstein/DeepLidar/data/SJER/training/
training_csvs: /home/b.weinstein/TreeSegmentation/analysis/Results/detection_boxes/SJER/2018/
training:
RGB: /orange/ewhite/NeonData/SJER/DP3.30010.001/2018/FullSite/D17/2018_SJER_3/L3/Camera/Mosaic/V01/
LIDAR: /orange/ewhite/NeonData/SJER/DP1.30003.001/2018/FullSite/D17/2018_SJER_3/L1/DiscreteLidar/ClassifiedPointCloud/
evaluation:
RGB: /home/b.weinstein/DeepLidar/data/SJER/plots/
LIDAR: /home/b.weinstein/DeepLidar/data/SJER/plots/
TEAK:
h5: /ufrc/ewhite/b.weinstein/training_h5s/noblending/TEAK/
training_csvs: /home/b.weinstein/TreeSegmentation/analysis/Results/detection_boxes/TEAK/2018/
hand_annotations:
RGB: /home/b.weinstein/DeepLidar/data/TEAK/training/
LIDAR: /home/b.weinstein/DeepLidar/data/TEAK/training/
training:
RGB: /orange/ewhite/NeonData/TEAK/DP3.30010.001/2018/FullSite/D17/2018_TEAK_3/L3/Camera/Mosaic/V01/
LIDAR: /orange/ewhite/NeonData/TEAK/DP1.30003.001/2018/FullSite/D17/2018_TEAK_3/L1/DiscreteLidar/ClassifiedPointCloud/
evaluation:
RGB: /home/b.weinstein/DeepLidar/data/TEAK/plots/
LIDAR: /home/b.weinstein/DeepLidar/data/TEAK/plots/
NIWO:
h5: /ufrc/ewhite/b.weinstein/training_h5s/noblending/NIWO/
training_csvs: /home/b.weinstein/TreeSegmentation/analysis/Results/detection_boxes/NIWO/2018/
hand_annotations:
RGB: /home/b.weinstein/DeepLidar/data/NIWO/training/
LIDAR: /home/b.weinstein/DeepLidar/data/NIWO/training/
training:
RGB: /orange/ewhite/NeonData/NIWO/DP3.30010.001/2018/FullSite/D13/2018_NIWO_2/L3/Camera/Mosaic/V01/
LIDAR: /orange/ewhite/NeonData/NIWO/DP1.30003.001/2018/FullSite/D13/2018_NIWO_2/L1/DiscreteLidar/ClassifiedPointCloud/
evaluation:
RGB: /home/b.weinstein/DeepLidar/data/NIWO/plots/
LIDAR: /home/b.weinstein/DeepLidar/data/NIWO/plots/
MLBS:
h5: /ufrc/ewhite/b.weinstein/training_h5s/noblending/MLBS/
training_csvs: /home/b.weinstein/TreeSegmentation/analysis/Results/detection_boxes/MLBS/2018/
hand_annotations:
RGB: /home/b.weinstein/DeepLidar/data/MLBS/training/
LIDAR: /home/b.weinstein/DeepLidar/data/MLBS/training/
training:
RGB: /orange/ewhite/NeonData/MLBS/DP3.30010.001/2018/FullSite/D07/2018_MLBS_3/L3/Camera/Mosaic/V01/
LIDAR: /orange/ewhite/NeonData/MLBS/DP1.30003.001/2018/FullSite/D07/2018_MLBS_3/L1/DiscreteLidar/ClassifiedPointCloud/
evaluation:
RGB: /home/b.weinstein/DeepLidar/data/MLBS/plots/
LIDAR: /home/b.weinstein/DeepLidar/data/MLBS/plots/
OSBS:
evaluation:
RGB: /home/b.weinstein/DeepLidar/data/OSBS/plots/
LIDAR: /home/b.weinstein/DeepLidar/data/OSBS/plots/