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LeafMachine2_Reference.yaml
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LeafMachine2_Reference.yaml
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# Cite:
# Weaver, W. N., and S. A.Smith. 2023. From leaves to labels: Building modular
# machine learning networks for rapid herbarium specimen analysis with LeafMachine2.
# Applications in Plant Sciences. e11548. https://doi.org/10.1002/aps3.11548
# To use default value, set to null
leafmachine:
do:
check_for_illegal_filenames: False
check_for_corrupt_images_make_vertical: False
run_leaf_processing: True
print:
verbose: True
optional_warnings: True
logging:
log_level: null
# Overall Project Input Settings
project:
# Project Output Dir
dir_output: 'D:\D_Desktop\LM2' #'D:/D_Desktop/Loasaceae_Rosy_Out' #'D:\T_Downloads\Chuck_earlyJune23_FieldPrismimages\LM2' # 'D:/Dropbox/LM2_Env/Image_Datasets/TEST_LM2' # 'D:\D_Desktop\Richie\Richie_Out'
run_name: 'Manuscript_Images_epoch30ish_noPartial_L02_SEG90_LV2e70' #'images_short_TEST' #'images_short_landmark'
# If location is GBIF, set the config in:
# LeafMachine2/configs/config_download_from_GBIF_all_images_in_file OR
# LeafMachine2/configs/config_download_from_GBIF_all_images_in_filter
image_location: 'local' # str |FROM| 'local' or 'GBIF'
GBIF_mode: 'all' # str |FROM| 'all' or 'filter'. All = config_download_from_GBIF_all_images_in_file.yml Filter = config_download_from_GBIF_all_images_in_filter.yml
batch_size: 20 #null # null = all
num_workers: 2 # int |DEFAULT| 4 # More is not always better. Most hardware loses performance after 4
# If *not* using local, set the following 4 to nullMy Project 92806
# If providing occ and img, then a combined will be generated
# If providing combined, then set images and occ to null
dir_images_local: 'D:\Dropbox\LM2_Env\Image_Datasets\Manuscript_Images' #'D:/D_Desktop/Loasaceae_Rosy' #'D:\D_Desktop\Richie\Imgs' #'D:/Dropbox/LM2_Env/Image_Datasets/Acacia/Acacia_prickles_4-26-23_LANCZOS/images/short' #'D:\D_Desktop\Richie\Imgs' #'home/brlab/Dropbox/LM2_Env/Image_Datasets/Manuscript_Images' # 'D:\Dropbox\LM2_Env\Image_Datasets\SET_FieldPrism_Test\TESTING_OUTPUT\Images_Processed\REU_Field_QR-Code-Images\Cannon_Corrected\Images_Corrected' # 'F:\temp_3sppFamily' # 'D:/Dropbox/LM2_Env/Image_Datasets/GBIF_BroadSample_3SppPerFamily' # SET_Diospyros/images_short' # 'D:/Dropbox/LM2_Env/Image_Datasets/SET_Diospyros/images_short' #'D:\Dropbox\LM2_Env\Image_Datasets\GBIF_BroadSample_Herbarium' #'D:/Dropbox/LM2_Env/Image_Datasets/SET_Diospyros/images_short' # str | only for image_location:local | full path for directory containing images
path_combined_csv_local: null # 'D:/Dropbox/LM2_Env/Image_Datasets/SET_Diospyros/DWC/combined_diospyros.csv' #'D:/Dropbox/LM2_Env/Image_Datasets/SET_Diospyros/DWC_short/combined_diospyros_short.csv' # str | only for image_location:local | full path for directory containing images
path_occurrence_csv_local: null # 'D:\Dropbox\LM2_Env\Image_Datasets\SET_Acacia\armature_occurrence.csv' # str | only for image_location:local | full path for directory containing images
path_images_csv_local: null # 'D:\Dropbox\LM2_Env\Image_Datasets\SET_Acacia\prickles_images.csv' # str | only for image_location:local | full path for directory containing images
use_existing_plant_component_detections: null # 'D:\D_Desktop\MOR_Fagaceae_OUT\MOR_Fagaceae_epoch30ish\Plant_Components\labels' #'D:/Dropbox/LM2_Env/Image_Datasets/TEST_LM2/campus/Plant_Components/labels' #'F:\LM2_parallel_test\FULL_TEST_GBIF_BroadSample_3SppPerFamily_smalle25wDoc\Plant_Components\labels' #'/home/brlab/Dropbox/LM2_Env/Image_Datasets/TEST_LM2/RULERVAL_3sppPerFam/Plant_Components/labels' # 'D:\Dropbox\LM2_Env\Image_Datasets\TEST_LM2\images_short\Plant_Components\labels' # null for unprocessed images
use_existing_archival_component_detections: null #'D:\D_Desktop\MOR_Fagaceae_OUT\MOR_Fagaceae_epoch30ish\Archival_Components\labels' #'D:/Dropbox/LM2_Env/Image_Datasets/TEST_LM2/campus/Archival_Components/labels' #'F:\LM2_parallel_test\FULL_TEST_GBIF_BroadSample_3SppPerFamily_smalle25wDoc\Archival_Components\labels' #'/home/brlab/Dropbox/LM2_Env/Image_Datasets/TEST_LM2/RULERVAL_3sppPerFam/Archival_Components/labels' # 'D:\Dropbox\LM2_Env\Image_Datasets\TEST_LM2\images_short\Archival_Components\labels' # null for unprocessed images
process_subset_of_images: False
dir_images_subset: '/media/brlab/e5827490-fff7-471f-a73d-e7ae3ea264bf/LM2_TEST/SET_Diospyros/images_10perSpecies'
n_images_per_species: 10
species_list: '/home/brlab/Dropbox/LM2_Env/Image_Datasets/SET_Diospyros/10_images_per_species.csv'
cropped_components:
# empty list for all, add to list to IGNORE, lowercase, comma seperated
# archival |FROM|
# ruler, barcode, colorcard, label, map, envelope, photo, attached_item, weights
# plant |FROM|
# leaf_whole, leaf_partial, leaflet, seed_fruit_one, seed_fruit_many, flower_one, flower_many, bud, specimen, roots, wood
do_save_cropped_annotations: False
save_cropped_annotations: ['label'] #, 'ruler', 'barcode','leaf_whole', 'leaf_partial', 'leaflet', 'seed_fruit_one', 'seed_fruit_many', 'flower_one', 'flower_many', 'bud'] # 'save_all' to save all classes
save_per_image: False # creates a folder for each image, saves crops into class-names folders # TODO
save_per_annotation_class: True # saves crops into class-names folders
binarize_labels: False
binarize_labels_skeletonize: False
modules:
armature: False
specimen_crop: False
data:
save_json_rulers: False
save_json_measurements: False
save_individual_csv_files_rulers: False
save_individual_csv_files_measurements: False
save_individual_csv_files_landmarks: False ########
save_individual_efd_files: False ########
include_darwin_core_data_from_combined_file: False
do_apply_conversion_factor: True ###########################
overlay:
save_overlay_to_pdf: False
save_overlay_to_jpgs: True
overlay_dpi: 300 # int |FROM| 100 to 300
overlay_background_color: 'black' # str |FROM| 'white' or 'black'
show_archival_detections: True
show_plant_detections: True
show_segmentations: True
show_landmarks: True
ignore_archival_detections_classes: []
ignore_plant_detections_classes: ['leaf_partial',] #['leaf_whole', 'leaf_partial', 'specimen']
ignore_landmark_classes: []
line_width_archival: 12 # int 2
line_width_plant: 12 # int 6
line_width_seg: 12 # int # thick = 12
line_width_efd: 12 # int # thick = 3
alpha_transparency_archival: 0.3 # float between 0 and 1
alpha_transparency_plant: 0
alpha_transparency_seg_whole_leaf: 0.4
alpha_transparency_seg_partial_leaf: 0.3
# Configure Plant Component Detector
# Update March 22, 2024
# Choose from these setting:
# if PCD_version == "Original (Version 2.1)":
# st.session_state.config['leafmachine']['plant_component_detector']['detector_type'] = 'Plant_Detector'
# st.session_state.config['leafmachine']['plant_component_detector']['detector_version'] = 'PLANT_GroupAB_200'
# st.session_state.config['leafmachine']['plant_component_detector']['detector_iteration'] = 'PLANT_GroupAB_200'
# st.session_state.config['leafmachine']['plant_component_detector']['detector_weights'] = 'best.pt'
# elif PCD_version == "LeafPriority (Version 2.2)":
# st.session_state.config['leafmachine']['plant_component_detector']['detector_type'] = 'Plant_Detector'
# st.session_state.config['leafmachine']['plant_component_detector']['detector_version'] = 'PLANT_LeafPriority'
# st.session_state.config['leafmachine']['plant_component_detector']['detector_iteration'] = 'PLANT_LeafPriority'
# st.session_state.config['leafmachine']['plant_component_detector']['detector_weights'] = 'LeafPriority.pt'
plant_component_detector:
# ./leafmachine2/component_detector/runs/train/detector_type/detector_version/detector_iteration/weights/detector_weights
detector_type: 'Plant_Detector'
detector_version: 'PLANT_LeafPriority'
detector_iteration: 'PLANT_LeafPriority'
detector_weights: 'LeafPriority.pt'
minimum_confidence_threshold: 0.5 # 0.5 = default
do_save_prediction_overlay_images: True
ignore_objects_for_overlay: [] #['leaf_partial'] # list[str] # list of objects that can be excluded from the overlay # all = null
# Configure Archival Component Detector
archival_component_detector:
# ./leafmachine2/component_detector/runs/train/detector_type/detector_version/detector_iteration/weights/detector_weights
detector_type: 'Archival_Detector'
detector_version: 'PREP_final'
detector_iteration: 'PREP_final'
detector_weights: 'best.pt'
minimum_confidence_threshold: 0.5 # 0.5 = default
do_save_prediction_overlay_images: True
ignore_objects_for_overlay: [] # list[str] # list of objects that can be excluded from the overlay # all = null
# Configure Plant Component Detector
armature_component_detector:
# ./leafmachine2/component_detector/runs/train/detector_type/detector_version/detector_iteration/weights/detector_weights
detector_type: 'Armature_Detector'
detector_version: 'ARM_A_1000'
detector_iteration: 'ARM_A_1000'
detector_weights: 'best.pt'
minimum_confidence_threshold: 0.5 #0.2
do_save_prediction_overlay_images: True
ignore_objects_for_overlay: []
landmark_detector:
# ./leafmachine2/component_detector/runs/train/detector_type/detector_version/detector_iteration/weights/detector_weights
landmark_whole_leaves: True
landmark_partial_leaves: False
detector_type: 'Landmark_Detector_YOLO'
detector_version: 'Landmarks'
# detector_iteration: 'Landmarks'
detector_iteration: 'Landmarks_V2'
detector_weights: 'best.pt'
minimum_confidence_threshold: 0.02
do_save_prediction_overlay_images: True ######################################################################################################
ignore_objects_for_overlay: [] # list[str] # list of objects that can be excluded from the overlay # all = null
use_existing_landmark_detections: null #'D:/Dropbox/LM2_Env/Image_Datasets/TEST_LM2/images_short_landmark/Plant_Components/Landmarks_Whole_Leaves_Overlay' # null for unprocessed images
do_show_QC_images: False
do_save_QC_images: True
do_show_final_images: False
do_save_final_images: True
landmark_detector_armature:
# ./leafmachine2/component_detector/runs/train/detector_type/detector_version/detector_iteration/weights/detector_weights
upscale_factor: 10
detector_type: 'Landmark_Detector_YOLO'
detector_version: 'Landmarks_Arm_A_200'
detector_iteration: 'Landmarks_Arm_A_200'
detector_weights: 'last.pt'
minimum_confidence_threshold: 0.06
do_save_prediction_overlay_images: True ######################################################################################################
ignore_objects_for_overlay: [] # list[str] # list of objects that can be excluded from the overlay # all = null
use_existing_landmark_detections: null #'D:/Dropbox/LM2_Env/Image_Datasets/TEST_LM2/images_short_landmark/Plant_Components/Landmarks_Whole_Leaves_Overlay' # null for unprocessed images
do_show_QC_images: True
do_save_QC_images: True
do_show_final_images: True
do_save_final_images: True
ruler_detection:
detect_ruler_type: True # only True right now
ruler_detector: 'ruler_classifier_38classes_v-1.pt' # MATCH THE SQUARIFY VERSIONS
ruler_binary_detector: 'model_scripted_resnet_720_withCompression.pt' # MATCH THE SQUARIFY VERSIONS
minimum_confidence_threshold: 0.4
save_ruler_validation: True # save_ruler_class_overlay: True
save_ruler_validation_summary: True # save_ruler_overlay: True
save_ruler_processed: False # this is the angle-corrected rgb ruler
# Configure Archival Component Detector
leaf_segmentation:
segment_whole_leaves: True
segment_partial_leaves: False ### ***
keep_only_best_one_leaf_one_petiole: True
save_segmentation_overlay_images_to_pdf: True
save_each_segmentation_overlay_image: True
save_individual_overlay_images: True # Not recommended, will create many files. Useful for QC or presentation
overlay_line_width: 1 # int |DEFAULT| 1
use_efds_for_png_masks: False # requires that you calculate efds --> calculate_elliptic_fourier_descriptors: True
save_masks_color: True
save_full_image_masks_color: True
save_rgb_cropped_images: True
find_minimum_bounding_box: True
calculate_elliptic_fourier_descriptors: True # bool |DEFAULT| True
elliptic_fourier_descriptor_order: null # int |DEFAULT| 40
segmentation_model: 'GroupB_Dataset_100000_Iter_1176PTS_512Batch_smooth_l1_LR00025_BGR'
minimum_confidence_threshold: 0.9 #0.7
generate_overlay: False
overlay_dpi: 300 # int |FROM| 100 to 300
overlay_background_color: 'black' # str |FROM| 'white' or 'black'
# do_save_prediction_overlay_images: False
images_to_process:
ruler_directory: 'D:/Dropbox/LM2_Env/Image_Datasets/GroundTruth_CroppedAnnotations_Group1_Partial/PREP/Ruler'
component_detector_train:
plant_or_archival: 'PLANT' # str |FROM| 'PLANT' or 'ARCHIVAL' or 'LANDMARK' or 'ARM'
model_options:
data: '/home/brlab/Dropbox/LeafMachine2/leafmachine2/component_detector/datasets/PLANT_Group3_EverythingSomeNotReviewed/PLANT_Group3_EverythingSomeNotReviewed.yaml' #'/home/brlab/Dropbox/LeafMachine2/leafmachine2/component_detector/datasets/Landmarks_Arm/Landmarks_Arm.yaml' # 'D:/Dropbox/LeafMachine2/leafmachine2/component_detector/datasets/PLANT_Botany_Small/PLANT_Botany_Small.yaml'
project: null # str |DEFAULT| 'runs/train'
name: 'PLANT_Group3_EverythingSomeNotReviewed' # str |DEFAULT| 'exp' #PLANT_GroupAB_200
weights: '/home/brlab/Dropbox/LeafMachine2/leafmachine2/component_detector/runs/train/Plant_Detector/PLANT_GroupAB_200/PLANT_GroupAB_200/weights/best.pt' #'/home/brlab/Dropbox/LeafMachine2/leafmachine2/component_detector/architecture/yolov5n.pt' #'/home/brlab/Dropbox/LeafMachine2/leafmachine2/component_detector/runs/train/Landmark_Detector_YOLO/Landmarks/Landmarks/weights/best.pt' #'/home/brlab/Dropbox/LeafMachine2/leafmachine2/component_detector/runs/train/Landmark_Detector_YOLO/Landmarks/Landmarks2/weights/best.pt' # '/home/brlab/Dropbox/LeafMachine2/leafmachine2/component_detector/runs/train/Plant_Detector/PLANT_GroupA/weights/best.pt' # '/home/brlab/Dropbox/LeafMachine2/leafmachine2/component_detector/runs/train/Plant_Detector/PLANT_GroupB/PLANT_GroupB/weights/last.pt' # null # str |DEFAULT| yolov5x6.pt |FROM| small to big: yolov5n yolov5s yolov5m yolov5l yolov5x yolov5x6
optimizer: null # str SGD |FROM| 'SGD', 'Adam', 'AdamW'
epochs: 200 # int |DEFAULT| 300 | normally 200 for landmarks, 200 for bboxes
batch_size: 28 # int |DEAFULT| 8 # 14 for 48GB VRAM
patience: 0 # int |DEFAULT| 100
save_period: 10 # int |DEFAULT| 1
imgsz: 1280 # int |DEFAULT| 512 #1280
workers: null # int |DEFAULT| 8
hyp: null # str |DEFAULT| 'data/hyps/hyp.scratch-low.yaml' |FROM| /data/hyps/
resume: null # bool |DEFAULT| False
rect: null # bool |DEFAULT| False
cache: null # bool |DEFAULT| True, disable if computer ram is too small
freeze: null # list[int] |DEFAULT| [0] |FORMAT| [0 1 2 3 4 5] to freeze first 6 layers, backbone=10, first3=0 1 2
evolve: null # int |DEFAULT| 0 # 100+ if evolving hyp
device: null # list[int] |DEFAULT| '' |FROM| cuda device, i.e. 0 or 0,1,2,3 or cpu
upload_dataset: null # bool |DEFAULT| False
# Recommend using defaults for below:
nosave: null # bool |DEFAULT| False
noval: null # bool |DEFAULT| False
cfg_model: null # str |DEFAULT|
noautoanchor: null # bool |DEFAULT| False
noplots: null # bool |DEFAULT| False
bucket: null # str |DEFAULT| ''
image_weights: null # bool |DEFAULT| False
single_cls: null # bool |DEFAULT| False
multi_scale: null # bool |DEFAULT| False
sync_bn: null # bool |DEFAULT| True
exist_ok: null # bool |DEFAULT| False
quad: null # bool |DEFAULT| False
cos_lr: null # bool |DEFAULT| False
label_smoothing: null # float |DEFAULT| 0.0
local_rank: null # int |DEFAULT| -1 |NOTICE| DDP parameter, do not modify
ruler_train:
model_name: 'model_scripted_resnet_720_withCompress_38classes.pt' # end in .pt
do_overwrite_model: False # bool
dir_train: '/home/brlab/Dropbox/LeafMachine2/data/ruler_classifier_training_data/Rulers_ByType_v2_Squarify_720px_withCompress_38classes' #'E:/TEMP_ruler/Rulers_ByType_Squarify_720px'
dir_val: null
split_train_dir_automatically: null # bool |DEFAULT| True
split_train_dir_consistently: null # bool |DEFAULT| True
save_all_checkpoints: null # bool |DEFAULT| False
seed_for_random_split: null # int |DEFAULT| 2022
n_gpus: 2 # int |DEFAULT| 1
n_machines: null # int |DEFAULT| 1 -- changing this will require effort
default_timeout_minutes: null # int |DEFAULT| 30
img_size: null # int |DEFAULT| 720
use_cuda: null # bool |DEFAULT| True
batch_size: 18 # int |DEFAULT| 18
learning_rate: null # float |DEFAULT| 0.001
n_epochs: 50 # int |DEFAULT| 20
print_every: null # int |DEFAULT| 5
num_classes: null # int |DEFAULT| 22
ruler_binarization_train:
model_name: 'model_scripted_resnet_720_withCompression.pt' # end in .pt
do_overwrite_model: True # bool
dir_train: '/home/brlab/Dropbox/LM2_Env/Image_Datasets/GroundTruth_Ruler_Binarization/binary_classifier_training' #'E:/TEMP_ruler/Rulers_ByType_Squarify_720px'
dir_val: null
split_train_dir_automatically: null # bool |DEFAULT| True
split_train_dir_consistently: null # bool |DEFAULT| True
save_all_checkpoints: null # bool |DEFAULT| False
seed_for_random_split: null # int |DEFAULT| 2022
n_gpus: 2 # int |DEFAULT| 1
n_machines: null # int |DEFAULT| 1 -- changing this will require effort
default_timeout_minutes: null # int |DEFAULT| 30
img_size: null # int |DEFAULT| 720
use_cuda: null # bool |DEFAULT| True
batch_size: 18 # int |DEFAULT| 18
learning_rate: null # float |DEFAULT| 0.001
n_epochs: 50 # int |DEFAULT| 20
print_every: null # int |DEFAULT| 5
num_classes: null # int |DEFAULT| 22
ruler_DocEnTR_train:
data_path: '/home/brlab/Desktop/binary_classifier_training_original_img' #'F:/binary_classifier_training_original_img'
dir_save: '/home/brlab/Dropbox/LeafMachine2/leafmachine2/machine/DocEnTR/model_zoo/weights_wDocEnTR'
split_size: 256 # int |DEFAULT| 256 |FROM| 128 or 256
vit_patch_size: 8 # int |DEFAULT| 8 |FROM| 8, 16
vit_model_size: 'small' # str |DEFAULT| 'small' |FROM| 'small', 'base', 'large'
testing_dataset: '2016' # str
validation_dataset: '2016' # str
batch_size: 64 # int |DEFAULT| 8
epochs: 30 # int |DEFAULT| 101
model_weights_path: ''
gpu_id: 1
segmentation_train:
dir_images_train: 'D:/Dropbox/LeafMachine2/data/segmentation_training_data/groupB/train/images' #'D:/Dropbox/LeafMachine2/leafmachine2/segmentation/detectron2/LM2_data/leaf_whole_part1/train/images'
dir_images_val: 'D:/Dropbox/LeafMachine2/data/segmentation_training_data/groupB/val/images' #'D:/Dropbox/LeafMachine2/leafmachine2/segmentation/detectron2/LM2_data/leaf_whole_part1/val/images'
dir_images_test: 'D:/Dropbox/LM2_ENV/Image_Datasets/GroundTruth_CroppedAnnotations_Group1_Partial/PLANT/Leaf_WHOLE' #'D:/Dropbox/LM2_ENV/Image_Datasets/GroundTruth_CroppedAnnotations_Group1_Partial/PLANT/Leaf_WHOLE'
dir_out: 'D:/Dropbox/LeafMachine2/leafmachine2/segmentation/models' # str -- null saves output in default location ./dectron2/models/
filename_train_json: null # str 'POLYGONS_train.json'
filename_val_json: null # str 'POLYGONS_val.json'
model_options:
# additional setting can be changed in the point-rend file --> leaf_config_pr.py
model_name: 'GroupB_Dataset_100000_Iter_1176PTS_512Batch_smooth_l1_LR00025_BGR' # str 'POC_Dataset_10000_Iter_784PTS_CIOU_WK2'
base_architecture: null # str "PR_mask_rcnn_R_50_FPN_3x" from normally from detectron2 model zoo
do_validate_in_training: True # bool |DEFAULT| True
batch_size: 24 # int |DEFAULT| 32
iterations: 100000 # int |DEFAULT| 10000
checkpoint_freq: 500 # int |DEFAULT| 1000
# apply_augmentation: False
warmup: null # int |DEFAULT| 1000
n_gpus: 2 # int |DEFAULT| 1
n_workers: null # int |DEFAULT| 8
n_machines: 1 # int |DEFAULT| 1 -- changing this will require effort
default_timeout_minutes: null # int |DEFAULT| 30
segmentation_eval:
model_name: 'GroupB_Dataset_100000_Iter_1176PTS_512Batch_smooth_l1_LR00025_BGR'
show_image: False # bool |DEFAULT| False
thresh: 0.90 # float |DEFAULT| 0.70
do_eval_training_images: True # bool |DEFAULT| True
do_eval_val_images: True # bool |DEFAULT| True
do_eval_check_directory: True # bool |DEFAULT| True
# landmark_train:
# dir_train: '/home/brlab/Dropbox/LM2_Env/Image_Datasets/GroundTruth_POINTS/POINTS_Acacia_Prickles_2023-05-01/images/train' # 'D:/Dropbox/LM2_Env/Image_Datasets/GroundTruth_POINTS/GroundTruth_POINTS/images'
# dir_val: '/home/brlab/Dropbox/LM2_Env/Image_Datasets/GroundTruth_POINTS/POINTS_Acacia_Prickles_2023-05-01/images/val' # 'D:/Dropbox/LM2_Env/Image_Datasets/GroundTruth_POINTS/GroundTruth_POINTS/images'
# dir_save: '/home/brlab/Dropbox/LeafMachine2/leafmachine2/landmarks_arm/landmark_arm_models' # 'D:/Dropbox/LeafMachine2/leafmachine2/landmarks/landmark_models'
# run_name: 'landmarks_arm_v1'
# # This will select the correct csv file that contains the GT annotations. File format should be something like this: apex_angle__train__gt.csv
# # The class (eg. apex_angle) and the version (train or test) need to be seperated by 2 underscores '__' AND the class must match the landmark:
# # Other names can be in the file name so long as the class and version can be parsed by --> string.split('__')
# landmark: null #['tip', 'middle', 'outer'] # str |DEFAULT| None |FROM| 'apex_angle', 'base_angle', 'lamina_base', 'lamina_tip', 'lamina_width', 'lobe_tip', 'midvein_trace', 'petiole_tip', 'petiole_trace', 'tip', 'middle', 'outer'
# model_options:
# resume: null #'/home/brlab/Dropbox/LeafMachine2/leafmachine2/landmarks/landmark_models/landmark_v1/lamina_width/landmark_v1_lamina_width_epoch-40.ckpt' # full path to .ckpt file to resume
# number_of_points: null # null for indeterminate. Otherwise set to an int for number of points that need to be located
# weight_to_get_correct_number_of_points: 1 # |DEFAULT| 1
# batch_size: 20 # int |DEFAULT| 32
# image_size: 256 # int |DEFAULT| 512 |FROM| 224, 256, 512
# epochs: 1000 # int |DEFAULT| 50
# log_interval: 60 # int |DEFAULT| 60. seconds between logging
# validation_frequency: 20 # int |DEFAULT| 10
# radius: 4 # int |DEFAULT| 2. Distance from GT for the prediction to be considered correct
# max_mask_pts: 100 # int |DEFAULT| 100. Subsample this number of points from the mask, so that GMM fitting runs faster
# use_gpu: True
# n_gpus: 1 # Which gpu to use. 0 if only one gpu. 1 to use second.
# learning_rate: 0.0001 # float |DEFAULT| 0.001
# optimizer: 'adam' # str |DEFAULT| 'adam' |FROM| 'sgd', 'adam'
# visdom_env: null # str
# visdom_server: null # str |DEFAULT| 'localhost'
# visdom_port: null # int |DEFAULT| 8989
# landmark_evaluate:
# dir_images: 'D:/Dropbox/LM2_Env/Image_Datasets/GroundTruth_POINTS/GroundTruth_POINTS/images' # 'D:/Dropbox/LM2_Env/Image_Datasets/GroundTruth_POINTS/GroundTruth_POINTS/images'
# dir_save: 'D:/Dropbox/LeafMachine2/leafmachine2/landmarks/landmark_detections' # 'D:/Dropbox/LeafMachine2/leafmachine2/landmarks/landmark_models'
# model: 'D:/Dropbox/LeafMachine2/leafmachine2/landmarks/landmark_models/landmark_v1'
# run_name: 'landmark_v1'
# # This will select the correct csv file that contains the GT annotations. File format should be something like this: apex_angle__train__gt.csv
# # The class (eg. apex_angle) and the version (train or test) need to be seperated by 2 underscores '__' AND the class must match the landmark:
# # Other names can be in the file name so long as the class and version can be parsed by --> string.split('__')
# landmark: 'midvein_trace' # str |DEFAULT| None |FROM| 'apex_angle', 'base_angle', 'lamina_base', 'lamina_tip', 'lamina_width', 'lobe_tip', 'midvein_trace', 'petiole_tip', 'petiole_trace'
# model_options:
# number_of_points: null # null for indeterminate. Otherwise set to an int for number of points that need to be located
# image_size: 256 # int |DEFAULT| 512 |FROM| 224, 256, 512
# radius: '4' # int |DEFAULT| 2. Distance from GT for the prediction to be considered correct
# max_mask_pts: 100 # int |DEFAULT| 100. Subsample this number of points from the mask, so that GMM fitting runs faster
# use_gpu: True
# save_heatmaps: False