Thesis / config /dataset_config.py
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# Dataset configuration
dataset_config = {
'ucf24': {
# dataset
'gt_folder': './evaluator/groundtruths_ucf_jhmdb/groundtruths_ucf/',
# input size
'train_size': 224,
'test_size': 224,
# transform
'jitter': 0.2,
'hue': 0.1,
'saturation': 1.5,
'exposure': 1.5,
'sampling_rate': 1,
# cls label
'multi_hot': False, # one hot
# optimizer
'optimizer': 'adamw',
'momentum': 0.9,
'weight_decay': 5e-4,
# warmup strategy
'warmup': 'linear',
'warmup_factor': 0.00066667,
'wp_iter': 500,
# class names
'valid_num_classes': 24,
'label_map': (
'Basketball', 'BasketballDunk', 'Biking', 'CliffDiving',
'CricketBowling', 'Diving', 'Fencing', 'FloorGymnastics',
'GolfSwing', 'HorseRiding', 'IceDancing', 'LongJump',
'PoleVault', 'RopeClimbing', 'SalsaSpin', 'SkateBoarding',
'Skiing', 'Skijet', 'SoccerJuggling', 'Surfing',
'TennisSwing', 'TrampolineJumping', 'VolleyballSpiking', 'WalkingWithDog'
),
},
'ava_v2.2':{
# dataset
'frames_dir': 'frames/',
'frame_list': 'frame_lists/',
'annotation_dir': 'annotations/',
'train_gt_box_list': 'train.csv',
'val_gt_box_list': 'val.csv',
'train_exclusion_file': 'ava_train_excluded_timestamps_v2.2.csv',
'val_exclusion_file': 'ava_val_excluded_timestamps_v2.2.csv',
'labelmap_file': 'ava_action_list_v2.1_for_activitynet_2018.pbtxt', # 'ava_v2.2/ava_action_list_v2.2.pbtxt',
'class_ratio_file': 'categories_count_32s2fpsnew.json',
'backup_dir': 'C:/Users/Administrator/Downloads/YOWOv2/backup_dir',
# input size
'train_size': 224,
'test_size': 224,
# transform
'jitter': 0.2,
'hue': 0.1,
'saturation': 1.5,
'exposure': 1.5,
'sampling_rate': 1,
# cls label
'multi_hot': True, # multi hot
# train config
'optimizer': 'adamw',
'momentum': 0.9,
'weight_decay': 5e-4,
# warmup strategy
'warmup': 'linear',
'warmup_factor': 0.00066667,
'wp_iter': 500,
# class names
'valid_num_classes': 3,
'label_map': ('bully', 'victim', 'outsider')
# 'valid_num_classes': 80,
# 'label_map': (
# 'bend/bow(at the waist)', 'crawl', 'crouch/kneel', 'dance', 'fall down', # 1-5
# 'get up', 'jump/leap', 'lie/sleep', 'martial art', 'run/jog', # 6-10
# 'sit', 'stand', 'swim', 'walk', 'answer phone', # 11-15
# 'brush teeth', 'carry/hold (an object)', 'catch (an object)', 'chop', 'climb (e.g. a mountain)', # 16-20
# 'clink glass', 'close (e.g., a door, a box)', 'cook', 'cut', 'dig', # 21-25
# 'dress/put on clothing', 'drink', 'drive (e.g., a car, a truck)', 'eat', 'enter', # 26-30
# 'exit', 'extract', 'fishing', 'hit (an object)', 'kick (an object)', # 31-35
# 'lift/pick up', 'listen (e.g., to music)', 'open (e.g., a window, a car door)', 'paint', 'play board game', # 36-40
# 'play musical instrument', 'play with pets', 'point to (an object)', 'press','pull (an object)', # 41-45
# 'push (an object)', 'put down', 'read', 'ride (e.g., a bike, a car, a horse)', 'row boat', # 46-50
# 'sail boat', 'shoot', 'shovel', 'smoke', 'stir', # 51-55
# 'take a photo', 'text on/look at a cellphone', 'throw', 'touch (an object)', 'turn (e.g., a screwdriver)', # 56-60
# 'watch (e.g., TV)', 'work on a computer', 'write', 'fight/hit (a person)', 'give/serve (an object) to (a person)', # 61-65
# 'grab (a person)', 'hand clap', 'hand shake', 'hand wave', 'hug (a person)', # 66-70
# 'kick (a person)', 'kiss (a person)', 'lift (a person)', 'listen to (a person)', 'play with kids', # 71-75
# 'push (another person)', 'sing to (e.g., self, a person, a group)', 'take (an object) from (a person)', # 76-78
# 'talk to (e.g., self, a person, a group)', 'watch (a person)' # 79-80
# ),
}
}