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dimp50.py
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dimp50.py
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from pytracking.utils import TrackerParams
from pytracking.features.net_wrappers import NetWithBackbone
def parameters():
params = TrackerParams()
params.debug = 0
params.visualization = False
params.use_gpu = True
params.image_sample_size = 18*16
params.search_area_scale = 5
# Learning parameters
params.sample_memory_size = 50
params.learning_rate = 0.01
params.init_samples_minimum_weight = 0.25
params.train_skipping = 20
# Net optimization params
params.update_classifier = True
params.net_opt_iter = 10
params.net_opt_update_iter = 2
params.net_opt_hn_iter = 1
# Detection parameters
params.window_output = False
# Init augmentation parameters
params.use_augmentation = True
params.augmentation = {'fliplr': True,
'rotate': [10, -10, 45, -45],
'blur': [(3,1), (1, 3), (2, 2)],
'relativeshift': [(0.6, 0.6), (-0.6, 0.6), (0.6, -0.6), (-0.6,-0.6)],
'dropout': (2, 0.2)}
params.augmentation_expansion_factor = 2
params.random_shift_factor = 1/3
# Advanced localization parameters
params.advanced_localization = True
params.target_not_found_threshold = 0.25
params.distractor_threshold = 0.8
params.hard_negative_threshold = 0.5
params.target_neighborhood_scale = 2.2
params.dispalcement_scale = 0.8
params.hard_negative_learning_rate = 0.02
params.update_scale_when_uncertain = True
# IoUnet parameters
params.iounet_augmentation = False
params.iounet_use_log_scale = True
params.iounet_k = 3
params.num_init_random_boxes = 9
params.box_jitter_pos = 0.1
params.box_jitter_sz = 0.5
params.maximal_aspect_ratio = 6
params.box_refinement_iter = 5
params.box_refinement_step_length = 1
params.box_refinement_step_decay = 1
params.net = NetWithBackbone(net_path='dimp50.pth',
use_gpu=params.use_gpu)
params.vot_anno_conversion_type = 'preserve_area'
return params