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superpoint_train.yaml
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superpoint_train.yaml
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data:
name: 'coco'
resize: [240,320]
image_train_path: ['./data/mg_syn_coco_15e-3/images/train2017',]
label_train_path: ['./data/mg_syn_coco_15e-3/labels/train2017/',]
image_test_path: './data/mg_syn_coco_15e-3/images/test2017/'
label_test_path: './data/mg_syn_coco_15e-3/labels/test2017/'
augmentation:
photometric:
train_enable: true
test_enable: true
primitives: [
'random_brightness', 'random_contrast', 'additive_speckle_noise',
'additive_gaussian_noise', 'additive_shade', 'motion_blur' ]
params:
random_brightness: {max_abs_change: 50}
random_contrast: {strength_range: [0.5, 1.5]}
additive_gaussian_noise: {stddev_range: [0, 10]}
additive_speckle_noise: {prob_range: [0, 0.0035]}
additive_shade:
transparency_range: [-0.5, 0.5]
kernel_size_range: [100, 150]
nb_ellipses: 15
motion_blur: {max_kernel_size: 3}
homographic:
train_enable: true
test_enable: true
params:
translation: true
rotation: true
scaling: true
perspective: true
scaling_amplitude: 0.2
perspective_amplitude_x: 0.2
perspective_amplitude_y: 0.2
patch_ratio: 0.85
max_angle: 1.5707963 #3.1415926
allow_artifacts: true
valid_border_margin: 3
model:
name: 'superpoint'
using_bn: true
grid_size: 8
pretrained_model: 'none'
backbone:
backbone_type: 'VGG'
vgg:
channels: [64,64,64,64,128,128,128,128]
det_head: #detector head
feat_in_dim: 128
des_head: #descriptor head
feat_in_dim: 128
feat_out_dim: 256
det_thresh: 0.001 # 1/65
nms: 4
topk: -1
solver:
grid_size: 8
loss:
positive_margin: 1.0
negative_margin: 0.2
lambda_d: 0.05
lambda_loss: 10000
epoch: 8
base_lr: 0.001 #0.0001
train_batch_size: 2
test_batch_size: 2
save_dir: './export/'
model_name: 'sp'