Spaces:
Running
Running
File size: 2,048 Bytes
4d4dd90 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 |
data:
name: megadepth
preprocessing:
resize: 1024
side: long
square_pad: True
train_split: train_scenes_clean.txt
train_num_per_scene: 300
val_split: valid_scenes_clean.txt
val_pairs: valid_pairs.txt
min_overlap: 0.1
max_overlap: 0.7
num_overlap_bins: 3
read_depth: true
read_image: true
batch_size: 32
num_workers: 14
load_features:
do: false # enable this if you have cached predictions
path: exports/megadepth-undist-depth-r1024_pycolmap_SIFTGPU-nms3-fixed-k2048/{scene}.h5
padding_length: 2048
padding_fn: pad_local_features
data_keys: ["keypoints", "keypoint_scores", "descriptors", "oris", "scales"]
model:
name: two_view_pipeline
extractor:
name: extractors.sift
backend: pycolmap_cuda
max_num_keypoints: 2048
force_num_keypoints: True
nms_radius: 3
trainable: False
matcher:
name: matchers.lightglue
filter_threshold: 0.1
flash: false
checkpointed: true
add_scale_ori: true
input_dim: 128
ground_truth:
name: matchers.depth_matcher
th_positive: 3
th_negative: 5
th_epi: 5
allow_no_extract: True
train:
seed: 0
epochs: 50
log_every_iter: 100
eval_every_iter: 1000
lr: 1e-4
lr_schedule:
start: 30
type: exp
on_epoch: true
exp_div_10: 10
dataset_callback_fn: sample_new_items
plot: [5, 'gluefactory.visualization.visualize_batch.make_match_figures']
benchmarks:
megadepth1500:
data:
preprocessing:
side: long
resize: 1600
model:
extractor:
nms_radius: 0
eval:
estimator: opencv
ransac_th: 0.5
hpatches:
eval:
estimator: opencv
ransac_th: 0.5
model:
extractor:
max_num_keypoints: 1024
nms_radius: 0
|