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config.yaml
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config.yaml
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point_track:
classes:
- 'peds'
- 'cars'
embedder:
device: 'cuda'
tensorrt:
convert: false
fp16_mode: true
int8_mode: false #not tested
max_batch_size: 10 #should be the same as n_max_objects
offsetEmb:
num_points: 1500
border_ic: 3
outputD: 32
weights:
peds: ../weights/tracker/peds.pth
cars: ../weights/tracker/cars.pth
filter:
jit: true
device: 'cuda'
n_max_objects: 10
threshold_score: 0.15
threshold_area: 0.002
class_mapping:
peds:
- 1
cars:
- 0
preprocess:
mask_scale_factor: 0.5
image_devide_factor: 255
sampler:
jit: true
device: 'cuda'
offset_max: 128
expand_ratio: 0.2
bg_num: 1000 # minimum - mask_scale_factor * H * W
fg_num: 500 # bg_num + fg_num = num_points
cat_emb:
- [+0.948, +0.456, +0.167, ] #backgroung
- [+0.100, -0.100, +0.100, ] #current object
- [+0.546, -0.619, -2.630, ] #others objects
- [-0.100, +0.100, -0.100, ] #i dont know
assigner:
peds:
optim: 'hungarian'
use_mask_iou: true
use_bbox_iou: false
iou_scale: 0.402
iou_offset: 0
euclidean_scale: 1.344
euclidean_offset: 9.447
alive_threshold: 10
association_threshold: 0.48
means_threshold: 70.0
cars:
optim: 'hungarian'
use_mask_iou: true
use_bbox_iou: false
iou_scale: 0.733
iou_offset: 0
euclidean_scale: 1.009
euclidean_offset: 9.447
alive_threshold: 30
association_threshold: 0.817
means_threshold: 90.0
yolact_edge:
tensorrt: false
score_threshold: 0.1
weights: ../weights/detector/yolact_edge_36_3360_interrupt.pth
model_config: 'yolact_edge_kitti_mots'
#device: 'cuda'