[net]
# Testing
#batch=1
#subdivisions=1
# Training
batch=64
subdivisions=2
width=416
height=416
channels=3
momentum=0.9
decay=0.0005
angle=0
saturation = 1.5
exposure = 1.5
hue=.1
contrastive=1
contrastive_jit_flip=1
contrastive_color=0
learning_rate=0.00261
burn_in=1000
max_batches = 500200
policy=steps
steps=400000,450000
scales=.1,.1
[convolutional]
batch_normalize=1
filters=32
size=3
stride=2
pad=1
activation=leaky
filters=64
stride=1
[route]
layers=-1
groups=2
group_id=1
layers = -1,-2
size=1
layers = -6,-1
[maxpool]
size=2
filters=128
filters=256
filters=512
##################################
#[conv_lstm]
#batch_normalize=1
#size=3
#pad=1
#output=128
#groups=1
#peephole=0
#bottleneck=1
##time_normalizer=0.5
#lstm_activation=tanh
#activation=leaky
###########
########### to [yolo-3]
[upsample]
layers = -1, 23
########### to [yolo-2]
layers = -1, 15
#output=64
layers= 0
onlyforward=1
[local_avgpool]
size=4
stride=4
layers= -1,-3
# Contrastive embeddings
filters=1152
#activation=tanh
activation=linear
layers= -9
#onlyforward=1
filters=765
[yolo]
mask = 0,1,2,3,4,5,6,7,8
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
classes=80
num=9
jitter=.3
scale_x_y = 1.05
cls_normalizer=1.0
iou_normalizer=0.07
iou_loss=ciou
ignore_thresh = .7
truth_thresh = 1
random=0
resize=1.5
nms_kind=greedynms
beta_nms=0.6
max_delta=5
embedding_layer = -4
track_history_size = 20
sim_thresh = 0.7
dets_for_show = 2
dets_for_track = 8
track_ciou_norm = 0.2
layers= -5
[contrastive]
classes=1
temperature=1.0
yolo_layer= -2
max_delta=10
