ctcn.txt

[MODEL]
name = "CTCN"
num_classes = 201
img_size = 512
concept_size = 402
num_anchors = 7
total_num_anchors = 1785
snippet_length = 1
root = '/ssd3/huangjun/Paddle/feats'

[TRAIN]
epoch = 35
filelist = 'dataset/ctcn/Activity1.3_train_rgb.listformat'
rgb = 'senet152-201cls-rgb-70.3-5seg-331data_331img_train'
flow = 'senet152-201cls-flow-60.9-5seg-331data_train'
batch_size = 16
num_threads = 8
use_gpu = True
num_gpus = 8
learning_rate = 0.0005
learning_rate_decay = 0.1
lr_decay_iter = 9000
l2_weight_decay = 1e-4
momentum = 0.9

[VALID]
filelist = 'dataset/ctcn/Activity1.3_val_rgb.listformat'
rgb = 'senet152-201cls-rgb-70.3-5seg-331data_331img_val'
flow = 'senet152-201cls-flow-60.9-5seg-331data_val'
batch_size = 16
num_threads = 8
use_gpu = True
num_gpus = 8

[TEST]
filelist = 'dataset/ctcn/Activity1.3_val_rgb.listformat'
rgb = 'senet152-201cls-rgb-70.3-5seg-331data_331img_val'
flow = 'senet152-201cls-flow-60.9-5seg-331data_val'
class_label_file = 'dataset/ctcn/labels.txt'
video_duration_file = 'dataset/ctcn/val_duration_frame.list'
batch_size = 1
num_threads = 1
score_thresh = 0.001
nms_thresh = 0.8
sigma_thresh = 0.9
soft_thresh = 0.004

[INFER]
filelist = 'dataset/ctcn/infer.list'
rgb = 'senet152-201cls-rgb-70.3-5seg-331data_331img_val'
flow = 'senet152-201cls-flow-60.9-5seg-331data_val'
batch_size = 1
num_threads = 1