model: 'MambaA'
data_name: 'YTConv_ANTICIPATION'
data_info_name: 'shard_0'
task: 'ANTICIPATION'
loss: 'ANTICIPATION' # NONUNIFORM, originally
metric: 'AP' # AP or cAP
optimizer: 'AdamW'
device: 'cuda'
feature_pretrained: 'resnet50'
root_path: './data/YTConv/shard_0'
rgb_type: 'rgb_kinetics_resnet50'
flow_type: 'audio_wav2vec2' # or 'none'?
annotation_type: 'target_perframe/case1'
video_list_path: 'data_info/YTConv_info.json'
output_path: './output_cfg'
window_size: 128
batch_size: 32 
test_batch_size: 1 
# loss_offset: 4 # loss weight a_t when L - {loss_offset} <= t <= L
num_epoch: 30
lr: 0.0001
weight_decay: 0.05
num_workers: 4
dropout: 0.20
num_classes: 2 # for YTConv pretraining, there is no 
embedding_dim: 2048
hidden_dim: 1024
num_layers: 1
stride: 4
anticipation_length: 10
actionness: False
ignore_index: -100