dinov2/distillation/config/config.yaml

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student:
model_name: resnet
student_key: res5
kwargs:
depth: 50
out_features: ['res5']
freeze_at: 0
norm_type: BN
##Example for STDC
# kwargs:
# base_channels: 64
# layers: [4, 5, 3]
# block_num: 4
# block_type: cat
# use_conv_last: false
teacher:
model_name: dinov2_vitg14
teacher_key: feature_map
out_dim: 1536
n_patches: 256
feature_matcher:
out_channels: 1536
kernel_size: 1
stride: 1
padding: 0
activation: ReLU
data_transform:
n_global_crops: 2
n_local_crops: 8
global_crops_scale: [0.32, 1.0]
local_crops_scale: [0.05, 0.32]
global_crops_size: [224, 224]
local_crops_size: [224, 224]
optimizer:
type: AdamW
kwargs:
lr: 2.5e-4
betas: [0.9, 0.999]
weight_decay: 0.05
scheduler:
type: CosineAnnealingLR
kwargs:
T_max: 30
eta_min: 1e-6
monitor: val_loss
interval: epoch
frequency: 1
loss:
alpha: 1.0
beta: 1.0
train:
name: resnet50
max_epochs: 30
accelerator: gpu
devices: [1]
num_nodes: 1
strategy: auto
data_loader:
data_dir: /home/arda/data/train2017
batch_size: 32
num_workers: 8
feature_matcher:
out_channels: 1536
kernel_size: 1
stride: 1
padding: 0
checkpoints:
dirpath: checkpoints
monitor: val_similarity
mode: max
save_top_k: 3