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Running
on
Zero
Running
on
Zero
File size: 1,205 Bytes
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---
seed: -1
dataset: MCAC
data_path: "PATH/TO/MCAC/"
log_dir: logs/
resume_path: ""
find_unused_parameters: False
MCAC_occ_limit: -1
MCAC_crop_size: -1
MCAC_max_num_classes: 5
MCAC_max_number_per_type: 300
MCAC_exclude_imgs_with_counts_over: -1 # no images with counts over the amount, -1 is the no excclusion case
MCAC_exclude_imgs_with_num_classes_over: -1 # no images with number of classes over the amount, -1 is the no excclusion case
img_channels: 3
img_size: [224, 224]
image_transforms: None
drop_last: True
counting_backbone_pretrained: True # if using pretrained vakues (only a thing for dino)
counting_backbone_unfreeze_layers: -1
counting_head: "5_32" # linearprobe
upsample_padding_mode: 'replicate' # 'zeros', 'reflect', 'replicate'
matching_type: no_match # no_match, count, density
normalize_matching: False # ONLY APPLIES TO DENSITY MATCHING
matcher_cost_p_norm: 1
matcher_cost_power: 1
counting_loss: pixelwise_mae
gtd_scale: 1
learning_rate: 3e-4
scheduler: None
weight_decay: 0
scheduler_steps: 100
scheduler_gamma: 0.5
accumulate_grad_batches: 1
train_batch_size: 32
eval_batch_size: 32
num_workers: 4
num_sanity_val_steps: -1
val_every: 1
max_epochs: 10000
max_steps: -1 |