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configuration:
batch_size: 64
optimizer: torch.optim.AdamW
lr: 0.001
trainer: experiment_setup.train_loop
scorer: experiment_setup.score
model: models.clipseg.CLIPDensePredT
lr_scheduler: cosine
T_max: 20000
eta_min: 0.0001
max_iterations: 20000 # <-##########################################
val_interval: null
# dataset
dataset: datasets.phrasecut.PhraseCut
split_mode: pascal_test
mode: train
mask: text_and_crop_blur_highlight352
image_size: 352
normalize: True
pre_crop_image_size: [sample, 1, 1.5]
aug: 1new
with_visual: True
split: train
# general
mix: True
prompt: shuffle+
norm_cond: True
mix_text_min: 0.0
# model
out: 1
version: 'ViT-B/16'
extract_layers: [3, 7, 9]
reduce_dim: 64
depth: 3
loss: torch.nn.functional.binary_cross_entropy_with_logits
amp: True
test_configuration_common:
normalize: True
image_size: 352
metric: metrics.FixedIntervalMetrics
batch_size: 1
test_dataset: pascal
sigmoid: True
# max_iterations: 250
test_configuration:
-
name: pas_t
mask: text
-
name: pas_h
mask: blur3_highlight01
-
name: pas_h2
mask: crop_blur_highlight352
columns: [name,
pas_t_fgiou_best, pas_t_miou_best, pas_t_fgiou_ct,
pas_h_fgiou_best, pas_h_miou_best, pas_h_fgiou_ct,
pas_h2_fgiou_best, pas_h2_miou_best, pas_h2_fgiou_ct, pas_h2_fgiou_best_t,
train_loss, duration, date
]
individual_configurations:
- {name: rd64-uni-phrasepas5i-0, remove_classes: [pas5i, 0], negative_prob: 0.2, mix_text_max: 0.5, test_configuration: {splits: [0], custom_threshold: 0.24}}
- {name: rd64-uni-phrasepas5i-1, remove_classes: [pas5i, 1], negative_prob: 0.2, mix_text_max: 0.5, test_configuration: {splits: [1], custom_threshold: 0.24}}
- {name: rd64-uni-phrasepas5i-2, remove_classes: [pas5i, 2], negative_prob: 0.2, mix_text_max: 0.5, test_configuration: {splits: [2], custom_threshold: 0.24}}
- {name: rd64-uni-phrasepas5i-3, remove_classes: [pas5i, 3], negative_prob: 0.2, mix_text_max: 0.5, test_configuration: {splits: [3], custom_threshold: 0.24}}
- {name: rd64-phrasepas5i-0, remove_classes: [pas5i, 0], negative_prob: 0.0, test_configuration: {splits: [0], custom_threshold: 0.28}}
- {name: rd64-phrasepas5i-1, remove_classes: [pas5i, 1], negative_prob: 0.0, test_configuration: {splits: [1], custom_threshold: 0.28}}
- {name: rd64-phrasepas5i-2, remove_classes: [pas5i, 2], negative_prob: 0.0, test_configuration: {splits: [2], custom_threshold: 0.28}}
- {name: rd64-phrasepas5i-3, remove_classes: [pas5i, 3], negative_prob: 0.0, test_configuration: {splits: [3], custom_threshold: 0.28}}
# baseline
- {name: bl64-phrasepas5i-0, model: models.clipseg.CLIPDenseBaseline, remove_classes: [pas5i, 0], reduce2_dim: 64, negative_prob: 0.0, test_configuration: {splits: [0], custom_threshold: 0.24}}
- {name: bl64-phrasepas5i-1, model: models.clipseg.CLIPDenseBaseline, remove_classes: [pas5i, 1], reduce2_dim: 64, negative_prob: 0.0, test_configuration: {splits: [1], custom_threshold: 0.24}}
- {name: bl64-phrasepas5i-2, model: models.clipseg.CLIPDenseBaseline, remove_classes: [pas5i, 2], reduce2_dim: 64, negative_prob: 0.0, test_configuration: {splits: [2], custom_threshold: 0.24}}
- {name: bl64-phrasepas5i-3, model: models.clipseg.CLIPDenseBaseline, remove_classes: [pas5i, 3], reduce2_dim: 64, negative_prob: 0.0, test_configuration: {splits: [3], custom_threshold: 0.24}}
# ViT
- {name: vit64-uni-phrasepas5i-0, remove_classes: [pas5i, 0], model: models.vitseg.VITDensePredT, negative_prob: 0.2, mix_text_max: 0.5, lr: 0.0001, test_configuration: {splits: [0], custom_threshold: 0.02}}
- {name: vit64-uni-phrasepas5i-1, remove_classes: [pas5i, 1], model: models.vitseg.VITDensePredT, negative_prob: 0.2, mix_text_max: 0.5, lr: 0.0001, test_configuration: {splits: [1], custom_threshold: 0.02}}
- {name: vit64-uni-phrasepas5i-2, remove_classes: [pas5i, 2], model: models.vitseg.VITDensePredT, negative_prob: 0.2, mix_text_max: 0.5, lr: 0.0001, test_configuration: {splits: [2], custom_threshold: 0.02}}
- {name: vit64-uni-phrasepas5i-3, remove_classes: [pas5i, 3], model: models.vitseg.VITDensePredT, negative_prob: 0.2, mix_text_max: 0.5, lr: 0.0001, test_configuration: {splits: [3], custom_threshold: 0.02}}
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