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clip:
semantic_clip_model_name: 'ViT-L/14'
semantic_pretrained_data: 'openai'
clip_model_name: "ViT-B/16"
pretrained_data: 'openai'
car:
iom_thres: 0.6
mask_threshold: 0.4
min_area_ratio: 0.2
num_iteration: 1
confidence_threshold: 0.25 # 0.2
clipes_threshold: 0.7
bg_factor: 1
stuff_bg_factor: 1
visual_prompt_type: ['gray', 'blur']
stuff_visual_prompt_type: ['gray', 'blur']
semantic_templates: ['a clean origami {}.',
'a photo of a {}.',
'This is a photo of a {}',
'There is a {} in the scene',
'There is the {} in the scene',
'a photo of a {} in the scene',
'a photo of a small {}.',
'a photo of a medium {}.',
'a photo of a large {}.',
'This is a photo of a small {}.',
'This is a photo of a medium {}.',
'This is a photo of a large {}.',
'There is a small {} in the scene.',
'There is a medium {} in the scene.',
'There is a large {} in the scene.']
bg_cls: ['ground', 'land', 'grass', 'tree', 'building',
'wall', 'sky', 'lake', 'water', 'river', 'sea',
'railway', 'railroad', 'helmet', 'cloud', 'house',
'mountain', 'ocean', 'road', 'rock', 'street',
'valley', 'bridge']
test:
algo: "car"
ds_name: "pascal_459"
seg_mode: "semantic"
split: 'validation'
data_root: "$YOUR_DATA_DIR"
# You need to extract the sam mask for the ADE dataset if use_pseudo=False
sam_mask_root: "$YOUR_SAM_MASK_DIR"
output_path: "./outputs/"
use_pseudo: True
n_class: 460
num_chunks: 1
chunk_index: 0
ignore_background: True
save_path: "./outputs" |