CLIP_as_RNN / configs /gres.yaml
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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: 0.5
mask_threshold: 0.5
confidence_threshold: 0
clipes_threshold: 0.3
cam_text_template: 'a clean origami {}.'
color: [255, 0, 0] # red
visual_prompt_type: ['circle']
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: "gres"
split: 'val'
seg_mode: "refer"
data_root: "$YOUR_ADE_DATA_DIR"
output_path: "./outputs/"
prompts_augment: False
use_pseudo: True
use_background: False
prompts_prefix: False
prompts_augment: False
sentence_process:
mixing_alpha: 0.
save_path: "./outputs"