Instructions to use unaibai/spat-instance with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use unaibai/spat-instance with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="unaibai/spat-instance")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, Mask2FormerForUniversalSegmentation processor = AutoImageProcessor.from_pretrained("unaibai/spat-instance") model = Mask2FormerForUniversalSegmentation.from_pretrained("unaibai/spat-instance", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 472 Bytes
bb7a3e5 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 | {
"title": "BOOST",
"description": "",
"classes": [
"booster",
"spat"
],
"scale": 1.0,
"tiling": {
"tile": 1024,
"overlap": 256,
"edge_margin": 2
},
"inference": {
"topk_per_tile": 150,
"mask_threshold": 0.5,
"nms_iou": 0.5,
"nms_iomin": 0.8,
"score_threshold_eval": 0.05
},
"score_threshold": 0.95,
"tta_chosen_on_val": [
"hflip"
],
"base_checkpoint": "facebook/mask2former-swin-large-coco-instance"
} |