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
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Download README.md from unaibai/spat-instance: direct link, hf CLI and curl.
- Browser
- Download file 669 Bytes
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https://huggingface.co/unaibai/spat-instance/resolve/main/README.md
- Command line
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hf download hf://unaibai/spat-instance/README.md
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curl -L -o README.md https://huggingface.co/unaibai/spat-instance/resolve/main/README.md
669 Bytes
metadata
library_name: transformers
pipeline_tag: image-segmentation
tags:
- instance-segmentation
- mask2former
BOOST
Mask2Former (facebook/mask2former-swin-large-coco-instance) fine-tuned for instance segmentation of: booster, spat.
Trained on full-resolution tiles of 1024 px at image scale 1. Use sliding-window inference over the whole image (see deploy_config.json); running the model on a downscaled full image will miss small objects.
Test results (held-out split)
| mask AP | AP50 | AP75 | precision | recall | F1 |
|---|---|---|---|---|---|
| 0.458 | 0.643 | 0.509 | 0.719 | 0.839 | 0.774 |
Precision, recall and F1 at score >= 0.95, IoU >= 0.5.