Image Segmentation
Transformers
Safetensors
English
calico
text-generation
computer-vision
semantic-segmentation
co-segmentation
part-segmentation
multi-image-reasoning
vision-language
Instructions to use PLAN-Lab/CALICO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PLAN-Lab/CALICO with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="PLAN-Lab/CALICO")# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("PLAN-Lab/CALICO", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 339af2de2a59691b4a640dbe1961e52c25ee98f495bdd4dc92077289fdc6bed5
- Size of remote file:
- 2.15 GB
- SHA256:
- 399403a0245d505e31b04076d65155999909907766e558d856e3d26e2a456b8b
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