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:
- 380d06e469d410e88195a386ad8eac738a7249a831c418a1c7fc1315317095d8
- Size of remote file:
- 4.94 GB
- SHA256:
- a417a84fac860be7e6c606a3b1b66462dc481199df410655162415343aee6585
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