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", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 26b1d2dda23b8b7c7c2e29ca58bca201a0bc4012e9f4e3e65f7590f6ad908e72
- Size of remote file:
- 4.95 GB
- SHA256:
- 272d9186b3b45696e68c1ffa0dce3ab7aba618de3fa61126fd04e5e9cdea462c
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