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:
- f65275a7014ae8c1e03f2faa999b7016ddf925084e52ee3e478219d4e98197f9
- Size of remote file:
- 4.99 GB
- SHA256:
- 4f71b674f01ebb20a2f9d44c3ec83803a6e95a285c97d3e70d498f710829008a
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