Instructions to use jlee-larr/dynaflip-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jlee-larr/dynaflip-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="jlee-larr/dynaflip-base", trust_remote_code=True, device_map="auto") pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("jlee-larr/dynaflip-base", trust_remote_code=True, dtype="auto", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Add link to paper, usage code snippet and pipeline tag
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README.md
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license: apache-2.0
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---
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# DynaFLIP-base
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## Citation
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license: apache-2.0
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library_name: transformers
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pipeline_tag: zero-shot-image-classification
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# DynaFLIP-base
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This model was proposed in [DynaFLIP: Rethinking Robotics Perception via Tri-Modal-Dynamics Guided Representation](https://huggingface.co/papers/2605.30350).
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## Usage
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The model is compatible with Transformers:
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```python
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from transformers import AutoModel, AutoProcessor
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repo_id = "jlee-larr/dynaflip-base"
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model = AutoModel.from_pretrained("jlee-larr/dynaflip-base")
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processor = AutoProcessor.from_pretrained("jlee-larr/dynaflip-base")
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```
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## Citation
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