Dynaflip Base

--- license: apache-2.0 library_name: transformers pipeline_tag: zero-shot-image-classification --- # DynaFLIP-base This model was proposed in [DynaFLIP: Rethinking Robotics Perception via Tri-Modal-Dynamics Guided Representation](https://huggingface.co/papers/2605.30350). ## Usage The model is compatible with Transformers: ```python from transformers import AutoModel, AutoProcessor from PIL import Image import torch REPO = "jlee-larr/dynaflip-base" dynaflip = AutoModel.from_pretrained(REPO, trust_remote_code=True).eval() processor = AutoProcessor.from_pretrained(REPO, trust_remote_code=True) image = Image.open("example.png").convert("RGB") inputs = processor(images=image, return_tensors="pt") with torch.no_grad(): v = dynaflip.vision_outputs(inputs["pixel_values"]) # v.last_hidden_state -> (B, num_patches, 768) patch tokens # v.pooler_output -> (B, 1536) CLS + mean(patches) ``` ## Citation ```bibtex @article{lee2026dynaflip, title = {DynaFLIP: Rethinking Robotics Perception via Tri-Modal-Dynamics Guided Representation}, author = {Lee, Jusuk and Lee, Seungjae and Shin, Jonghun and Jung, Hoseong and Kim, Sungha and Cho, Daesol and Kim, H. Jin and Huang, Jia-Bin and Huang, Furong}, journal = {arXiv preprint arXiv:2605.30350}, year = {2026}, } ```

Source context: 2032 downloads · 0 likes · Pipeline zero-shot-image-classification · Library transformers · Repo jlee-larr/dynaflip-base