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This model was proposed in DynaFLIP: Rethinking Robotics Perception via Tri-Modal-Dynamics Guided Representation.
Fonte do modelo
Descrição da fonte
This model was proposed in DynaFLIP: Rethinking Robotics Perception via Tri-Modal-Dynamics Guided Representation.
The model is compatible with Transformers:
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1 fonteVerificado 17 de set.
Artefatos de modelo
1 artefatoTrechos de fonte
2 trechos
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)
@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},
}
--- 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