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BiliSakura/
EUPE-ConvNeXt-B

Name: EUPE-ConvNeXt-B
Availability: InStock

image-feature-extraction

2603.22387

other

endpoints_compatible

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EUPE-ConvNeXt-B (converted)

This repository contains a converted EUPE checkpoint (from the original Facebook release) in safetensors format, prepared under BiliSakura for downstream upload and reuse.

Metadata

Architecture: ConvNeXt
Backbone family: EUPE ConvNeXt (base)
Output dim: 1024
Intended use: image feature extraction / encoder backbone
License: FAIR Research License (non-commercial)

Source

Original model: EUPE-ConvNeXt-B
Original codebase: facebookresearch/eupe
Paper: Efficient Universal Perception Encoder

Files

model.safetensors: converted checkpoint weights
config.json: architecture/config parameters
preprocessor_config.json: image preprocessing setup
transformers_eupe.py: local EUPE Transformers registration wrapper
eupe/: vendored ConvNeXt backbone used by transformers_eupe.py

Preprocessing

preprocessor_config.json uses:

Resize to 256 x 256
RGB conversion
Rescale by 1/255
Normalize with ImageNet mean/std:
- mean: [0.485, 0.456, 0.406]
- std: [0.229, 0.224, 0.225]

Quick Transformers Inference

python

import sys
import torch
from PIL import Image
from transformers import AutoImageProcessor, AutoModel

model_dir = "./EUPE-ConvNeXt-B"
sys.path.insert(0, model_dir)
from transformers_eupe import register_eupe_transformers

register_eupe_transformers()
processor = AutoImageProcessor.from_pretrained(model_dir)
model = AutoModel.from_pretrained(model_dir).eval()

image = Image.open("example.jpg").convert("RGB")
inputs = processor(images=image, return_tensors="pt")

with torch.no_grad():
    outputs = model(**inputs)

print(outputs.last_hidden_state.shape, outputs.pooler_output.shape)

Citation

If you use this model, please cite EUPE:

bibtex

@misc{zhu2026eupe,
  title={Efficient Universal Perception Encoder},
  author={Zhu, Chenchen and Suri, Saksham and Jose, Cijo and Oquab, Maxime and Szafraniec, Marc and Wen, Wei and Xiong, Yunyang and Labatut, Patrick and Bojanowski, Piotr and Krishnamoorthi, Raghuraman and Chandra, Vikas},
  year={2026},
  eprint={2603.22387},
  archivePrefix={arXiv},
  primaryClass={cs.CV},
  url={https://arxiv.org/abs/2603.22387},
}

BiliSakura/EUPE-ConvNeXt-B