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safetensors format, prepared under BiliSakura for downstream upload and reuse.ConvNeXttiny)768model.safetensors: converted checkpoint weightsconfig.json: architecture/config parameterspreprocessor_config.json: image preprocessing setuptransformers_eupe.py: local EUPE Transformers registration wrappereupe/: vendored ConvNeXt backbone used by transformers_eupe.pypreprocessor_config.json uses:256 x 2561/255[0.485, 0.456, 0.406][0.229, 0.224, 0.225]1import sys
2import torch
3from PIL import Image
4from transformers import AutoImageProcessor, AutoModel
5
6model_dir = "./EUPE-ConvNeXt-T"
7sys.path.insert(0, model_dir)
8from transformers_eupe import register_eupe_transformers
9
10register_eupe_transformers()
11processor = AutoImageProcessor.from_pretrained(model_dir)
12model = AutoModel.from_pretrained(model_dir).eval()
13
14image = Image.open("example.jpg").convert("RGB")
15inputs = processor(images=image, return_tensors="pt")
16
17with torch.no_grad():
18 outputs = model(**inputs)
19
20print(outputs.last_hidden_state.shape, outputs.pooler_output.shape)1@misc{zhu2026eupe,
2 title={Efficient Universal Perception Encoder},
3 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},
4 year={2026},
5 eprint={2603.22387},
6 archivePrefix={arXiv},
7 primaryClass={cs.CV},
8 url={https://arxiv.org/abs/2603.22387},
9}