coco panoptic eomt large 640
简介
核心亮点
- 全景分割能力，同时识别物体实例与背景
- 基于 COCO 标准数据集，通用场景识别度高
- 640 分辨率输入，在精度与速度间取得平衡
- 采用 MIT 许可，对商业化部署非常友好
使用方法
# 安装 Hugging Face transformers
pip install transformers torch# 使用 transformers 加载模型
from transformers import AutoModel, AutoTokenizer
model = AutoModel.from_pretrained("tue-mps/coco_panoptic_eomt_large_640")
tokenizer = AutoTokenizer.from_pretrained("tue-mps/coco_panoptic_eomt_large_640")Hugging Face 下载
我们推荐使用命令行或者 Hugging Face Hub SDK 来进行模型的下载。
操作指引：在下载前，请先通过如下命令安装 huggingface_hub：
pip install -U huggingface_hub命令行下载
下载完整模型库
huggingface-cli download tue-mps/coco_panoptic_eomt_large_640下载单个文件到指定本地文件夹（以下载 config.json 到当前路径下 ./dir 目录为例）
huggingface-cli download tue-mps/coco_panoptic_eomt_large_640 config.json --local-dir ./dirSDK 下载
# 模型下载
from huggingface_hub import snapshot_download
model_dir = snapshot_download('tue-mps/coco_panoptic_eomt_large_640')Git 下载
请确保 lfs 已经被正确安装
git lfs install
git clone https://huggingface.co/tue-mps/coco_panoptic_eomt_large_640如果您希望跳过 lfs 大文件下载，可以使用如下命令
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/tue-mps/coco_panoptic_eomt_large_640模型文件托管在 Hugging Face Hub，使用 HF CLI / SDK / Git 直接下载，不经过本站。
PyTorch / Transformers 使用
安装 Transformers
pip install -U transformers torch模型加载和推理
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained('tue-mps/coco_panoptic_eomt_large_640')
tokenizer = AutoTokenizer.from_pretrained('tue-mps/coco_panoptic_eomt_large_640')完整文档
---
library_name: transformers
license: mit
tags:
- vision
- image-segmentation
- pytorch
---
EoMT
EoMT (Encoder-only Mask Transformer) is a Vision Transformer (ViT) architecture designed for high-quality and efficient image segmentation. It was introduced in the CVPR 2025 highlight paper:
Your ViT is Secretly an Image Segmentation Model
by Tommie Kerssies, Niccolò Cavagnero, Alexander Hermans, Narges Norouzi, Giuseppe Averta, Bastian Leibe, Gijs Dubbelman, and Daan de Geus.
> Key Insight: Given sufficient scale and pretraining, a plain ViT along with additional few params can perform segmentation without the need for task-specific decoders or pixel fusion modules. The same model backbone supports semantic, instance, and panoptic segmentation with different post-processing 🤗
The original implementation can be found in this repository.
The HuggingFace model page is available at this link.
---
How to use
Here is how to use this model for Panotpic Segmentation:
import matplotlib.pyplot as plt
import requests
import torch
from PIL import Image
from transformers import EomtForUniversalSegmentation, AutoImageProcessor
model_id = "tue-mps/coco_panoptic_eomt_large_640"
processor = AutoImageProcessor.from_pretrained(model_id)
model = EomtForUniversalSegmentation.from_pretrained(model_id)
image = Image.open(requests.get("http://images.cocodataset.org/val2017/000000039769.jpg", stream=True).raw)
inputs = processor(
images=image,
return_tensors="pt",
)
with torch.inference_mode():
outputs = model(**inputs)
Prepare the original image size in the format (height, width)
target_sizes = [(image.height, image.width)]
Post-process the model outputs to get final segmentation prediction
preds = processor.post_process_panoptic_segmentation(
outputs,
target_sizes=target_sizes,
)
Visualize the panoptic segmentation mask
plt.imshow(preds[0]["segmentation"])
plt.axis("off")
plt.title("Panoptic Segmentation")
plt.show()Citation
If you find our work useful, please consider citing us as:@inproceedings{kerssies2025eomt,
author = {Kerssies, Tommie and Cavagnero, Niccolò and Hermans, Alexander and Norouzi, Narges and Averta, Giuseppe and Leibe, Bastian and Dubbelman, Gijs and de Geus, Daan},
title = {Your ViT is Secretly an Image Segmentation Model},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2025},
}