---
title: mit-b0
canonical_url: "https://www.modelscope.cn/models/nv-community/mit-b0"
md_url: "https://www.modelscope.cn/models/nv-community/mit-b0.md"
repository: nv-community/mit-b0
last_updated: 2025-04-17
license: other
pipeline_tag: image-classification
tasks:
  - image-classification
model_type:
  - segformer
architectures:
  - SegformerForImageClassification
library_name:
  - transformer
  - tensorflow
  - pytorch
frameworks:
  - pytorch
downloads: 724
stars: 0
tags:
  - vision
---

# mit-b0

> mit-b0 - nv-community 在 ModelScope 开源的模型。SegFormer (b0-sized) encoder pre-trained-only

nv-community/mit-b0 是 ModelScope 魔搭社区上的image-classification模型，采用 other 许可。

- **Repository**: nv-community/mit-b0
- **License**: other
- **Tasks**: image-classification
- **Tags**: vision
- **Downloads**: 724
- **Stars**: 0
- **Last updated**: 2025-04-17

Source: https://www.modelscope.cn/models/nv-community/mit-b0

---

# SegFormer (b0-sized) encoder pre-trained-only

SegFormer encoder fine-tuned on Imagenet-1k. It was introduced in the paper [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://arxiv.org/abs/2105.15203) by Xie et al. and first released in [this repository](https://github.com/NVlabs/SegFormer). 

Disclaimer: The team releasing SegFormer did not write a model card for this model so this model card has been written by the Hugging Face team.

## Model description

SegFormer consists of a hierarchical Transformer encoder and a lightweight all-MLP decode head to achieve great results on semantic segmentation benchmarks such as ADE20K and Cityscapes. The hierarchical Transformer is first pre-trained on ImageNet-1k, after which a decode head is added and fine-tuned altogether on a downstream dataset.

This repository only contains the pre-trained hierarchical Transformer, hence it can be used for fine-tuning purposes.

## Intended uses & limitations

You can use the model for fine-tuning of semantic segmentation. See the [model hub](https://huggingface.co/models?other=segformer) to look for fine-tuned versions on a task that interests you.

### How to use

Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:

```python
from transformers import SegformerImageProcessor, SegformerForImageClassification
from PIL import Image
import requests

url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)

image_processor = SegformerImageProcessor.from_pretrained("nvidia/mit-b0")
model = SegformerForImageClassification.from_pretrained("nvidia/mit-b0")

inputs = image_processor(images=image, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
# model predicts one of the 1000 ImageNet classes
predicted_class_idx = logits.argmax(-1).item()
print("Predicted class:", model.config.id2label[predicted_class_idx])
```

For more code examples, we refer to the [documentation](https://huggingface.co/transformers/model_doc/segformer.html#).

### License

The license for this model can be found [here](https://github.com/NVlabs/SegFormer/blob/master/LICENSE).

### BibTeX entry and citation info

```bibtex
@article{DBLP:journals/corr/abs-2105-15203,
  author    = {Enze Xie and
               Wenhai Wang and
               Zhiding Yu and
               Anima Anandkumar and
               Jose M. Alvarez and
               Ping Luo},
  title     = {SegFormer: Simple and Efficient Design for Semantic Segmentation with
               Transformers},
  journal   = {CoRR},
  volume    = {abs/2105.15203},
  year      = {2021},
  url       = {https://arxiv.org/abs/2105.15203},
  eprinttype = {arXiv},
  eprint    = {2105.15203},
  timestamp = {Wed, 02 Jun 2021 11:46:42 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2105-15203.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}
```