Segformer B1 Sidewalk

🚀 SegFormer (b1-sized) model fine-tuned on sidewalk-semantic dataset

A SegFormer model fine-tuned on the segments/sidewalk-semantic dataset at a resolution of 512x512, offering high - performance semantic segmentation.

🚀 Quick Start

The SegFormer model presented here is fine - tuned on the segments/sidewalk - semantic dataset at a resolution of 512x512. It was first introduced in the paper SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers by Xie et al. and initially released in this repository.

✨ Features

• Hierarchical Architecture: SegFormer is composed of a hierarchical Transformer encoder and a lightweight all - MLP decode head, which enables it to achieve excellent results on semantic segmentation benchmarks like ADE20K and Cityscapes.
• Pre - training and Fine - tuning: The hierarchical Transformer is pre - trained on ImageNet - 1k, and then a decode head is added and fine - tuned on a downstream dataset.

💻 Usage Examples

Basic Usage

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

from transformers import SegformerFeatureExtractor, SegformerForSemanticSegmentation
from PIL import Image
import requests

feature_extractor = SegformerFeatureExtractor(reduce_labels=True)
model = SegformerForSemanticSegmentation.from_pretrained("ChainYo/segformer-sidewalk")

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

inputs = feature_extractor(images=image, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits  # shape (batch_size, num_labels, height/4, width/4)

For more code examples, we refer to the documentation.

📚 Documentation

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.

Intended uses & limitations

You can use the raw model for semantic segmentation. See the model hub to look for fine - tuned versions on a task that interests you.

📄 License

This model is licensed under the Apache 2.0 license.