segformer b0 finetuned ade 512 512

Introduction

The SegFormer-B0 model fine-tuned on the ADE20K dataset is designed for semantic segmentation tasks. Introduced by Xie et al., this model is part of the SegFormer architecture, which combines a hierarchical Transformer encoder with a lightweight MLP decode head to deliver efficient segmentation results.

Architecture

SegFormer features a hierarchical Transformer encoder pre-trained on ImageNet-1k. The model's architecture includes a decode head that is fine-tuned with the encoder on specific datasets for semantic segmentation. This design supports advanced performance on benchmarks such as ADE20K and Cityscapes.

Training

The SegFormer architecture begins with pre-training the Transformer encoder on ImageNet-1k. Once pre-trained, a decode head is integrated, after which the entire model is fine-tuned on downstream datasets for specific segmentation tasks.

Guide: Running Locally

1. Installation: Ensure you have transformers and torch libraries installed. You can install them using pip:

   pip install transformers torch
   

2. Code Example:

   from transformers import SegformerImageProcessor, SegformerForSemanticSegmentation
   from PIL import Image
   import requests
   
   processor = SegformerImageProcessor.from_pretrained("nvidia/segformer-b0-finetuned-ade-512-512")
   model = SegformerForSemanticSegmentation.from_pretrained("nvidia/segformer-b0-finetuned-ade-512-512")
   
   url = "http://images.cocodataset.org/val2017/000000039769.jpg"
   image = Image.open(requests.get(url, stream=True).raw)
   
   inputs = processor(images=image, return_tensors="pt")
   outputs = model(**inputs)
   logits = outputs.logits  # shape (batch_size, num_labels, height/4, width/4)
   

3. Cloud GPUs: For efficient execution, consider using cloud GPU services such as AWS EC2, Google Cloud Platform, or Azure, which offer high-performance computing resources.

License

The SegFormer model's license details can be found here. The model is released under a license provided by its authors and the NVlabs repository.