segformer b2 finetuned ade 512 512

Introduction

The SegFormer B2 model, fine-tuned on the ADE20k dataset, is designed for semantic segmentation tasks. The model was introduced in the paper "SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers" by Xie et al. It employs a hierarchical Transformer encoder along with a lightweight all-MLP decode head to deliver high performance on benchmarks like ADE20K and Cityscapes.

Architecture

SegFormer includes a hierarchical Transformer encoder that is initially pre-trained on ImageNet-1k. A decode head is then added and the entire setup is fine-tuned on a downstream dataset. This architecture is effective for tasks requiring semantic segmentation, leveraging the strength of Transformers in capturing hierarchical representations.

Training

The SegFormer model is first pre-trained on ImageNet-1k, focusing on diverse image content, before being fine-tuned on ADE20k. This process allows the model to effectively learn semantic segmentation capabilities, adapting the pre-trained knowledge to specific tasks.

Guide: Running Locally

To run the SegFormer model locally, follow these basic steps:

1. Install the Transformers Library:
   Ensure you have the transformers library installed.

   pip install transformers
   

2. Load the Model and Feature Extractor:
   Use the following Python code to load the model and feature extractor.

   from transformers import SegformerFeatureExtractor, SegformerForSemanticSegmentation
   from PIL import Image
   import requests
   
   feature_extractor = SegformerFeatureExtractor.from_pretrained("nvidia/segformer-b2-finetuned-ade-512-512")
   model = SegformerForSemanticSegmentation.from_pretrained("nvidia/segformer-b2-finetuned-ade-512-512")
   

3. Process an Image:
   Download and process an image for segmentation.

   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
   

4. Suggested Cloud GPUs:
   For enhanced performance and faster processing, consider using cloud GPU services like AWS EC2 with GPU instances, Google Cloud Platform, or Azure.

License

The SegFormer model is released under an unspecified license categorized as "other." Users should review the license details directly from the repository or the Hugging Face model card page.