segformer b5 finetuned ade 640 640

Introduction

The SegFormer B5 model fine-tuned on the ADE20K dataset is designed for semantic segmentation tasks. It was introduced in the paper "SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers" by Xie et al. This model is built using a hierarchical Transformer encoder alongside a lightweight MLP decode head, achieving strong results on benchmarks like ADE20K and Cityscapes.

Architecture

The architecture of SegFormer comprises a hierarchical Transformer encoder that is initially pre-trained on ImageNet-1k. It incorporates a lightweight all-MLP decode head added during the fine-tuning stage on a downstream dataset. This design enables efficient processing and high accuracy in semantic segmentation tasks.

Training

The SegFormer model was pre-trained on ImageNet-1k and fine-tuned on the ADE20K dataset. The hierarchical encoder-decoder structure allows the model to perform effectively across various semantic segmentation benchmarks.

Guide: Running Locally

To use the SegFormer model locally for semantic segmentation, follow these steps:

1. Install Dependencies: Ensure you have Python and the transformers library installed.

   pip install transformers
   

2. Load Model and Feature Extractor:

   from transformers import SegformerFeatureExtractor, SegformerForSemanticSegmentation
   feature_extractor = SegformerFeatureExtractor.from_pretrained("nvidia/segformer-b5-finetuned-ade-512-512")
   model = SegformerForSemanticSegmentation.from_pretrained("nvidia/segformer-b5-finetuned-ade-512-512")
   

3. Prepare Input Image:

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

4. Perform Inference:

   inputs = feature_extractor(images=image, return_tensors="pt")
   outputs = model(**inputs)
   logits = outputs.logits
   

5. Recommended Hardware: For better performance, especially with large models like SegFormer B5, consider using cloud GPUs from providers like AWS, Google Cloud, or Azure.

License

The SegFormer model is released under a specific license, which can be accessed here. Users are advised to review the licensing terms before use.