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bvaibhav83/
segformer-b0-finetuned-segments-sidewalk-3

Name: segformer-b0-finetuned-segments-sidewalk-3
Availability: InStock

segments/sidewalk-semantic

2105.15203

endpoints_compatible

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SegFormer (b0-sized) model fine-tuned on Segments.ai sidewalk-semantic.

SegFormer model fine-tuned on Segments.ai sidewalk-semantic. It was introduced in the paper SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers by Xie et al. and first released in this repository.

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.

How to use

Here is how to use this model to classify an image of the sidewalk dataset:

python

from transformers import SegformerFeatureExtractor, SegformerForSemanticSegmentation
from PIL import Image
import requests
feature_extractor = SegformerFeatureExtractor.from_pretrained("nvidia/segformer-b0-finetuned-ade-512-512")
model = SegformerForSemanticSegmentation.from_pretrained("segments-tobias/segformer-b0-finetuned-segments-sidewalk")
url = "https://segmentsai-prod.s3.eu-west-2.amazonaws.com/assets/admin-tobias/439f6843-80c5-47ce-9b17-0b2a1d54dbeb.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)

bvaibhav83/segformer-b0-finetuned-segments-sidewalk-3

SegFormer (b0-sized) model fine-tuned on Segments.ai sidewalk-semantic.

Model description

How to use

bvaibhav83/
segformer-b0-finetuned-segments-sidewalk-3