
# SegFormer (b0-sized) model fine-tuned on sidewalk-semantic dataset

SegFormer model fine-tuned on segments/sidewalk-semantic at resolution 512x512. It was introduced in the paper [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://arxiv.org/abs/2105.15203) by Xie et al. and first released in [this repository](https://github.com/NVlabs/SegFormer). 

## 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](https://huggingface.co/models?other=segformer) to look for fine-tuned versions on a task that interests you.

### How to use

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

```python
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](https://huggingface.co/transformers/model_doc/segformer.html#).
{"license": "apache-2.0", "tags": ["vision", "image-segmentation"], "datasets": ["segments/sidewalk-semantic"]}
transformers pytorch safetensors segformer vision image-segmentation dataset:segments/sidewalk-semantic arxiv:2105.15203 license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #safetensors #segformer #vision #image-segmentation #dataset-segments/sidewalk-semantic #arxiv-2105.15203 #license-apache-2.0 #endpoints_compatible #region-us 


# SegFormer (b0-sized) model fine-tuned on sidewalk-semantic dataset

SegFormer model fine-tuned on segments/sidewalk-semantic at resolution 512x512. 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.

## 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.

### How to use

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



For more code examples, we refer to the documentation.
# SegFormer (b0-sized) model fine-tuned on sidewalk-semantic dataset

SegFormer model fine-tuned on segments/sidewalk-semantic at resolution 512x512. 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. ## 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. ### How to use

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



For more code examples, we refer to the documentation.
TAGS
#transformers #pytorch #safetensors #segformer #vision #image-segmentation #dataset-segments/sidewalk-semantic #arxiv-2105.15203 #license-apache-2.0 #endpoints_compatible #region-us 
 # SegFormer (b0-sized) model fine-tuned on sidewalk-semantic dataset

SegFormer model fine-tuned on segments/sidewalk-semantic at resolution 512x512. 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. ## 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. ### How to use

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



For more code examples, we refer to the documentation.
passage: TAGS
#transformers #pytorch #safetensors #segformer #vision #image-segmentation #dataset-segments/sidewalk-semantic #arxiv-2105.15203 #license-apache-2.0 #endpoints_compatible #region-us 
# SegFormer (b0-sized) model fine-tuned on sidewalk-semantic dataset

SegFormer model fine-tuned on segments/sidewalk-semantic at resolution 512x512. 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.## 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.### How to use

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



For more code examples, we refer to the documentation.