
**Semantic segmentation** is the task of classifying each pixel in an image to a corresponding category. It has a wide range of use cases in fields such as medical imaging, autonomous driving, robotics, etc. For the facade dataset we are interested to classify the front-view of buildings based on 12 distinct classes. The classes are as follows: facade, molding, cornice, pillar, window, door, sill, blind, balcony, shop, deco, and background. 

In 2014, [Long et al.](https://arxiv.org/abs/1411.4038) published a fundamental paper that used convolutional neural networks for semantic segmentation. More recent successes in computer vision include the usage of Transformers, therefore also the usage in image classification tasks (i.e. [ViT](https://huggingface.co/blog/fine-tune-vit)). In this turn, Transformers have also been used for semantic segmentation, demonstrating excellent performance on several widely used datasets. We will specifically look at the SegFormer, which is a state-of-the-art model architecture for semantic segmentation introduced in 2021. It has a hierarchical Transformer encoder that does not rely on positional encodings and a simple multi-layer perceptron decoder.  In this case, we will use SegFormer to classify street view images of buildings.

More, recently, Yin et al. introduced a novel approach to semantic segmentation that achieves state-of-the-art performance in a zero-shot setting. This means that the model is able to achieve results equivalent to supervised methods on various semantic segmentation datasets, without being trained on these datasets. The approach involves replacing class labels with vector-valued embeddings of short paragraphs that describe the class. This allows for the merging of multiple datasets with different class labels and semantics, resulting in a merged dataset of over 2 million images. The resulting model achieves performance equal to state-of-the-art supervised methods on 7 benchmark datasets, and even outperforms methods when fine-tuned on standard semantic segmentation datasets. 

See the full tutorial [here](https://github.com/Xpitfire/SSIW).
{"language": ["en"], "license": "mit", "tags": ["building"], "datasets": ["Xpitfire/cmp_facade"], "metrics": ["accuracy"], "pipeline_tag": "image-segmentation"}
transformers pytorch segformer image-classification building image-segmentation en dataset:Xpitfire/cmp_facade arxiv:1411.4038 license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #segformer #image-classification #building #image-segmentation #en #dataset-Xpitfire/cmp_facade #arxiv-1411.4038 #license-mit #autotrain_compatible #endpoints_compatible #region-us 


Semantic segmentation is the task of classifying each pixel in an image to a corresponding category. It has a wide range of use cases in fields such as medical imaging, autonomous driving, robotics, etc. For the facade dataset we are interested to classify the front-view of buildings based on 12 distinct classes. The classes are as follows: facade, molding, cornice, pillar, window, door, sill, blind, balcony, shop, deco, and background. 

In 2014, Long et al. published a fundamental paper that used convolutional neural networks for semantic segmentation. More recent successes in computer vision include the usage of Transformers, therefore also the usage in image classification tasks (i.e. ViT). In this turn, Transformers have also been used for semantic segmentation, demonstrating excellent performance on several widely used datasets. We will specifically look at the SegFormer, which is a state-of-the-art model architecture for semantic segmentation introduced in 2021. It has a hierarchical Transformer encoder that does not rely on positional encodings and a simple multi-layer perceptron decoder.  In this case, we will use SegFormer to classify street view images of buildings.

More, recently, Yin et al. introduced a novel approach to semantic segmentation that achieves state-of-the-art performance in a zero-shot setting. This means that the model is able to achieve results equivalent to supervised methods on various semantic segmentation datasets, without being trained on these datasets. The approach involves replacing class labels with vector-valued embeddings of short paragraphs that describe the class. This allows for the merging of multiple datasets with different class labels and semantics, resulting in a merged dataset of over 2 million images. The resulting model achieves performance equal to state-of-the-art supervised methods on 7 benchmark datasets, and even outperforms methods when fine-tuned on standard semantic segmentation datasets. 

See the full tutorial here.
TAGS
#transformers #pytorch #segformer #image-classification #building #image-segmentation #en #dataset-Xpitfire/cmp_facade #arxiv-1411.4038 #license-mit #autotrain_compatible #endpoints_compatible #region-us 

TAGS
#transformers #pytorch #segformer #image-classification #building #image-segmentation #en #dataset-Xpitfire/cmp_facade #arxiv-1411.4038 #license-mit #autotrain_compatible #endpoints_compatible #region-us 
