
## Multiclass semantic segmentation using DeepLabV3+
This repo contains the model and the notebook [to this Keras example on Multiclass semantic segmentation using DeepLabV3+](https://keras.io/examples/vision/deeplabv3_plus/).

Full credits to: [Soumik Rakshit](http://github.com/soumik12345)

The model is trained for demonstrative purposes and does not guarantee the best results in production. For better results, follow & optimize the [Keras example]((https://keras.io/examples/vision/deeplabv3_plus/) as per your need.

## Background Information 
Semantic segmentation, with the goal to assign semantic labels to every pixel in an image, is an essential computer vision task. In this example, we implement the DeepLabV3+ model for multi-class semantic segmentation, a fully-convolutional architecture that performs well on semantic segmentation benchmarks.   

## Training Data
The model is trained on a subset (10,000 images) of [Crowd Instance-level Human Parsing Dataset](https://arxiv.org/abs/1811.12596). The Crowd Instance-level Human Parsing (CIHP) dataset has 38,280 diverse human images. Each image in CIHP is labeled with pixel-wise annotations for 20 categories, as well as instance-level identification. This dataset can be used for the "human part segmentation" task.

## Model
The model uses ResNet50 pretrained on ImageNet as the backbone model.

References:   
1. [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://arxiv.org/pdf/1802.02611.pdf)   
2. [Rethinking Atrous Convolution for Semantic Image Segmentation](https://arxiv.org/abs/1706.05587)   
3. [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://arxiv.org/abs/1606.00915)
{"license": ["cc0-1.0"], "library_name": "keras", "tags": ["computer-vision", "image-segmentation"]}
keras computer-vision image-segmentation arxiv:1811.12596 arxiv:1802.02611 arxiv:1706.05587 arxiv:1606.00915 license:cc0-1.0 has_space region:us
TAGS
#keras #computer-vision #image-segmentation #arxiv-1811.12596 #arxiv-1802.02611 #arxiv-1706.05587 #arxiv-1606.00915 #license-cc0-1.0 #has_space #region-us 


## Multiclass semantic segmentation using DeepLabV3+
This repo contains the model and the notebook to this Keras example on Multiclass semantic segmentation using DeepLabV3+.

Full credits to: Soumik Rakshit

The model is trained for demonstrative purposes and does not guarantee the best results in production. For better results, follow & optimize the Keras example as per your need.

## Background Information 
Semantic segmentation, with the goal to assign semantic labels to every pixel in an image, is an essential computer vision task. In this example, we implement the DeepLabV3+ model for multi-class semantic segmentation, a fully-convolutional architecture that performs well on semantic segmentation benchmarks.   

## Training Data
The model is trained on a subset (10,000 images) of Crowd Instance-level Human Parsing Dataset. The Crowd Instance-level Human Parsing (CIHP) dataset has 38,280 diverse human images. Each image in CIHP is labeled with pixel-wise annotations for 20 categories, as well as instance-level identification. This dataset can be used for the "human part segmentation" task.

## Model
The model uses ResNet50 pretrained on ImageNet as the backbone model.

References:   
1. Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation   
2. Rethinking Atrous Convolution for Semantic Image Segmentation   
3. DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
## Multiclass semantic segmentation using DeepLabV3+
This repo contains the model and the notebook to this Keras example on Multiclass semantic segmentation using DeepLabV3+.

Full credits to: Soumik Rakshit

The model is trained for demonstrative purposes and does not guarantee the best results in production. For better results, follow & optimize the Keras example as per your need. ## Background Information 
Semantic segmentation, with the goal to assign semantic labels to every pixel in an image, is an essential computer vision task. In this example, we implement the DeepLabV3+ model for multi-class semantic segmentation, a fully-convolutional architecture that performs well on semantic segmentation benchmarks. ## Training Data
The model is trained on a subset (10,000 images) of Crowd Instance-level Human Parsing Dataset. The Crowd Instance-level Human Parsing (CIHP) dataset has 38,280 diverse human images. Each image in CIHP is labeled with pixel-wise annotations for 20 categories, as well as instance-level identification. This dataset can be used for the "human part segmentation" task. ## Model
The model uses ResNet50 pretrained on ImageNet as the backbone model.

References:   
1. Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation   
2. Rethinking Atrous Convolution for Semantic Image Segmentation   
3. DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
TAGS
#keras #computer-vision #image-segmentation #arxiv-1811.12596 #arxiv-1802.02611 #arxiv-1706.05587 #arxiv-1606.00915 #license-cc0-1.0 #has_space #region-us 
 ## Multiclass semantic segmentation using DeepLabV3+
This repo contains the model and the notebook to this Keras example on Multiclass semantic segmentation using DeepLabV3+.

Full credits to: Soumik Rakshit

The model is trained for demonstrative purposes and does not guarantee the best results in production. For better results, follow & optimize the Keras example as per your need. ## Background Information 
Semantic segmentation, with the goal to assign semantic labels to every pixel in an image, is an essential computer vision task. In this example, we implement the DeepLabV3+ model for multi-class semantic segmentation, a fully-convolutional architecture that performs well on semantic segmentation benchmarks. ## Training Data
The model is trained on a subset (10,000 images) of Crowd Instance-level Human Parsing Dataset. The Crowd Instance-level Human Parsing (CIHP) dataset has 38,280 diverse human images. Each image in CIHP is labeled with pixel-wise annotations for 20 categories, as well as instance-level identification. This dataset can be used for the "human part segmentation" task. ## Model
The model uses ResNet50 pretrained on ImageNet as the backbone model.

References:   
1. Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation   
2. Rethinking Atrous Convolution for Semantic Image Segmentation   
3. DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
passage: TAGS
#keras #computer-vision #image-segmentation #arxiv-1811.12596 #arxiv-1802.02611 #arxiv-1706.05587 #arxiv-1606.00915 #license-cc0-1.0 #has_space #region-us 
## Multiclass semantic segmentation using DeepLabV3+
This repo contains the model and the notebook to this Keras example on Multiclass semantic segmentation using DeepLabV3+.

Full credits to: Soumik Rakshit

The model is trained for demonstrative purposes and does not guarantee the best results in production. For better results, follow & optimize the Keras example as per your need.## Background Information 
Semantic segmentation, with the goal to assign semantic labels to every pixel in an image, is an essential computer vision task. In this example, we implement the DeepLabV3+ model for multi-class semantic segmentation, a fully-convolutional architecture that performs well on semantic segmentation benchmarks.## Training Data
The model is trained on a subset (10,000 images) of Crowd Instance-level Human Parsing Dataset. The Crowd Instance-level Human Parsing (CIHP) dataset has 38,280 diverse human images. Each image in CIHP is labeled with pixel-wise annotations for 20 categories, as well as instance-level identification. This dataset can be used for the "human part segmentation" task.## Model
The model uses ResNet50 pretrained on ImageNet as the backbone model.

References:   
1. Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation   
2. Rethinking Atrous Convolution for Semantic Image Segmentation   
3. DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs