semantic segmentation deep learning python
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Semantic segmentation is a computer vision task that involves classifying each pixel in an image into a specific class. In this tutorial, we will explore how to perform semantic segmentation using deep learning in Python. We will use a popular deep learning library called TensorFlow along with the Keras API.
Before starting the tutorial, make sure you have the following installed:
You can install the required libraries using the following command:
For this tutorial, we will use the ADE20K dataset, which is a large-scale scene parsing dataset with 150 object categories. You can download it from the official website: ADE20K Dataset.
After downloading, extract the dataset and organize it into the following structure:
We will use a pre-trained DeepLabV3 model for semantic segmentation. DeepLabV3 is a state-of-the-art deep learning model for semantic image segmentation.
Before feeding an image to the model, we need to preprocess it. This involves resizing the image to the model's input size and normalizing the pixel values.
Now, we can use the pre-trained model to perform semantic segmentation on the input image.
The output segmentation mask is a probability map for each class. We need to post-process it to get the final segmented image.
In this tutorial, we explored how to perform semantic segmentation using a pre-trained DeepLabV3 model in Python. We covered loading the model, preprocessing input images, performing segmentation, and post-processing the segmentation mask. Feel free to experiment with different images and fine-tune the threshold for post-processing to achieve optimal results.
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