ESRGAN implementation from scratch
Updated: November 19, 2024
Summary
The video introduces the implementation of ESR GAN from scratch, focusing on the model architecture. It compares SR Resnet architectures to the new Residual Block used in ESR GAN and explains key differences. Detailed steps are provided for creating components like Com Block, Upsample Block, Dense Residual Block, and RRDB Block, forming the Generator. No training is covered in the video, but references to pre-trained weights availability are made. The walkthrough emphasizes understanding the model architecture rather than delving into the workings of ESR GAN itself.
Introduction and Recommendation
The speaker introduces the topic of implementing ESR GAN from scratch and recommends checking out both SR GAN and ESR GAN papers for a better understanding. They highlight that in this video, the focus will be on implementing the model architecture without explaining how ESR GAN works in detail.
Model Architecture Overview
A walkthrough of the SR Resnet architectures in comparison to the new Residual Block used in ESR GAN. The speaker explains the differences and highlights the main focus on the model architecture.
Creating Com Block
Detailed explanation on creating the Com Block, specifying parameters such as in channels, out channels, activation, bias, and activation function like Leaky ReLU.
Creating Upsample Block
Explanation of creating the Upsample Block with parameters like in channels, scale factor, and nearest neighbor upsampling. Mention of pre-trained weights availability but no training in this video.
Creating Dense Residual Block
Step-by-step creation of the Dense Residual Block involving initialization, setting channels, residual connection, defining module list, and loop creation for blocks.
Creating RRDB Block
Explanation of creating the RRDB Block involving initialization, sequential creation of residual blocks, and implementation of skip connections.
Putting Generator Together
Detailed steps to assemble the Generator by specifying image channels, number of blocks, initial layers, residuals, upsampling blocks, and final layers.
FAQ
Q: What is the main focus of the video when implementing ESR GAN from scratch?
A: The main focus of the video is on implementing the model architecture of ESR GAN without explaining how ESR GAN works in detail.
Q: What parameters are specified when creating the Com Block?
A: When creating the Com Block, parameters like in channels, out channels, activation, bias, and activation function (e.g., Leaky ReLU) are specified.
Q: What is the Upsample Block and what parameters are involved in its creation?
A: The Upsample Block involves parameters like in channels, scale factor, and nearest neighbor upsampling.
Q: What is mentioned regarding pre-trained weights in the video?
A: The video mentions the availability of pre-trained weights but specifies that there will be no training involved in this video.
Q: Can you explain the creation process of the Dense Residual Block as outlined in the video?
A: The creation of the Dense Residual Block involves steps such as initialization, setting channels, establishing residual connections, defining a module list, and creating blocks in a loop.
Q: What is the RRDB Block and how is it created according to the video?
A: The RRDB Block stands for Recursive Residual Dense Block and it is created through initialization, sequential creation of residual blocks, and implementing skip connections.
Q: What are the detailed steps involved in assembling the Generator in the video?
A: The steps include specifying image channels, determining the number of blocks, setting initial layers, configuring residuals, including upsampling blocks, and adding final layers.
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