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GauGAN-Image-generation
keras-io/GauGAN-Image-generation
GauGAN-Image-generation is a machine learning model from keras-io. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for tf-keras.
In this, GauGAN architecture has been implemented for conditional image generation which was proposed in Semantic Image Synthesis with Spatially-Adaptive Normalization.
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At a glance
- Library
- tf-keras
- Access
- Public
- Created
- Jun 10, 2022
- Updated
- Jul 5, 2024
- SHA
- 7a1e67f1
Model card
From the Hugging Face model README
Model description
In this, GauGAN architecture has been implemented for conditional image generation which was proposed in Semantic Image Synthesis with Spatially-Adaptive Normalization.
GauGAN uses a Generative Adversarial Network (GAN) to generate realistic images that are conditioned on cue images and segmentation maps.
This repo contains the model for the notebook GauGAN for conditional image generation
Full credits go to Soumik Rakshit & Sayak Paul
Training and evaluation data
Here, the Facades dataset is used for training GauGAN model. Some custom layers that were added into the model are - SPADE (SPatially-Adaptive (DE) normalization), Residual block including SPADE & Gaussian sampler. Also, the GauGAN encoder consists of a few downsampling blocks. It outputs the mean and variance of a distribution as shown in this image.
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
| name | learning_rate | decay | rho | momentum | epsilon | centered | training_precision |
|---|---|---|---|---|---|---|---|
| RMSprop | 0.0010000000474974513 | 0.0 | 0.8999999761581421 | 0.0 | 1e-07 | False | float32 |
Model Plot
<details> <summary>View Model Plot</summary>
- Library
- tf-keras
- Created
- Jun 10, 2022
- Updated
- Jul 5, 2024