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README.md

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+
+---
+language: en
+tags:
+- image-processing
+- image-denoising
+- deep-learning
+license: apache-2.0
+model_name: Image Denoising Model
+---
+
+# Autoencoder Model 
+
+This Repo contains the weights and config of Autoencoder Model that can be used for image denoising.
+
+# Datasets
+
+- LOL Dataset
+- Smartphone Image Denoising Dataset
+
+# References
+https://keras.io/examples/vision/autoencoder/
+

config.json

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+{"architecture": "Autoencoder", "input_channels": 3, "output_channels": 3, "layers": [{"type": "Conv2d", "in_channels": 3, "out_channels": 32, "kernel_size": [3, 3], "padding": "same"}, {"type": "ReLU", "inplace": true}, {"type": "MaxPool2d", "kernel_size": [2, 2], "padding": 0}, {"type": "Conv2d", "in_channels": 32, "out_channels": 64, "kernel_size": [3, 3], "padding": "same"}, {"type": "ReLU", "inplace": true}, {"type": "MaxPool2d", "kernel_size": [2, 2], "padding": 0}, {"type": "Conv2d", "in_channels": 64, "out_channels": 128, "kernel_size": [3, 3], "padding": "same"}, {"type": "ReLU", "inplace": true}, {"type": "MaxPool2d", "kernel_size": [2, 2], "padding": 0}, {"type": "ConvTranspose2d", "in_channels": 128, "out_channels": 128, "kernel_size": [3, 3], "stride": 2, "padding": 1, "output_padding": 1}, {"type": "ReLU", "inplace": true}, {"type": "ConvTranspose2d", "in_channels": 128, "out_channels": 64, "kernel_size": [3, 3], "stride": 2, "padding": 1, "output_padding": 1}, {"type": "ReLU", "inplace": true}, {"type": "ConvTranspose2d", "in_channels": 64, "out_channels": 32, "kernel_size": [3, 3], "stride": 2, "padding": 1, "output_padding": 1}, {"type": "ReLU", "inplace": true}, {"type": "Conv2d", "in_channels": 32, "out_channels": 3, "kernel_size": [3, 3], "padding": "same"}, {"type": "Sigmoid"}]}

pytorch_model.bin

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