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README.md

@@ -1,13 +1,49 @@
 ---
-title: Manga Colorizer
-emoji: 📈
+title: MangaColorizer
+emoji: 🖌️🎨
 colorFrom: green
 colorTo: gray
 sdk: gradio
-sdk_version: 4.24.0
-app_file: app.py
+app_file: main.py
 pinned: false
 license: mit
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# MangaColorizer
+This project is a colorizer of grayscale images, and in particular for manga, comics or drawings.  
+Given a black and white (grayscale) image, the model produces a colorized version of it.
+
+[Link to the Demo](https://huggingface.co/spaces/zaidmehdi/manga-colorizer)
+
+![Demo App](docs/images/demo_screenshot.png "Demo App")
+
+
+## How I built this project:
+The data I used to train this model contains 755 colored images from some chapters of **Bleach, Dragon Ball Super, Naruto, One Piece and Attack on Titan**. I also used 215 other images for the validation set, as well as 109 other images for the test set.  
+  
+In the current version, I trained an encoder-decoder model from scratch with the following architecture:
+```
+MangaColorizer(
+  (encoder): Sequential(
+    (0): Conv2d(1, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+    (1): ReLU(inplace=True)
+    (2): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
+    (3): ReLU(inplace=True)
+    (4): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
+    (5): ReLU(inplace=True)
+  )
+  (decoder): Sequential(
+    (0): ConvTranspose2d(256, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+    (1): ReLU(inplace=True)
+    (2): ConvTranspose2d(128, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+    (3): ReLU(inplace=True)
+    (4): ConvTranspose2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+    (5): Tanh()
+  )
+)
+```
+The inputs to the model are the grayscale version of the manga images, and the target is the colored version of the image.  
+The loss function used is the MSE of all the pixel values produced by the model (compared to the target pixel values).  
+**Currently, it achieves an MSE of 0.00859 on the test set.**  
+
+For more details, you can refer to the docs directory.