From 5b4c5ed2550683722be19ac662212be33004d2a9 Mon Sep 17 00:00:00 2001 From: igraf <igraf@cl.uni-heidelberg.de> Date: Fri, 23 Feb 2024 22:01:44 +0000 Subject: [PATCH] Update README.md --- project/README.md | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/project/README.md b/project/README.md index 73d0590..c74b108 100644 --- a/project/README.md +++ b/project/README.md @@ -52,7 +52,7 @@ In our project, we have focused on **30** specific **classes** out of the 262 av The original dataset lacks a predefined split into training, development (validation), and testing sets. To tailor our dataset for effective model training and evaluation, we implemented a custom script that methodically divides the dataset into specific proportions. <figure> -<img align="left" src="figures/dataset_split.png" alt= "Dataset Split" width="40%" height="auto"> +<img align="left" src="figures/dataset_split.png" alt= "Dataset Split" width="45%" height="auto"> </figure> @@ -111,7 +111,8 @@ The features we can use for training our models are always based on the **pixel In the dummy example below, there is a 3x3 pixel image. Each pixel has three values: intensity of red, green and blue (**RGB**). -<img align="center" width="100%" height="auto" src="figures/image_features.png" title="test"> + +<img align="right" width="20%" height="auto" src="figures/image_features.png"> By concatenating the color values of one pixel and then concatenating the pixels, we can represent the image as a 1D vector. The length of the vector is then equal to the number of pixels in the image multiplied by 3 (RGB). -- GitLab