diff --git a/project/README.md b/project/README.md index 522d2e921a1e331365a95cc6e20fa8ab1b8e61c5..73d0590d31616bc928df8b53547d51a8a0f79a05 100644 --- a/project/README.md +++ b/project/README.md @@ -10,11 +10,11 @@ This folder contains the work for our final project of the EML Proseminar. The p The motivation for this project is to explore the capabilities of machine learning models for image classification tasks. The inspiration for this project comes from the idea of automatic fruit recognition, which has **practical applications** for instance in supermarkets. Currently, customers have to manually select the type of fruit they are purchasing at self-service scales. This process is time-consuming and also inconvenient. Automating this process not only enhances efficiency but also provides a better customer experience, particularly in busy shopping environments. -Moreover, the scalability of this approach extends beyond just fruits; it could be adapted for other grocery items like bread or vegetables, as well as various industries requiring object recognition, such as material sorting in recycling plants or inventory management in warehouses. +Moreover, the scalability of this approach extends beyond just fruits; it could be adapted for other grocery items like bread or vegetables, as well as for various industries requiring object recognition, such as material sorting in recycling plants or inventory management in warehouses. ## Related Work -In our project, we looked related work on (fruit) image classification to gain insights into the best practices and build upon the existing knowledge in the field. We found two papers that were particularly relevant to our project: +In our project, we looked at related work on (fruit) image classification to gain insights into the best practices and build upon the existing knowledge in the field. We found two papers that were particularly relevant to our project: - the bachelor's thesis that created the dataset we use, which is essential for understanding its structure and development, and - a study on fruit recognition using deep learning, providing advanced insights into neural network applications in fruit classification. @@ -131,8 +131,8 @@ By concatenating the color values of one pixel and then concatenating the pixels â†ªï¸ See how the pixel data is extracted from the sample image in [`extract_features.py`](minimal_examples/extract_features.py) *What does the pixel data tell us about the image?* -- the pixel in the top left corner has the RGB values (245, 204, 99) (:yellow_circle:) -- the pixel in the bottom right corner has the RGB values (50, 41, 32) (= brown/black :brown_circle:) +- the pixel in the top left corner has the RGB values (245, 204, 99) (🟡) +- the pixel in the bottom right corner has the RGB values (50, 41, 32) (= brown/black 🟤) ### Resizing Images @@ -355,7 +355,7 @@ The following table shows an excerpt of the feature and size combination used. - for some classes, the diagonal in the confusion matrix below is quite bright (e.g. apricots and passion fruits) :arrow_right: the classifier is quite good at predicting these classes - but we also see that the classifier has a **strong bias** towards some classes (e.g. apricots, jostaberries and passion fruits and figs) -<img align="center" src="figures/naive_bayes/GaussianNB_50x50_hsv_sobel_confusion_matrix_var_smoothing_4.281332398719396e-08.png" alt= "Confusion Matrix" width="80%" height="auto"> +<img align="center" src="figures/naive_bayes/GaussianNB_50x50_hsv_sobel_confusion_matrix_var_smoothing_4.281332398719396e-08.png" alt= "Confusion Matrix" width="70%" height="auto"> </figure> @@ -530,8 +530,7 @@ When using the RGB or HSV values as features, we have three features for each pi As can be seen in the following plot, the **pixels in the middle** have higher values and are thus more important for the classification than the pixels near the edges. The same pattern is found for all Decision Tree and Random Forest models that we have trained. This meets our expectations, as the middle of the image is **where the fruit is typically located** and the edges are often just the background. -<img src="figures/random_forest/RandomForestClassifier_50x50_hsv_feature_importances_max_depth_70_max_features_sqrt_min_samples_leaf_2_min_samples_split_2_n_estimators_100.png" alt= "Random Forest Feature Importance" width="500" height="auto"> - +<img align="center" src="figures/random_forest/RandomForestClassifier_50x50_hsv_feature_importances_max_depth_70_max_features_sqrt_min_samples_leaf_2_min_samples_split_2_n_estimators_100.png" alt= "Random Forest Feature Importance" width="500" height="auto"> ### Data Reduction