Merge branch 'master' of git.wmi.amu.edu.pl:s444463/projekt_widzenie

README.md

@@ -16,3 +16,59 @@ Linki do datasetów:
 2. https://www.kaggle.com/datasets/grassknoted/asl-alphabet
 3. https://www.kaggle.com/datasets/lexset/synthetic-asl-alphabet
 4. https://www.kaggle.com/datasets/kuzivakwashe/significant-asl-sign-language-alphabet-dataset
+
+
+## Trening modelu
+
+Do trenowania używano biblioteki Keras
+
+### Pierwsze podejście model trenowany od zera (from scratch)
+
+
+```
+img_height=256
+img_width=256
+batch_size=128
+epochs=30
+```
+
+```
+  layers.Rescaling(1./255, input_shape=(img_height, img_width, 3)),
+  layers.Conv2D(16, 3, padding='same', activation='relu'),
+  layers.MaxPooling2D(),
+  layers.Conv2D(32, 3, padding='same', activation='relu'),
+  layers.MaxPooling2D(),
+  layers.Conv2D(64, 3, padding='same', activation='relu'),
+  layers.MaxPooling2D(),
+  layers.Flatten(),
+  layers.Dense(128, activation='relu'),
+  layers.Dense(29,activation='softmax')
+```
+
+Zbiór testowy własny: 22% Accuracy
+
+Zbiór testowy mieszany z Kaggle: 80% Accuracy
+ 
+---
+
+## Drugie podejście model VGG16
+
+Zastosowano early stopping z val_loss
+```
+img_height=224
+img_width=224
+batch_size=128
+epochs=50
+```
+
+Usunięto 3 wierzchne wartswy i dodano warstwy:
+```
+x = layers.Flatten()(vgg_model.output)
+x = layers.Dense(len(class_names), activation='softmax')(x)
+```
+
+Zbiór testowy własny: 40% Accuracy
+
+Zbiór testowy mieszany z Kaggle: ???% Accuracy
+
+