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

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Mikołaj Pokrywka 2023-01-24 18:49:58 +01:00
commit 82edb6a12e

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@ -16,3 +16,59 @@ Linki do datasetów:
2. https://www.kaggle.com/datasets/grassknoted/asl-alphabet 2. https://www.kaggle.com/datasets/grassknoted/asl-alphabet
3. https://www.kaggle.com/datasets/lexset/synthetic-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 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