projekt_widzenie

Run apllication

1. pip install -r requirements.txt
2. streamlit run main.py
3. On http://localhost:8501/ you should see the app

Dataset

Mamy łącznie 197784 zdjęć

Linki do datasetów:

1. https://www.kaggle.com/datasets/mrgeislinger/asl-rgb-depth-fingerspelling-spelling-it-out
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

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Drugie podejście model VGG16

img_height=224
img_width=224
batch_size=128
epochs=10

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