77 lines
1.6 KiB
Markdown
77 lines
1.6 KiB
Markdown
# projekt_widzenie
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## Run apllication
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1. `pip install -r requirements.txt`
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2. `streamlit run main.py`
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3. On http://localhost:8501/ you should see the app
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## Dataset
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Mamy łącznie 197784 zdjęć
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+ swój własno zrobiony zbiór testowy 148 zdjęć
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Linki do datasetów:
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1. https://www.kaggle.com/datasets/mrgeislinger/asl-rgb-depth-fingerspelling-spelling-it-out
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2. https://www.kaggle.com/datasets/grassknoted/asl-alphabet
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3. https://www.kaggle.com/datasets/lexset/synthetic-asl-alphabet
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4. https://www.kaggle.com/datasets/kuzivakwashe/significant-asl-sign-language-alphabet-dataset
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## Trening modelu
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Do trenowania używano biblioteki Keras
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### Pierwsze podejście model trenowany od zera (from scratch)
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```
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img_height=256
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img_width=256
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batch_size=128
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epochs=30
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```
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```
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layers.Rescaling(1./255, input_shape=(img_height, img_width, 3)),
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layers.Conv2D(16, 3, padding='same', activation='relu'),
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layers.MaxPooling2D(),
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layers.Conv2D(32, 3, padding='same', activation='relu'),
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layers.MaxPooling2D(),
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layers.Conv2D(64, 3, padding='same', activation='relu'),
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layers.MaxPooling2D(),
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layers.Flatten(),
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layers.Dense(128, activation='relu'),
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layers.Dense(29,activation='softmax')
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```
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Zbiór testowy własny: 22% Accuracy
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Zbiór testowy mieszany z Kaggle: 80% Accuracy
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---
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## Drugie podejście model VGG16
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Zastosowano early stopping z val_loss
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```
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img_height=224
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img_width=224
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batch_size=128
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epochs=50
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```
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Usunięto 3 wierzchne wartswy i dodano warstwy:
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```
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x = layers.Flatten()(vgg_model.output)
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x = layers.Dense(len(class_names), activation='softmax')(x)
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```
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Zbiór testowy własny: 52% Accuracy
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Zbiór testowy mieszany z Kaggle: 79% Accuracy
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