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app.py
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with tabs[1]:
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st.write('## Find Order')
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st.markdown("""<p>The pretrained model has been trained with
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This layer is followed by another Conv2D layer with the same filter size, activation function, and batch normalization.
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MaxPooling2D is used to reduce the dimensionality of the data, with a pool size of (2,2). Dropout is then applied, with a rate of 0.25.
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This process is repeated with two more sets of Conv2D, BatchNormalization, MaxPooling2D and Dropout layers, increasing the filter size to 64 and 128 for each set respectively.
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Finally, the model is flattened to transform the multidimensional output into a one-dimensional array, and a Dense layer with 512 nodes is added with an activation function of ReLU.
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Batch normalization and dropout are also included in this layer. Finally, a last dense layer with a softmax activation function is added, which outputs the predicted class probabilities.</p>
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""", unsafe_allow_html=True)
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with st.sidebar:
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with tabs[1]:
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st.write('## Find Order')
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st.markdown("""<p>The pretrained model has been trained with 84,000 synthetic generated images. The goal is to detect the font type from a given list of 21 font types.
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Each image starts with a capital letter followed by 3-10 random simple letters. images are created with random horizontal reslutions and resized to 64x64 at the end.
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random amount of noise and rotation is also added.</p>
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""", unsafe_allow_html=True)
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with st.sidebar:
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model.h5
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size 16325952
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version https://git-lfs.github.com/spec/v1
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oid sha256:77f193b4deb96cc63657c376dd877bcd37f1afba40996f002857c71082744b15
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size 16325952
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