Improvising the KN 1.0 Model

[kinderasteroid]

Hello @varshavshetty. Now since we have the KN1.0 Model with us, We need to See to the Areas where we can improvise our model. Hence You Can Check These Below Ideas to do so

1. Increase Model Complexity: Add more convolutional layers, increase the number of filters, or add more dense layers. This can help the model learn more complex patterns in the data.
2. Data Augmentation: Generate more training data by applying random transformations like rotation, flipping, and scaling to the existing images. This can help the model generalize better.
3. Hyperparameter Tuning: Experiment with different hyperparameters such as learning rate, batch size, and optimizer. Use techniques like grid search or random search to find the best combination.
4. Regularization: Add dropout layers or L2 regularization to reduce overfitting and improve generalization.
5. Training with more Datasets: This is more apt way i.e to Train the Existing model with More Datasets and Features. We can Reffer to Online Websites (Ex:kaggle) for more dataset existance

Below is the Refference Improvement Code From chatgpt which you can reffer, Go through the Suggested methods and attached code . You can experiment to check if model is doing good by the Above recomended Suggestions. Reach me Back with ideas you decide to follow or directly with modified code is also acceptable

model = models.Sequential([
    layers.Conv2D(32, (3, 3), activation='relu', input_shape=(100, 100, 3)),
    layers.MaxPooling2D((2, 2)),
    layers.Conv2D(64, (3, 3), activation='relu'),
    layers.MaxPooling2D((2, 2)),
    layers.Conv2D(128, (3, 3), activation='relu'),
    layers.MaxPooling2D((2, 2)),
    layers.Flatten(),
    layers.Dropout(0.5),  # Dropout layer for regularization
    layers.Dense(128, activation='relu'),
    layers.Dense(1, activation='sigmoid')
])

# Compile the model
model.compile(optimizer='adam',
              loss='binary_crossentropy',
              metrics=['accuracy'])

# Data augmentation
datagen = tf.keras.preprocessing.image.ImageDataGenerator(
    rotation_range=20,
    width_shift_range=0.2,
    height_shift_range=0.2,
    shear_range=0.2,
    zoom_range=0.2,
    horizontal_flip=True,
    fill_mode='nearest'
)

# Train the model with augmented data
model.fit(datagen.flow(images_arr, labels_arr, batch_size=32), epochs=20)

# Evaluate the model
model.evaluate(images_arr, labels_arr)

# Use the trained model for real-time detection
# (code for real-time detection remains the same)

[kinderasteroid]

@varshavshetty Run the following code before working on the Code and make PR to this branch itself

git fetch origin

git checkout kn1.0-staging

[varshavshetty]

@varshavshetty Run the following code before working on the Code and make PR to this branch itself

git fetch origin

git checkout kn1.0-staging

So I should modify the code and then run these two commands?

[kinderasteroid]

@varshavshetty Just run that code in your directory where previous code was put. and it will change the branch automatically

[varshavshetty]

@kinderasteroid Ok after that I should modify the code?I mean python code.

[varshavshetty]

@kinderasteroid Later I should commit changes and push it to same branch right?

[kinderasteroid]

@varshavshetty After that, Decision is upto you. I have suggested the ideas how you can improvise the model. you can research on it and you can experiment on your code to check if there will be any improvements and all

Because your First code is in #2 right now so if any errors occur. we have that code in main branch which we can pull and restore it back. you will be working on newly created branch even in local system right. Hence yeah you can modify

[varshavshetty]

@kinderasteroid okay

[varshavshetty]

@kinderasteroid I checked the code but that data agumentation doesn't give good accuracy...so i removed that and did some changes ...is it okay?

[kinderasteroid]

@varshavshetty Yes , Whatever Improvises the model. You can do it. Even try Increasing epochs

[varshavshetty]

@kinderasteroid I added your code for defining CNN model but there i changed first layer code....

[varshavshetty]

@kinderasteroid epochs i had increased to 20

[kinderasteroid]

Is there improvement?

[varshavshetty]

@kinderasteroid I told right accuracy has improved......both changes i did at same time

[kinderasteroid]

Cool, Make a PR. I will test it

[varshavshetty]

@kinderasteroid I did PR

[kinderasteroid]

Once the android app's finalized Please work on This Part and Make your Best to bring out best of possible.

FYI @varshavshetty