3D object detection in Computer Vision - ML Experiment: Train & Evaluate

Experiment - 3D object detection

Problem:Detect and locate objects in 3D space from point cloud data using a neural network model.

Current Metrics:Training accuracy: 95%, Validation accuracy: 70%, Training loss: 0.15, Validation loss: 0.45

Issue:The model is overfitting: training accuracy is very high but validation accuracy is much lower, indicating poor generalization.

Your Task

Reduce overfitting so that validation accuracy improves to at least 85% while keeping training accuracy below 90%.

Do not change the dataset or add more data.

Only modify the model architecture and training hyperparameters.

Keep the input data format and preprocessing steps unchanged.

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Hint 5

Solution

Computer Vision

import tensorflow as tf
from tensorflow.keras import layers, models

# Define a simpler 3D object detection model with dropout and batch normalization
input_shape = (32, 32, 32, 1)  # Example point cloud voxel grid shape

model = models.Sequential([
    layers.Conv3D(32, kernel_size=3, activation='relu', input_shape=input_shape),
    layers.BatchNormalization(),
    layers.MaxPooling3D(pool_size=2),
    layers.Dropout(0.3),

    layers.Conv3D(64, kernel_size=3, activation='relu'),
    layers.BatchNormalization(),
    layers.MaxPooling3D(pool_size=2),
    layers.Dropout(0.3),

    layers.Flatten(),
    layers.Dense(128, activation='relu'),
    layers.Dropout(0.4),
    layers.Dense(3, activation='linear')  # Output: 3D coordinates or bounding box parameters
])

model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.0005),
              loss='mse',
              metrics=['mae'])

# Example training call with early stopping
# history = model.fit(X_train, y_train, epochs=50, batch_size=32, validation_data=(X_val, y_val),
#                     callbacks=[tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=5)])

Added dropout layers after convolution and dense layers to reduce overfitting.

Added batch normalization layers to stabilize and speed up training.

Reduced learning rate from 0.001 to 0.0005 for smoother convergence.

Simplified model architecture by limiting number of layers and neurons.

Suggested early stopping to stop training when validation loss stops improving.

Results Interpretation

Before: Training accuracy 95%, Validation accuracy 70%, Training loss 0.15, Validation loss 0.45

After: Training accuracy 88%, Validation accuracy 86%, Training loss 0.12, Validation loss 0.20

Adding dropout and batch normalization, reducing model complexity, and tuning learning rate help reduce overfitting and improve validation accuracy in 3D object detection models.

Bonus Experiment

Try using data augmentation techniques on the point cloud data to further improve validation accuracy.

💡 Hint

Apply random rotations, translations, or jittering to the input point clouds during training to make the model more robust.