1. Sketch Object Prediction
Luciano Ricotta

2. Executive Summary Project Intentions Tasks Performed:
Train a CNN for sketch object identification and optimize its performance
Tasks Performed:
Data Preparation
Generalize Implementation
Optimize performance
Assessment of Project
More computing power is needed to use all 345 categories
Successful in generalizing the code and optimizing performance

3. Approaches: Program
Keisuke Irie’s implementation of a CNN and ensemble learning had only 4 image classifications
Step 1: Debugging the code
Step 2: Preparing the rest of the data
Step 3: Generalizing the code for N categories
Step 4: Optimizing the code by varying:
Number of categories
Sample size
Epochs
Convolution layers
Batch size
Neurons
CNN architecture

4. Approaches: Platform Personal computer for debugging the code
Euler for data preparation
Used python’s multiprocessing utility to run program in parallel.
Allocated 20 CPUs for a significant speed up
Euler for training the CNN
Data files (not raw) totaled to 382 GB, and each object file was between 0.5 to 1 GB
A lot of RAM was required (more than the 12 GB in my laptop)

5. Results: Image Prediction
Number of categories range between 2 and 20
Varying neurons, batch size, and convolution layers does not have much of an effect, although an increase in neurons has a slight accuracy boost.
The accuracy generally likes to stay between 80 and 90 for high number of categories and between 90 and 100 for low number of categories.
Number of categories 2 4 6 8 12 16 20
Average Accuracy

6. Results: Country Prediction
Number of categories range between 4 and 16
Varying neurons, batch size, and convolution layers does not have much of an effect, although an increase in neurons has a slight performance boost.
The accuracy is not significantly affected by any of these factors.
Number of categories 4 8 12 16 20
Average Accuracy

7. Discussion: Moving forward
More computing power could let us:
Include the other 300+ categories - which could result in poor accuracy, but…
This would push us to implement the a common CNN architecture such as the AlexNet architecture to compensate for the increased number of categories.