1. DeepCount
Mark Lenson

2. Applications for Counting
Satellite images
Cars, People
Biology
Cell counting
Agriculture
Plants, animals, bugs

3. Previous Work: Counting through Density Estimation
Counts objects by estimating object density [1]
Done by optimizing coefficients W to obtain a density function value at each pixel

4. Previous Work: Counting through Density Estimation
Optimization of loss function:
Linear mapping transforms each pixel into a density value
No deep learning

5. Deep Learning
Deep learning is a type of machine learning that uses algorithms and artificial neural networks with many layers
An artificial neural network is a biologically inspired network of nodes, connected by weights, and arranged into layers
A large dataset is provided for the network to learn from, known as training examples
Example: This network can then be used to predict handwritten digits by recognizing patterns [2]

6. Alexnet Convolutional neural network with hidden layers
Used for image recognition
5 convolutional layers, 2 fully connected layers, 1 softmax output layer
Typical Alexnet Structure [3]

7. Process/Definitions
Netlogo used to generate dataset of two-thousand of images with random number of objects (shapes)
Images contained 1 to 10 objects
Ran in Jupyter Notebook, an interface for running python
TensorFlow is a library used for machine learning and training neural networks
Tflearn is a deep learning library built in Tensorflow that contains Alexnet
Tensorboard used to analyze and inspect data from TensorFlow

8. Parameters Changed in Alexnet
Original Alexnet from tflearn demo had accuracy ~ 29%
Filter changed from 11 to 3x3
Stride changed from 5 to 2 to include more pixels in the convolution
Accuracy increased to ~ 80%

9. Original Alexnet

10. Original Alexnet

11. New Alexnet

12. New Alexnet When new Alexnet allowed to run for the full 500 epochs
Accuracy > 90%

13. New Alexnet Accuracy for images with 1 to 10 objects
Accuracy vs. Number of Objects in the Image

14. Confusion matrix Shows which classifications were mistaken
Row index: What it should have predicted
Column index: Actual prediction
Fuzziness possibly due to resolution error (Two objects stacked on top of each other)

15. How Alexnet compares to human performance
Verbal subitizing, children 6-16 years old [4]
Alexnet

16. References
[1] “Learning to Count Objects in Images” Visual Geometry Group, University of Oxford
[2]
[3]
[4] Verbal-subitizing-accuracy-as-a-function-of-numerosity-TD-14- typically