ledonthuan / keras-caltech-256

257-way Image Classification using Fully Connected Neural Network, Convolutional Neural Network built from scratch and Transfer Learning

Caltech-256

Caltech-256 is a challenging set of 257 (including the last category of clutter) object categories containing a total of only 30607 images. Furthermore this dataset is imbalanced as seen in the plot below. In this exercise I utilized different Neural Network architectures and compare their performance. This project took me exactly 1 month because of the scale of the problem and the training and tweaking multiple CNN models that most took overnight to train.

Sample Images

Category Distribution

Results

Model	Accuracy(Test-set)
Fully Connected	14%
VGG	44%
Inception	45%
Resnet	48%
VGG (Transfer-Learning)	57%
Inception (Transfer-Learning)	63%
Inception Resnet (Transfer-Learning)	71%
Xception (Transfer-Learning)	66%

Conclusion

As seen in the table above using a Convolutional Neural Network is a big leap in accuracy from Fully Connected Neural Networks and Transfer learning of is a significantly better than training a CNN from scratch. Furthermore, Transfer Learning is the fastest to train because you are only training a fraction of the network and in addition, it requires the least data.