This is my project for the Deep Learning and AI program at DePaul.
It trains a neural network based on the Fashion MNist dataset to build a model for classifying clothing into predetermined categories.
There are 10 different options for responses. This is a WIP. It works with grey-scaled images that I resize manually ahead of time. Using the cv2.resize method so I can allow for random images of color and different sizes reduces the accuracy so that is something I am exploring.
print("I think this is a T-shirt or top")
print("I think this is a pair of pants or jeans")
print("I think this is a sweater/pullover")
print("I think this is a dress")
print("I think this is a coat")
print("I think this is a pair of sandals...or odd looking shoe.")
print("I think this is a shirt")
print("I think this is a sneaker.")
print("I think this is a bag.")
print("I think this is an ankle boot")
main.py: this file contains the construction of the model. The model itself is saved as model.h5
testImage.py: this is how you run test images.
First, run: python testImage.py
Next, it will ask you for an image. The image must exist in the same directory. Type the image name and hit enter.
The model will guess what your image is.