This is a neural network model developed from the ground up to classify images from the Fashion-MNIST dataset.
The dataset comprises 70,000 grayscale images across 10 categories. Each example is a 28x28 grayscale image,
associated with a label from 10 classes including T-shirts/tops, trousers, pullovers, dresses, coats, sandals, shirts, sneakers, bags, and ankle boots.
Intended Use
This model is intended for educational purposes and as a baseline for more complex implementations. It can be used by students and AI enthusiasts
to understand the workings of neural networks and their application in image classification.
Training Data
The model was trained on the Fashion-MNIST dataset, which contains 60,000 training images and 10,000 test images.
Each image is 28x28 pixels, grayscale, associated with one of 10 classes representing different types of clothing and accessories.
The model uses a self-implemented stochastic gradient descent (SGD) optimizer.
Evaluation Results
The model achieved the following performance on the test set:
Accuracy: 86.7%
Precision, Recall, and F1-Score:
Label
Precision
Recall
F1-score
T-shirt/Top
0.847514
0.767
0.805249
Trouser
0.982618
0.961
0.971689
Pullover
0.800000
0.748
0.773127
Dress
0.861868
0.886
0.873767
Coat
0.776278
0.805
0.790378
Sandal
0.957958
0.957
0.957479
Shirt
0.638587
0.705
0.670152
Sneaker
0.935743
0.932
0.933868
Bag
0.952381
0.960
0.956175
Ankle-Boot
0.944554
0.954
0.949254
Limitations and Biases
Due to the nature of the training dataset, the model may not capture the full complexity of fashion items in diverse real-world scenarios.
In practice, we found out that it is sensitive to background colors and article's proportions.
How to Use
python
1import torch
2import torchvision.transforms as transforms
3from PIL import Image
45model = torch.load('fashion-mnist-base.pt')67# Images need to be transformed to the `fashion MNIST` dataset format8transform = transforms.Compose(9[10 transforms.Resize((28,28)),11 transforms.Grayscale(),12 transforms.ToTensor(),13 transforms.Normalize((0.5,),(0.5,)),# Normalization14 transforms.Lambda(lambda x:1.0- x),# Invert colors15 transforms.Lambda(lambda x: x[0]),16 transforms.Lambda(lambda x: x.unsqueeze(0)),17]18)1920img = Image.open('fashion/dress.png')21img = transform(img)22model.predictions(img)
