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--- |
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license: apache-2.0 |
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datasets: |
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- fashion_mnist |
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language: |
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- en |
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metrics: |
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- accuracy |
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pipeline_tag: image-classification |
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--- |
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# Fashion-MNIST Baseline Classifier |
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## Model Details |
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- **Model Name:** fashion-mnist-base |
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- **Framework:** Custom implementation in Python |
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- **Version:** 0.1 |
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- **License:** Apache-2.0 |
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## Model Description |
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This is a neural network model developed from the ground up to classify images from the Fashion-MNIST dataset. |
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The dataset comprises 70,000 grayscale images across 10 categories. Each example is a 28x28 grayscale image, |
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associated with a label from 10 classes including T-shirts/tops, trousers, pullovers, dresses, coats, sandals, shirts, sneakers, bags, and ankle boots. |
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## Intended Use |
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This model is intended for educational purposes and as a baseline for more complex implementations. It can be used by students and AI enthusiasts |
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to understand the workings of neural networks and their application in image classification. |
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## Training Data |
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The model was trained on the Fashion-MNIST dataset, which contains 60,000 training images and 10,000 test images. |
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Each image is 28x28 pixels, grayscale, associated with one of 10 classes representing different types of clothing and accessories. |
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### Architecture Details: |
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- Input layer: 784 neurons (flattened 28x28 image) |
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- Hidden layer 1: 256 neurons, ReLU activation, Dropout |
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- Hidden layer 2: 64 neurons, ReLU activation, Dropout |
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- Output layer: 10 neurons, logits |
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### Hyperparameters: |
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- Learning rate: 0.005 |
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- Batch size: 32 |
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- Epochs: 25 |
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The model uses a self-implemented stochastic gradient descent (SGD) optimizer. |
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## Evaluation Results |
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The model achieved the following performance on the test set: |
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- Accuracy: 86.7% |
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- Precision, Recall, and F1-Score: |
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| Label | Precision | Recall | F1-score | |
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|-------------|-----------|---------|----------| |
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| T-shirt/Top | 0.847514 | 0.767 | 0.805249 | |
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| Trouser | 0.982618 | 0.961 | 0.971689 | |
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| Pullover | 0.800000 | 0.748 | 0.773127 | |
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| Dress | 0.861868 | 0.886 | 0.873767 | |
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| Coat | 0.776278 | 0.805 | 0.790378 | |
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| Sandal | 0.957958 | 0.957 | 0.957479 | |
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| Shirt | 0.638587 | 0.705 | 0.670152 | |
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| Sneaker | 0.935743 | 0.932 | 0.933868 | |
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| Bag | 0.952381 | 0.960 | 0.956175 | |
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| Ankle-Boot | 0.944554 | 0.954 | 0.949254 | |
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## Limitations and Biases |
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Due to the nature of the training dataset, the model may not capture the full complexity of fashion items in diverse real-world scenarios. |
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In practice, we found out that it is sensitive to background colors and article's proportions. |
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## How to Use |
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```python |
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import torch |
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import torchvision.transforms as transforms |
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from PIL import Image |
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model = torch.load('fashion-mnist-base.pt') |
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# Images need to be transformed to the `fashion MNIST` dataset format |
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transform = transforms.Compose( |
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[ |
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transforms.Resize((28, 28)), |
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transforms.Grayscale(), |
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transforms.ToTensor(), |
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transforms.Normalize((0.5,), (0.5,)), # Normalization |
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transforms.Lambda(lambda x: 1.0 - x), # Invert colors |
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transforms.Lambda(lambda x: x[0]), |
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transforms.Lambda(lambda x: x.unsqueeze(0)), |
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] |
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) |
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img = Image.open('fashion/dress.png') |
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img = transform(img) |
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model.predictions(img) |
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``` |
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## Sample Output |
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``` |
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{'Dress': 84.437744, |
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'Coat': 7.631796, |
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'Pullover': 4.2272186, |
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'Shirt': 1.297625, |
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'T-shirt/Top': 1.2237197, |
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'Bag': 0.9053432, |
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'Trouser/Jeans': 0.27268794, |
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'Sneaker': 0.0031491981, |
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'Ankle-Boot': 0.00063403655, |
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'Sandal': 8.5103806e-05} |
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``` |
