tsilva/fashion-mnist-classifier-cnn

Fashion-MNIST CNN Classifier

This repository contains a validation-selected shallow CNN classifier for Fashion-MNIST, trained with dlab.

Architecture

Results

Seed-confirmed validation performance for this recipe, plus the final one-time test-set evaluation performed after checkpoint selection:

metric	value
validation loss	0.2650 ± 0.0098
validation accuracy	95.14% ± 0.19 pp
test loss	0.2791
test accuracy	94.76%
test correct	9476 / 10000

The ONNX model was exported from the best validation-loss checkpoint from the selected recipe. The test metrics were not used to select the checkpoint and were logged in W&B run 8h2ywarm.

Model Details

• Dataset: Fashion-MNIST
• Architecture: shallow CNN
• Channels: [64, 128, 256]
• Dropout: 0.2
• Optimizer: Adam
• Learning rate: 0.001
• Weight decay: 3e-5
• Scheduler: cosine
• Label smoothing: 0.02
• Weight averaging: EMA
• Training augmentation: random affine rotation/translation/scale
• Source W&B run: qwfq2ihy
• Source checkpoint: 044.ckpt
• Final test W&B run: 8h2ywarm

Input / Output

Use model.onnx for code-independent inference.

• Input name: images
• Input shape: [batch, 1, 28, 28]
• Input dtype: float32
• Output name: logits
• Output shape: [batch, 10]

Preprocessing:

• Convert image to grayscale.
• Resize to 28 x 28.
• Scale pixel values to [0, 1].
• Normalize with mean 0.2860 and standard deviation 0.3530.
• Arrange the tensor as channels-first [batch, 1, 28, 28].

Usage

Install the runtime dependencies:

pip install huggingface_hub onnxruntime pillow numpy

Run inference with the ONNX model:

import numpy as np
import onnxruntime as ort
from huggingface_hub import hf_hub_download
from PIL import Image

LABELS = {
    0: "T-shirt/top",
    1: "Trouser",
    2: "Pullover",
    3: "Dress",
    4: "Coat",
    5: "Sandal",
    6: "Shirt",
    7: "Sneaker",
    8: "Bag",
    9: "Ankle boot",
}

model_path = hf_hub_download(
    repo_id="tsilva/fashion-mnist-classifier-cnn",
    filename="model.onnx",
)

image = Image.open("example.png").convert("L").resize((28, 28))
x = np.asarray(image, dtype=np.float32) / 255.0
x = (x - 0.2860) / 0.3530
x = x[None, None, :, :].astype(np.float32)

session = ort.InferenceSession(model_path, providers=["CPUExecutionProvider"])
logits = session.run(["logits"], {"images": x})[0]
prediction = int(logits.argmax(axis=1)[0])

print(prediction, LABELS[prediction])

Labels

Fashion-MNIST labels:

id	label
0	T-shirt/top
1	Trouser
2	Pullover
3	Dress
4	Coat
5	Sandal
6	Shirt
7	Sneaker
8	Bag
9	Ankle boot

Files

• model.onnx: ONNX export of the validation-selected checkpoint, using opset 17. Prefer this file for portable inference.
• model.ckpt: PyTorch Lightning checkpoint for the same model. This is code-dependent and mainly useful for PyTorch-based inspection or continued experimentation.
• config.yaml: resolved training config.
• metrics.csv: training/validation metric history.
• modeling.py: minimal PyTorch model definition and checkpoint loader for users who specifically need the checkpoint path.
• label_mapping.json: label id to class name mapping.

Limitations

Most remaining validation errors are visually plausible confusions between related Fashion-MNIST classes, especially shirt/top/pullover/coat and sneaker/ankle boot cases.