metric	value
validation accuracy	99.3600% ± 0.0817 pp
validation loss	0.15172 ± 0.00235
test accuracy	99.4470% ± 0.0195 pp
test loss	0.14746 ± 0.00034
test errors	55.3 ± 1.95 / 10000

The ONNX model was exported from the best run checkpoint. Test metrics were produced after the recipe was selected and were logged in W&B sweep xa56lubb.

Model Details

Dataset: MNIST
Architecture: MLP
Hidden width: 1024
Hidden layers: 3
Activation: ReLU
Batch normalization: enabled
Dropout: 0.2
Optimizer: Adam
Learning rate: 0.001
Weight decay: 0.0001
Scheduler: OneCycleLR
Label smoothing: 0.02
Weight averaging: EMA
Batch size: 512
Training augmentation: random affine rotation/translation/scale
Source W&B run: gsuy1ifx
Source W&B sweep: xa56lubb

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 0.1307 and standard deviation 0.3081.
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:

python

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

LABELS = {
    0: "0",
    1: "1",
    2: "2",
    3: "3",
    4: "4",
    5: "5",
    6: "6",
    7: "7",
    8: "8",
    9: "9",
}

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

image = Image.open("example.png").convert("L").resize((28, 28))
x = np.asarray(image, dtype=np.float32) / 255.0
x = (x - 0.1307) / 0.3081
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

MNIST labels:

id	label
0	0
1	1
2	2
3	3
4	4
5	5
6	6
7	7
8	8
9	9

Files

model.onnx: ONNX export of the validation-selected checkpoint. 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 Hydra training config.
metrics.csv: training metrics from the uploaded checkpoint run.
metadata.json: compact metadata for inference and provenance.

Limitations

This MLP does not use convolutional inductive bias. It performs strongly on MNIST, but remaining errors are mostly concentrated in ambiguous or unusually written digits.