Mnist Classifier Gru

Representative checkpoint selected by validation accuracy:

The ONNX model was exported from the validation-selected checkpoint. Test metrics were produced after the recipe was selected and were logged in W&B test-audit run enmuxlt3.

Model Details

• Dataset: MNIST
• Architecture: GRU sequence classifier
• Sequence axis: columns
• Pooling: last
• Hidden width: 384
• Recurrent layers: 1
• Bidirectional: false
• Dropout: 0.2
• Optimizer: AdamW
• Learning rate: 0.003
• Weight decay: 0.001
• Scheduler: cosine
• Label smoothing: 0
• Batch size: 512
• Training augmentation: true
• Checkpoint selection: max validation accuracy
• Source W&B run: fs4fa4vl

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:

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-gru",
    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:

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 GRU model treats each MNIST image as a short sequence rather than using convolutional inductive bias. It is intended for normalized 28 x 28 grayscale MNIST-style images; remaining errors are expected to concentrate in ambiguous handwritten digits and distribution shifts outside that input format.