tsilva/mnist-classifier-lstm

MNIST LSTM Classifier

This repository contains a validation-selected MNIST LSTM digit classifier trained with dlab.

Architecture

[image]

Results

3-seed confirmation and test audit for the selected LSTM recipe:

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 `ntzpryf9`.

Model Details

• —Dataset: MNIST
• —Architecture: LSTM sequence classifier
• —Sequence axis: columns
• —Pooling: last
• —Hidden width: 384
• —Recurrent layers: 2
• —Bidirectional: false
• —Dropout: 0.1
• —Optimizer: AdamW
• —Learning rate: 0.003
• —Weight decay: 0.001
• —Scheduler: cosine
• —Label smoothing: 0.01
• —Batch size: 512
• —Training augmentation: true
• —Checkpoint selection: max validation accuracy
• —Source W&B run: `652i33os`

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-lstm",
    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 LSTM 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.