README.md

metadata

license: mit
library_name: onnx
pipeline_tag: image-classification
tags:
  - image-classification
  - mnist
  - convolutional-neural-network
  - onnx
  - onnxruntime
  - pytorch
  - dlab
datasets:
  - mnist
metrics:
  - accuracy

MNIST CNN Classifier

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

Architecture

Results

5-seed held-out test evaluation:

metric	value
test accuracy	99.6140% ± 0.0802 pp
test loss	0.14677 ± 0.00272
best validation loss	0.14282 ± 0.00138

Per-seed held-out test results:

seed	W&B run	test accuracy	test loss	best validation loss
1	5um57rnu	99.6100%	0.14772	0.14124
2	23f1frqb	99.6800%	0.14437	0.14317
3	25yaaj1o	99.4800%	0.15107	0.14403
4	3rrlxghp	99.6300%	0.14573	0.14150
5	y51200ov	99.6700%	0.14494	0.14418

The ONNX model was exported from the seed-1 checkpoint, which had the best validation loss in the final 5-seed evaluation sweep. Test metrics were not used for checkpoint selection and were logged in W&B sweep ikfs5ox8.

Model Details

• Dataset: MNIST
• Architecture: CNN
• Channels: [32, 64, 128]
• Convolutions per stage: 2
• Batch normalization: enabled
• Dropout: 0.1
• 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
• Early stopping: validation loss, patience 8, min delta 0.0005
• Source W&B run: 5um57rnu
• Source W&B sweep: ikfs5ox8

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-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 - 0.1307) / 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.
• metrics_summary.csv: compact 5-seed final evaluation summary.
• metadata.json: compact metadata for inference and provenance.

Limitations

This compact CNN is near MNIST saturation. Remaining errors are expected to be rare and often visually ambiguous or unusually written digits.