metadata
library_name: pytorch
pipeline_tag: image-classification
tags:
- mnist
- image-classification
- pytorch
license: mit
MNIST CNN (1-minute test run)
A small CNN trained from scratch on ylecun/mnist
for a fixed 60-second wall-clock budget (13129 steps, ~28.01 epochs).
Results
| Metric | Value |
|---|---|
| Test accuracy | 0.9939 |
| Test loss | 0.0232 |
| Training wall-clock | 60.0s |
| Steps | 13129 |
| Throughput | 27993 images/s |
Architecture
Conv(1->32, 3x3) -> ReLU -> MaxPool(2) -> Conv(32->64, 3x3) -> ReLU -> MaxPool(2)
-> Flatten -> Linear(3136->128) -> ReLU -> Linear(128->10).
Defined in model.py.
Usage
import torch
from model import SmallCNN
model = SmallCNN()
state = torch.load("model.pt", map_location="cpu")
model.load_state_dict(state["model_state_dict"])
model.eval()
# Input: float tensor (N, 1, 28, 28) scaled to [0, 1], then normalized
x = (x - 0.1307) / 0.3081
logits = model(x)
pred = logits.argmax(1)
Training preprocessing also applied random translation (pad 2, random crop) to the train split.