# Snip Pruning Eval

> Evaluates a single-shot pruning method's ability to identify and remove unimportant network connections at initialization, preserving classification accuracy across varying sparsity levels on standard vision and sequence datasets. Use when the user wants to benchmark on MNIST, CIFAR-10, Tiny-ImageNet, or asks about evaluating this task. Reports accuracy.

- Skill: `qhjqhj00/snip-pruning-eval` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/snip-pruning-eval`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/snip-pruning-eval/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Productivity
- Author: qhjqhj00 (https://skillmd.com/u/qhjqhj00)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/qhjqhj00/snip-pruning-eval

---


# snip-pruning-eval

> SNIP: Single-shot Network Pruning based on Connection Sensitivity — Lee et al. (2018) (arXiv:1810.02340, 2018)

## What this evaluates

Evaluates a single-shot pruning method's ability to identify and remove unimportant network connections at initialization, preserving classification accuracy across varying sparsity levels on standard vision and sequence datasets.

## Datasets

- **MNIST** — total ?; splits: train (-1), val (-1)
- **CIFAR-10** — total ?; splits: train (-1), val (-1)
- **Tiny-ImageNet** — total ?; splits: train (-1)

## Metrics

- `accuracy` **(primary)** — range: percent
  - Percentage of correctly classified samples out of the total test set. Calculated as (number of correct predictions / total number of samples) * 100%.

## Input / output format

**Input**: Image tensors (28x28 grayscale for MNIST, 32x32 RGB for CIFAR-10) or sequential row vectors for RNN tasks.

**Output**: Class label predictions (0-9 for MNIST/CIFAR-10) or probability distributions over classes.

## Scoring recipe

```python
def compute_accuracy(predictions, gold_labels):
    total = len(gold_labels)
    correct = sum(1 for p, g in zip(predictions, gold_labels) if p == g)
    return (correct / total) * 100
```

## Common pitfalls

- Confusing the sparsity level $\bar{\kappa}$ (percentage of pruned parameters) with the remaining parameter count $\kappa$.
- Assuming the method requires pretraining or iterative pruning schedules; SNIP computes sensitivity once at random initialization.
- Using incorrect mini-batch sizes for sensitivity computation (100 for MNIST, 128 for CIFAR), which directly affects which connections are pruned.

## Evidence (verbatim from paper)

> Overall, our approach prunes a substantial amount of parameters in a variety of network models with minimal or no loss in accuracy (< 1%).

## Citation

```bibtex
@misc{lee2018snip,
  title={SNIP: Single-shot Network Pruning based on Connection Sensitivity},
  author={Lee et al. (2018)},
  year={2018},
  note={arXiv:1810.02340}
}
```

- arXiv: 1810.02340