Yuan Lab袁静课题组

This is a sample reproduction guide for the Project Practice section, demonstrating a step-by-step layout skeleton. Real entries will be filled in for a specific open-source project with genuine environment, data and run steps. The headings and code blocks below are generic placeholders, not a runnable reproduction recipe.

Project Overview

Describe the chosen project’s goal, method and use case, with its repository URL and license.

Environment Setup

Describe the runtime and dependencies. Create an isolated conda environment per project to avoid conflicts.

# Example: isolated environment (use versions from the project README)
conda create -n <project> python=3.10 -y
conda activate <project>

# Install dependencies per the project requirements
pip install -r requirements.txt

Data Preparation

Describe the datasets needed — source, download and directory layout. Honor the license of any third-party data and cite its source.

Run Steps

List the key reproduction steps in order, each with a copyable command.

# 1. Preprocessing (if any)
python preprocess.py --config configs/example.yaml

# 2. Training / inference
python run.py --config configs/example.yaml

# 3. Evaluation
python evaluate.py --results output/

Results & Verification

Explain how to check whether the reproduced results match the paper or repository. Record numerical discrepancies, randomness and hardware effects honestly.

FAQ

• Dependency conflicts: follow the versions in the project README; install a conflicting package with pip --no-deps if needed.
• Out of memory: reduce batch size or use gradient accumulation; on CPU expect much longer runtimes.
• Data download failures: check the network or use a regional mirror; contact the data provider if needed.