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math-modeling-pipeline/phase-3-dl

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DL phase 3: multi-path image modeling in PyTorch

Software Engineering#pipeline#pytorch#image-processing#deep-learning#computer-vision#phase-3-dl
AuthorSOGERSEN
Version1.0.0
Installs0

System Documentation

What problem does it solve?

Phase 3-DL enables designing and implementing multiple PyTorch-based DL approaches for image processing and computer vision within the math-modeling-pipeline.

Core Features & Use Cases

  • Phase-3 DL Variant: Replaces traditional MATLAB implementations with Python + PyTorch for phase 3 in the pipeline.
  • Model Portfolio & Metrics: Supports multiple architectures (e.g., U-Net, SwinIR) with PSNR/SSIM/LPIPS evaluation and reports.
  • Execution Contracts & Environment: Enforces input/output contracts, GPU-accelerated training, and fallback mechanisms for reliability.

Quick Start

Prepare your data in {WORK_DIR}/数据集 and run the provided training script to begin Phase 3-DL.

Dependency Matrix

Required Modules

None required

Components

Standard package

💻 Claude Code Installation

Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.

Please help me install this Skill:
Name: math-modeling-pipeline/phase-3-dl
Download link: https://github.com/SOGERSEN/math-modeling-pipeline/archive/main.zip#math-modeling-pipeline-phase-3-dl

Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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