ai-agent

RankIQA.PyTorch: Image Quality Assessment

Get image quality evaluation with RankIQA.PyTorch, a PyTorch model for founders
153 stars31 forksPythonGuide quality 8/10Updated 6/20/2020100% free · open source
What it does

RankIQA.PyTorch is a PyTorch-based model for evaluating the quality of images

When to use it
  • When you need to assess the quality of a large number of images automatically
  • When you want to compare the performance of different image processing algorithms
  • When you need to filter out low-quality images from a dataset
Ready-to-paste prompt
python test.py -d /path/to/your/test/images -m models/RankIQA.pth
Heads up: Make sure you have PyTorch installed and a compatible CUDA version, as specified in the README, to avoid GPU compatibility issues
Saves to your device
Use with Claude
New

Skip the builder — one click puts this in Claude, Cursor, Antigravity and more.

🛠️ Technical setup

Expect 20–40 minutes in a terminal — or let your AI agent drive it.

Try it instantly — no install
Claude Code
mkdir -p ~/.claude/skills/rankiqa-pytorch && curl -fsSL https://workflowstacks.com/api/skills/rankiqa-pytorch/claude-skill -o ~/.claude/skills/rankiqa-pytorch/SKILL.md
Open in another AI app

Opens the app with this repo with the prompt ready to go — no copy-paste needed.

Connect the whole catalog (MCP)
claude mcp add --transport http workflowstacks https://workflowstacks.com/api/mcp

Adds a WorkflowStacks connector to Claude Code: search and load any skill here by chatting.

How RankIQA.PyTorch: Image Quality Assessment works
Codeflow
Free to inspect

RankIQA.PyTorch: Image Quality Assessment is a medium Python project (~3.2k lines across 46 code files). Expect some technical setup — comfortable with a terminal, or ask a developer. Last commit 76 months ago, no license file, no tests found.

Size
Medium codebase
~3.2k lines · 46 code files · 27 min skim
Setup
Some technical setup
Comfortable with a terminal? 20–40 min. Otherwise ask a dev.
Runs on
Python
No API keys detected
Python 99%Shell 1%
Where to start reading
  1. 1
    README.md
    Start here — what it does and how to install it
  2. 2
    main.py
    Where the program starts running
  3. 3
    requirements.txt
    Dependencies and the commands it exposes
  4. 4
    demos/__init__.py
    A worked example — copy this to get going
What's in each folder
demos/Examples you can copy7 files
models/Data models & types16 files
utils/Helper scripts9 files
z_task_shell/Folder6 files
applications/Folder4 files
criterions/Folder4 files
dataloader/Folder3 files
checkpoints/Folder1 files
READMENo tests foundDocumentedNo CIExamples includedNo licenseLast update 76 mo ago
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Details
Creator
zheng-yuwei
Language
Python
Category
ai-agent
Published
6/18/2020

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