Image Classification Best Practices
Guides the agent on choosing architectures, training recipes and benchmark targets for image classification.
- Repository
- aiming-lab/AutoResearchClaw
- Stars
- 14,802
- Licence
- MIT
- Files
- 1 file
What it does
This skill gives an AI agent a reference sheet of image classification practices. When the agent works on tasks like CIFAR or ImageNet benchmarks, it suggests model types suited to dataset size (such as ResNet for small datasets and ViT or Swin for large ones), typical optimizer and learning-rate settings, common data augmentation methods, and expected accuracy numbers to compare against. It is guidance text only and does not run anything itself.
Great for
- Researchers setting up a CIFAR-10 or ImageNet baseline experiment
- Students learning which architectures and training settings are typical for image classification
- Engineers who want quick reference numbers to sanity-check their model accuracy
Skip it if
- Production deployment decisions, since the file gives general starting points and not tested configurations for any specific dataset (the reason: the numbers are approximate and task-specific results vary)
- Non-image tasks such as text or tabular classification (the reason: the content covers only vision models and benchmarks)
Try asking
- “Which architecture should I start with for a CIFAR-100 classification experiment?”
- “What learning rate and weight decay should I use when training a ViT on a small image dataset?”
- “What accuracy should a ResNet-50 reach on ImageNet so I can tell if my training run is working?”
- “Suggest data augmentation methods for an image classifier trained on CIFAR-10.”
Install in 3 steps
- 1
Download the skill
The ZIP contains the 1 original files, the MIT licence and an attribution note.
cv-classification.zip - 2
Add it to your agent
Claude Code (all projects)
mkdir -p ~/.claude/skills && unzip cv-classification.zip -d ~/.claude/skills/Claude Code / Agent SDK (this project only)
mkdir -p .claude/skills && unzip cv-classification.zip -d .claude/skills/Claude apps (web/desktop): Settings → Capabilities → Skills → Upload skill, and choose the ZIP.
- 3
Ask for the task
No special command needed — the agent reads the skill's description and loads it when your request matches. Try one of the prompts above.
Get better results
- Treat the listed benchmark accuracies as rough reference points and verify them against the original papers cited in the metadata (He et al. 2016; Dosovitskiy et al. 2021).
- Name your dataset and compute budget in the request so the agent can pick between the small, medium and large scale recommendations.
- Pair this skill with your own experiment logs so the agent can compare your results against the listed targets.
What's inside
- SKILL.md
Redistributed unmodified from aiming-lab/AutoResearchClaw under the MIT licence. The assessment was drafted with AI from the skill's files on 2026-10-29. Skills can instruct your agent to run code — read SKILL.md and any scripts before installing.