prompt2dataset 0.1.0

Build labeled image datasets from a plain-English prompt.

prompt2dataset

Build labeled image datasets from a plain-English prompt.

$ cd my-dataset
$ p2d add
What image dataset do you want to build? > bird species native to the Pacific Northwest

prompt2dataset resolves your description into subjects via Claude, fetches images from one or more sources, deduplicates, downloads, and writes a manifest.

Installation

pip install prompt2dataset

p2d add, review, and info work with this base install. Training requires PyTorch. Install the CPU or CUDA extras depending on your hardware:

pip install "prompt2dataset[train]"       # CPU
pip install "prompt2dataset[train-cuda]"  # CUDA (installs matching torch/torchvision)

Setup

prompt2dataset needs an Anthropic API key. On first run it will prompt you and save the key to a local .env file. Or set it yourself:

# .env
ANTHROPIC_API_KEY=sk-ant-...
P2D_CONTACT=[e-mail]   # included in API request headers per Wikimedia's policy

Usage

All commands operate on the current directory.

p2d add

Prompts for a dataset description, resolves subjects, and downloads images. Run it again in the same directory to fetch additional subjects without re-downloading what's already there.

$ mkdir pacific-northwest-birds && cd pacific-northwest-birds
$ p2d add

p2d review

Step through downloaded images and mark them valid or delete them.

$ p2d review
$ p2d review --misclassified   # only images that a trained model got wrong

Keys: A accept, D delete, S skip, Q quit.

p2d info

Print dataset statistics and the subject list.

p2d train

Fine-tune a pretrained image classifier on the dataset. Uses torch-lr-finder to find a good learning rate automatically, then trains for N epochs and exports a TorchScript model.

$ p2d train
$ p2d train --model resnet50 --epochs 10

Options: --epochs, --val-split, --img-size, --model (mobilenet_v2, resnet18, resnet50).

Data sources

Source	Best for
DuckDuckGo	Broad or niche subjects, recent events, pop culture
Wikimedia Commons	Well-documented subjects with Wikipedia articles
iNaturalist	Animals, plants, fungi - research-grade, taxonomy-tagged
Openverse	General subjects, scenes, cultural content

None require an API key. Sources are selected interactively when you run p2d add.

Output layout

my-dataset/
  american-robin/
    american-robin_a3f1c8d2e9b4.jpg
    ...
  stellers-jay/
    ...
  .p2d/
    manifest.json       dataset metadata and item list
    labels.csv          filename, subject, source
    subjects.json       resolved subject list (cached)
    model.pt            TorchScript model (after p2d train)
    labels.json         class names in output order
    report.json         per-class precision/recall/F1
    misclassified.json  validation images the model got wrong

manifest.json is the authoritative record. Everything in .p2d/ is generated and can be reconstructed.

Global flag

--debug enables verbose logging for all commands:

p2d --debug add

License

MIT