Pictograph
Label images, train vision models, run them anywhere.
Docs · Quick start · API reference · App
Four ways in. Same API underneath, so you can move between them.
| Python | pip install pictograph, this repo |
| CLI | pip install "pictograph[cli]" · reference |
| REST | Any language, X-API-Key header · reference |
| Claude skill | Let an agent drive it · guide |
Quick start
pip install pictograph
Upload, label from a text prompt, train, predict.
from pictograph import Client
client = Client(api_key="pk_live_...") # or set PICTOGRAPH_API_KEY
client.datasets.create(
name="road-signs",
annotation_types=["bbox"],
)
client.images.upload(
dataset_name="road-signs",
file_path="sign.jpg",
)
client.auto_annotate.batch(
dataset_name="road-signs",
image_filenames=["sign.jpg"],
classes=[{"name": "stop sign", "type": "bbox"}],
)
client.exports.create(
dataset_name="road-signs",
name="v1",
format="coco",
)
run = client.training.create(
dataset_name="road-signs",
export_name="v1",
pipeline_type="rfdetr_detection",
name="signs-detector",
config={"epochs": 30},
)
client.training.wait_for_completion(run_id=run.id)
result = client.models.predict(
name="signs-detector",
image="test.jpg",
)
for a in result.annotations:
print(a.name, round(a.confidence, 3), a.bounding_box)
The same thing from the shell:
pictograph login
pictograph datasets create road-signs --type bbox
pictograph images upload road-signs ./sign.jpg
pictograph models predict signs-detector ./test.jpg
Or over REST:
curl -s https://api.pictograph.io/api/v1/developer/datasets/ \
-H "X-API-Key: $PICTOGRAPH_API_KEY"
Resources are addressed by name everywhere. There are no IDs to look up first.
Typed
Responses are Pydantic models. Failures are typed exceptions.
from pictograph.exceptions import NotFoundError, PaymentRequiredError, RateLimitError
try:
client.datasets.get(name="does-not-exist")
except NotFoundError:
...
Transient failures retry with backoff, and writes carry an idempotency key so a retry cannot apply twice.
Async
Every method has an async twin with the same signature.
import asyncio
from pictograph import AsyncClient
async def main():
async with AsyncClient() as client:
print([d.name for d in await client.datasets.list()])
asyncio.run(main())
Run models on your own hardware
from pictograph import get_model
model = get_model(name="signs-detector", task="object_detection")
result = model.predict(image="test.jpg")
Weights download and cache on first use. ONNX, PyTorch, ExecuTorch and TensorRT are supported targets. See local inference.
Agents
pip install "pictograph[agents]"
pictograph agents install-skill --target claude-code
from pictograph.agents import Toolkit
tools = Toolkit(client).as_anthropic_tools() # or .as_openai_tools()
Offline utilities
No network needed, so an existing local dataset can be brought into typed models before uploading anything.
pictograph.formats |
Read and write COCO, YOLO, Pascal VOC |
pictograph.metrics |
Score detections against ground truth by IoU |
pictograph.augment |
Transform images, remapping annotation geometry with them |
pictograph.tile |
Slice images into a grid, clipping annotations per tile |
Documentation
Full reference at pictograph.io/docs.
Quick start · Installation · Authentication · API reference · CLI · Agents · Annotation format · Auto-annotation · Local inference · Deployments · Export conversion · Async client · Error handling · Rate limits
Requires Python 3.10 or newer. Extras: [cli], [agents], [inference],
[torch], [all].
Contributing, security, license
CONTRIBUTING.md has the development setup and the checks a change must pass. Report vulnerabilities per SECURITY.md, not as a public issue. MIT licensed, see LICENSE.
1 maintainer
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