labeltify

Upload frames, and optionally their annotations, into LabelTify from DeepStream, OpenCV, Ultralytics, or any Python pipeline. The only thing it needs is a pipeline token.

pip install labeltify

1. On the dataset Upload page, create a pipeline token.
2. Upload:

from labeltify import LabelTifyClient

client = LabelTifyClient("labeltify_…")  # or set LABELTIFY_TOKEN and call LabelTifyClient()

img = client.upload_image(
    "DATASET_ID",
    jpeg_bytes,
    filename="cam01.jpg",
    captured_at="2026-06-01T12:00:00Z",
    camera="cam-01",
    pipeline="ds-prod",
    trigger="low_confidence",
    model_name="park-net",
    model_version="1.2",
)
client.upload_file("DATASET_ID", "frames/cam02.jpg", captured_at="2026-06-01T12:00:05Z", camera="cam-02")

The token knows its org, so you don't pass one. Timeouts, 429, and 5xx responses are retried with exponential backoff (max_retries=4 by default). Anything else raises LabelTifyError with the API's message and .status.

captured_at is required. Pass an aware datetime or an ISO 8601 string with Z or an offset. A naive datetime is rejected before any request.

original_detection is what the device's model saw (COCO, LabelTify, or YOLO JSON bytes). LabelTify stores it and does not show it, and the frame stays untouched. final_annotation is a ready annotation file, the same import as before. With it, work_state must be done or in_progress. Images with no final annotation stay untouched.

model_name, model_version, gps ((lat, lon) or {"lat", "lon"}), and additional_data (a JSON object, at most 2 KiB) are optional and separate. model_version requires model_name.

Frames whose corrupt reason is smear, grey, green, blank or truncated, and whose score is at least 0.5, are not uploaded. They come back in report["files"] with status skipped_corrupt, the score, and the reason. A blocks, tear or ghost hit is uploaded with the score so LabelTify can show it as Suspected corrupt. Pass skip_corrupt=False to upload every frame (they are still scored). Scoring uses every CPU core, up to 8 processes, and falls back to threads. Pillow and numpy are installed with the package.

Upload a folder with its annotations

report = client.upload_directory(
    "DATASET_ID",
    "datasets/parking",
    captured_at="exif_or_mtime",
    work_state="done",
    model_name="park-net",
    model_version="1.2",
)
print(len(report["items"]), "images stored in", report["requests"], "requests;", report["imported"])

This sends the folder the way dropping it on the dataset's Upload page would: images, plus COCO (polygons and boxes), YOLO (labels/*.txt with data.yaml or classes.txt), Pascal VOC, LabelMe, CSV, metadata.jsonl, a folder per class, or a LabelTify export. The server reads the annotations; the client only splits the folder into requests (at most 100 images and 80 MB each). A COCO file is cut down to each request's images, so polygons stay polygons.

• The dataset must already exist. Create it on the site. Parking polygons need type Instance segmentation. Traffic boxes need Object detection.
• captured_at is required. exif reads DateTimeOriginal and OffsetTimeOriginal (a missing offset names the file and stops). mtime uses the file's mtime as UTC. exif_or_mtime uses EXIF when the timestamp is there and mtime otherwise. You can also pass one aware datetime, a mapping of filename to datetime, or a function of the path. meta_for returns a full ImageMeta for one file.
• work_state="done" marks every image that received a final annotation. Images with no annotation stay untouched. With any annotation file in the folder, work_state must be done or in_progress.
• A zip in the folder is expanded here. Each member keeps its path as the upload filename, and each image is scored and gets its own metadata.
• Smear, grey, green, blank and truncated frames are left out unless you pass skip_corrupt=False. Other corrupt hits are uploaded and scored. Scoring runs on all cores.
• Create the pipeline token on that dataset's Upload page.
• One credit covers 10,000 uploaded images. A free dataset holds 100 photos.
• Frame embeddings follow the dataset setting. Turn them off on the dataset before a large upload when you do not want that charge.
• Use the portable pixel COCO files. The raw Studio shards store boxes as fractions from 0 to 1, and the server divides by width and height again.
• Two images with the same file name in different folders stop the upload before anything is sent; rename one.
• Images over 20 MiB, empty files, and files that are neither images nor annotations are listed in report["files"] and not sent. A zip is expanded when it holds at most 200 files and 20 MB.
• If a request fails after its retries, the error says how many images were already stored; run the folder again and only what is missing is uploaded (see below).
• compute_dhash=True also skips near-duplicate frames, like the Upload page. Pillow is already installed.

Migrating thousands of images

export LABELTIFY_TOKEN=labeltify_…
python -m labeltify upload ./photos --dataset DATASET_ID --captured-at exif_or_mtime --workers 6 --work-state done

or client.upload_directory(dataset_id, folder, workers=6, progress=print).

• Several requests at once. workers (default 4, at most 8; 1 sends one after another) requests run in parallel over kept connections, so there is no new TLS handshake per request. Each request holds at most max_request_bytes (default 24 MiB) and max_request_images (default 50). Files are read from disk only when their request is sent, so memory is about workers × 24 MiB, not the size of the folder.
• Resume for free. With skip_existing=True (default) every image is hashed (SHA-256), the dataset is asked which hashes it already holds (client.images_exist(dataset_id, hashes)), and only the rest is sent. Running the same folder again after an interruption takes seconds, not a second upload. Label files of images that are already there are still sent. force=True or skip_existing=False turns this off, and an older server without the lookup is handled as before.
• Progress. progress= is called after each request with requests_done, requests_total, images_stored and skipped_existing; the command line prints it.
• Frame embeddings follow the dataset setting and are made in the background after the upload: thousands of images take a while to get their vectors. The dataset's Images tab shows how many are done.
• If a request fails, the requests not started yet are dropped, those already stored stay stored, and the error says how many; run the command again to continue.

upload_bundle(dataset_id, files, images_meta=…, work_state=…) sends one such request from bytes you already have (files is a list of {"filename", "data", "content_type"}, and images_meta is one ImageMeta per image).

Upgrade from 0.4

labels is gone. Capture time is required. Corrupt frames are skipped unless you opt in.

# 0.4
client.upload_image(dataset_id, data, "cam.jpg", labels=open("ann.json", "rb").read(), work_state="done")

# 0.5
client.upload_image(
    dataset_id,
    data,
    filename="cam.jpg",
    captured_at="2026-06-01T12:00:00Z",
    final_annotation=open("ann.json", "rb").read(),
    work_state="done",
)

# 0.4
client.upload_directory(dataset_id, "frames", work_state="done")

# 0.5
client.upload_directory(dataset_id, "frames", captured_at="exif_or_mtime", work_state="done")

A deployment behind an access proxy can take extra headers on every request: LabelTifyClient(token, base_url=…, headers={"CF-Access-Client-Id": …, "CF-Access-Client-Secret": …}).

Send model proposals to the Loop review queue:

client.propose(
    "DATASET_ID",
    img["id"],
    [{"type": "bbox", "id": "p1", "classId": "car", "box": {"x": 0.1, "y": 0.1, "w": 0.2, "h": 0.2}, "source": "model", "confidence": 0.4}],
    model_ref="deepstream@1",
    uncertainty=0.8,
)

Boxes are normalized [0,1]. Camera, pipeline, and trigger are stored on the image so Loop can filter by them.

Local API: LabelTifyClient("labeltify_…", base_url="http://localhost:8787").

Download a dataset export

The export tool on the site (or the MCP export tool) hands out a file-list URL. One command pulls every image it lists, plus the annotation bundle:

export LABELTIFY_EXPORT_KEY="<key from the export>"
python -m labeltify download "https://labeltify.com/api/ds/EXPORT_ID/files" --out ./ofan --workers 16

or labeltify download … with the installed console script. From Python:

from labeltify import download_export

summary = download_export("https://labeltify.com/api/ds/…/files", "./ofan", key="<key>", progress=print)
# {"images": 77769, "downloaded": 77000, "skipped": 769, "failed": 0, "bytes": …, "annotations": True, "failures": []}

The export key can be passed via --key / LABELTIFY_EXPORT_KEY, as an explicit key= argument to download_export, or left in the URL as ?key=… (still supported for browsers and old scripts). For scripts and CI, prefer the header or explicit argument so the key does not land in request URLs and logs.

• Resume for free. Files already on disk with the right size (and SHA-256, when the list carries one) are skipped, so re-running after an interruption only fetches what is missing. Partial files land in name.part and are replaced atomically.
• Parallel. --workers (default 16, at most 64) downloads at once. Network errors, 429, and 5xx are retried with exponential backoff (--retries, default 5), honouring Retry-After. An expired image link (403/404) is reported as failed, not retried forever.
• Annotations are extracted next to the images, unless --no-annotations. Zip entries that would land outside the output folder are refused. An images-only export has no annotations, so that step is skipped on its own.
• --no-verify skips the size and hash checks. An expired file-list link (410) says so clearly. When every file made it, the exit code is 0 and the last stdout line is the JSON summary; otherwise it is 1.

Develop

pip install -e "clients/python[test]"
pytest clients/python/tests

Bump version in pyproject.toml to publish. Each new version that lands on main is published to PyPI by .github/workflows/publish-python.yml.