Workspace level new methods

At the workspace level you can now search and delete images.

Sample code using both:

"""Delete all orphan images matching a workspace-level search query.

Usage:
    python tests/manual/demo_search_delete.py
"""

from roboflow import Roboflow

QUERY = "project:false"
PAGE_SIZE = 100
DELETE_BATCH_SIZE = 1000
DRY_RUN = False  # set to False to actually delete

WORKSPACE_SLUG = "workspace"


def main():
    rf = Roboflow()
    workspace = rf.workspace(WORKSPACE_SLUG)

    print(f"Workspace: {workspace.url}")
    print(f"Query: {QUERY}")
    print(f"Dry run: {DRY_RUN}")
    print()

    # Collect all matching image IDs.
    # When deleting (not dry run), skip continuation tokens because deleted
    # images shift the result set — always re-search from page 1.
    all_ids = []
    token = None
    while True:
        page = workspace.search(
            QUERY,
            page_size=PAGE_SIZE,
            fields=["filename"],
            continuation_token=token,
        )
        results = page.get("results", [])
        if not results:
            break
        total = page.get("total", "?")
        all_ids.extend(img["id"] for img in results)
        print(f"Found {len(all_ids)}/{total} images so far...")
        token = page.get("continuationToken")
        if not token:
            break

    print(f"\nTotal images matching '{QUERY}': {len(all_ids)}")

    if not all_ids:
        print("Nothing to delete.")
        return

    if DRY_RUN:
        print("\n[DRY RUN] Would delete the above images. Set DRY_RUN=False to proceed.")
        return

    for i in range(0, len(all_ids), DELETE_BATCH_SIZE):
        batch = all_ids[i : i + DELETE_BATCH_SIZE]
        print(f"Deleting batch {i // DELETE_BATCH_SIZE + 1} ({len(batch)} images)...")
        result = workspace.delete_images(batch)
        print(f"  deleted={result.get('deletedSources')}, skipped={result.get('skippedSources')}")

    print(f"\nDone. Deleted {len(all_ids)} images.")


if __name__ == "__main__":
    main()

1.2.15 - Upload images with metadata

We now make it available to the python sdk to upload metadata when starting upload

Export search results functionality

We provide a new method to export images and annotations based on search queries.
It replicates the Asset Library Download functionality on the CLI.

RFDETR large and seg models upload

Includes model upload for new rf-detr models.
pi_heif discontinued Python3.9 support, so this is handled in the release as well.

Add yolo26 upload

Users can now upload yolo26 models from ultralytics 8.4.1 or higher

v1.2.11

What's Changed

• adds curlable script to sign urls to images in s3 buckets by @tonylampada in #419
• Add ability to configure model training epochs by @PawelPeczek-Roboflow in #420

Full Changelog: v1.2.10...v1.2.11

v1.2.10

What's Changed

• Added Confidence Argument to keypoint detection model by @Greenstan in #354
• Add 'rfdetr-seg-preview' to supported model types by @capjamesg in #414
• Bump version to 1.2.10 by @iurisilvio in #416

New Contributors

• @Greenstan made their first contribution in #354

Full Changelog: v1.2.9...v1.2.10

v1.2.9

What's Changed

• fix is_prediction function call by @sberan in #413

Full Changelog: v1.2.8...v1.2.9

v1.2.8

What's Changed

• Add are_predictions flag to dataset upload by @sberan in #412

Full Changelog: v1.2.7...v1.2.8

v1.2.7

What's Changed

• configuring devcontainer to call localhost by @digaobarbosa in #409
• Create Project object local instead of calling API after create project by @iurisilvio in #411

Full Changelog: v1.2.6...v1.2.7