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Data Management on Roboflow
Project Types
| Type | Annotation Format | Use Case |
|---|---|---|
| Object Detection | Bounding box (polygon/mask auto-converted) | Locate objects with boxes |
| Instance Segmentation | Polygon, Mask | Pixel-level per-object boundaries |
| Semantic Segmentation | Polygon, Mask | Pixel-level class regions |
| Keypoint Detection | Keypoints (skeleton) | Pose/skeleton estimation |
| Single-Label Classification | Image-level label (no drawn annotations) | One class per image |
| Multi-Label Classification | Image-level labels | Multiple classes per image |
Project type is set at creation and cannot be changed later.
Uploading Data
Methods
| Method | Best For | Formats |
|---|---|---|
| Web UI drag-and-drop | < 1,000 images | JPG, PNG, WEBP, AVIF, BMP, MOV, MP4, PDF + 40+ annotation formats |
CLI (roboflow import) |
> 1,000 images (images only) | Same image formats, no video |
| Cloud storage bucket mirror | Extensive or continuously-growing data already in S3 / GCS | See roboflow://skills/roboflow-cloud-storage/SKILL |
| Dataset Upload Workflow Block | Collecting from production Workflows | Programmatic |
| Universe fork | Starting from a public dataset | Any Universe dataset |
Limits: Max 20 MB per image, max 16,400 x 10,900 px. Duplicate images are skipped automatically.
Video Upload
Videos are split into frames at a configurable rate (1 frame/60s to 60 fps). Supported formats depend on browser (MP4 H.264 most compatible).
CLI Upload
pip install roboflow
roboflow import -w <workspace> -p <project-id> /path/to/dataset
Tags
Tags are free-form labels on images for organization and filtering.
| Action | How |
|---|---|
| Add during upload | Tag selector in upload dialog or via API |
| Add to existing images | Select images -> "Images Selected" -> "Apply tags" |
| Rename/delete in bulk | Project Settings -> Tags -> "Modify Tags" |
| Filter by tag | Search with tag:<name> or use Assign page filter |
| Use in versions | "Filter by Tag" preprocessing step (require/exclude/allow) |
Dataset Search (RoboQL)
Search images via the Images page search bar. Combine filters with boolean logic.
Filters
| Filter | Example | Description |
|---|---|---|
| (free text) | person on sidewalk |
Semantic search (CLIP-based) |
like-image:<ID> |
like-image:abc123 |
Find visually similar images |
filename: |
filename:*factory* |
Filename match (* for partial) |
tag: |
tag:factory |
Filter by tag |
split: |
split:train |
Filter by split |
job: |
job:<JOB_ID> |
Filter by annotation job |
class: |
class:helmet |
Has annotation with class |
metadata: |
metadata:key=value |
Filter by user metadata |
project: |
project:my-project |
Filter by project (workspace search) |
sort: |
sort:updated |
Sort results |
min-width: / max-width: |
min-width:1000 |
Image dimension filters |
min-height: / max-height: |
max-height:800 |
Image dimension filters |
min-annotations: / max-annotations: |
max-annotations:1 |
Annotation count filters |
Boolean Logic
AND,OR,NOT, parentheses:class:helmet AND NOT (tag:v1 OR tag:v2)- Inverted filter with
-:-class:vest - Comparison operators on numeric filters:
>,<,>=,<=,=(e.g.,class:helmet>=3)
Splits (Train / Valid / Test)
Images are assigned to train, valid, or test splits. Splits are rebalanced during version generation (Step 2 in version creation). Augmentations only apply to train split.
Dataset Versions
A version is a frozen snapshot of the dataset at a point in time. Changes to the project after version creation do not affect existing versions.
Version Creation Pipeline
- Source selection — images from the dataset split
- Train/Test split — rebalance percentages
- Preprocessing — applied to all splits (train + valid + test)
- Augmentation — applied only to train split
- Generate — creates immutable version
Preprocessing Options
| Step | Effect |
|---|---|
| Auto-Orient | Strips EXIF, normalizes orientation |
| Resize | Stretch to / Fit within / Fit (black edges) / Fit (white edges) |
| Grayscale | Convert RGB to single channel |
| Auto-Adjust Contrast | Contrast Stretching / Histogram Equalization / Adaptive (CLAHE) |
| Isolate Objects | Crop each bbox into separate image (converts OD to classification) |
| Static Crop | Crop all images to fixed region |
| Tile | Split images into NxN grid (default 2x2, helps small object detection) |
| Dynamic Crop | Crop images around a specific class |
| Modify Classes | Remap/omit classes for this version only |
| Filter Null | Control percentage of unannotated images |
| Filter by Tag | Require / Exclude / Allow images by tag |
| Random Sample | Sample a percentage of images per split |
Augmentation Options
Applied to train images only. Configurable max version size (e.g., 3x = source + 2 augmented copies).
| Augmentation | Image Level | BBox Level | Tier |
|---|---|---|---|
| Flip | yes | yes | Basic |
| 90 deg Rotate | yes | yes | Basic |
| Crop | yes | yes | Basic |
| Rotation | yes | yes | Basic |
| Shear | yes | yes | Basic |
| Grayscale | yes | no | Basic |
| Hue | yes | no | Basic |
| Saturation | yes | no | Basic |
| Brightness | yes | yes | Basic |
| Exposure | yes | yes | Basic |
| Blur | yes | yes | Basic |
| Noise | yes | yes | Basic |
| Camera Gain | yes | yes | Basic |
| Motion Blur | yes | yes | Basic |
| Cutout | yes | no | Enhanced (paid) |
| Mosaic | yes | no | Enhanced (paid) |
Dataset Analytics
Available at project sidebar -> "Analytics". Shows:
- Image count, annotation count, avg image size, median aspect ratio
- Missing and null annotation counts
- Class distribution across train/valid/test
- Image dimension insights (size + aspect ratio distribution)
- Annotation heatmap (click-drag to filter images by region)
- Object count histogram (click bars to see matching images)
Classes
Managed at Project Settings -> Classes.
| Action | Description |
|---|---|
| Rename | Type new name in Override column |
| Merge | Override multiple classes to same name |
| Delete | Check Delete checkbox |
| Lock | "Lock Annotation Classes" prevents new class creation |
Warning: Class changes at project level affect all images (irreversible). Use version-level "Modify Classes" preprocessing for non-destructive changes.
Annotation Groups
Annotation group = the category encompassing all classes in a project. Projects sharing the same annotation group share their class list and annotations.
- Enable during project creation: "Share image annotations with other projects"
- Shared annotations: editing in one project affects all linked projects
- Look for chain-link icon to identify shared images/projects
- Images shared across projects count only once toward usage
Project Folders
Folders group projects for organization. SSO workspaces can restrict folder access to specific team members.
| Action | How |
|---|---|
| Create | "+ New Folder" from workspace view |
| Move project | Project menu -> "Move Project" |
| Delete folder | Folder menu -> "Delete" (projects move to workspace root, not deleted) |
Export Formats
Versions can be exported as .zip download or curl command. 40+ formats supported including COCO, YOLO, Pascal VOC, TFRecord, and more. Full list at roboflow.com/formats.
Export via Python SDK:
project.version(1).download("yolov8")
MCP apps vs plain tools
Prefab MCP apps (create_project_app) exist when parameters are unclear, you need real UX, or a human must confirm after seeing form fields — plain chat/MCP calls should not guess project type and license alone.
MCP Tools Available
| Tool | Purpose |
|---|---|
projects_create |
Create a new project (specify type, annotation group) |
projects_list / projects_get |
List or get project details |
images_search |
Search images using RoboQL filters |
image_upload / image_upload_status |
Prepare zip image upload and poll status |
versions_generate |
Generate a dataset version with preprocessing/augmentation |
versions_get |
Inspect a version |
versions_export |
Export a version in a given format |
Related Pages
roboflow://skills/roboflow-data-management/labeling— annotation tools, AI labeling, Label Assist, Smart Polygon, Auto Label, annotation jobsroboflow://skills/roboflow-cloud-storage/SKILL— mirror an S3/GCS bucket into the workspace (credentials, datasources, glob rules, scheduled sync)