roboflow-universe

For agents — source-of-truth: This skill is authored in roboflow/computer-vision-skills and shipped with the Roboflow plugin. If your client has loaded the plugin (you'll see roboflow:<name> skills in your available skills list), use those local skills — they're read fresh from disk every session. The same content served as MCP resources at roboflow://skills/<name>/... is a fallback for clients without the plugin and may lag this repo. Don't call ReadMcpResourceTool for roboflow://skills/... URIs when a local roboflow:<name> skill is available.

Roboflow Universe

Open repository of 1M+ computer vision datasets and 50K+ pre-trained models at universe.roboflow.com.

URL Patterns

Page	URL	Content
Home	universe.roboflow.com	Search, trending projects, categories
Project	universe.roboflow.com/{owner}/{project}	Overview, classes, metrics, license, fork
Images	universe.roboflow.com/{owner}/{project}/browse	Browse images with annotations
Dataset version	universe.roboflow.com/{owner}/{project}/dataset/{version}	Version details, splits, download
Model	universe.roboflow.com/{owner}/{project}/model/{version}	Try model, metrics, deploy snippet

Searching Universe

MCP app (universe_search_app)

Use when someone must choose a dataset after seeing it: previews, classes, license, image counts, etc. Pure MCP JSON hits from universe_search are not a substitute for that UX — open the app when the decision needs eyes on the listings.

MCP Tool

Use universe_search to find datasets/models programmatically. Pass a descriptive query (e.g. "hard hat detection construction site").

Web Search

Search is hybrid — combines semantic similarity with keyword matching. Use specific, descriptive queries for best results.

Query Operators

All operators can be mixed with free-text: fire smoke class:fire,smoke images>200 model

Operator	Example	Effect
model	waste detection model	Only datasets with a trained model
object detection	helmet object detection	Filter by project type (also: classification, instance segmentation, keypoint detection)
class:X	class:helmet,person	Must contain these classes
tag:X	tag:safety	Filter by Universe tag
model:X	model:yolov8	Filter by trained model architecture
images>N	images>500	Min image count (also >=, <, <=, =)
stars>=N	stars>=5	Min star count
views>N	views>1000	Min view count
downloads>N	downloads>100	Min download count
updated:Nd	updated:30d	Updated within N days (also h, w, mo, y)
sort:X	sort:stars	Sort by field (stars, images, updated, downloads, views)
like:dataset-url	like:coco	Find similar datasets

Tips for Effective Queries

• Combine free-text with operators: pothole road damage class:pothole images>100 sort:stars
• Add model to only get inference-ready datasets
• Include project type keywords to filter: helmet instance segmentation
• Use class: when you know exactly what classes you need
• Use specific object names, not generic terms ("forklift in warehouse" > "vehicle")

Evaluating a Dataset

Before forking, check these signals:

Criterion	Where to Look	Good Sign
Class coverage	Classes list on project page	All your target classes present
Image count	Project overview	500+ images per class for detection
Annotation quality	Browse > click individual images	Tight bounding boxes, consistent labels
Class balance	Project overview / health	No extreme class imbalance
Image diversity	Browse images	Varied lighting, angles, backgrounds
License	"Cite this Project" section	Compatible with your use case (see below)
Model metrics	Model tab (if available)	mAP > 70% suggests decent annotations

Licenses

Found in the "Cite this Project" section on the project page. No license listed = all rights reserved.

License	Commercial Use	Modify	Attribution Required
Public Domain	Yes	Yes	No
CC BY 4.0	Yes	Yes	Yes
MIT	Yes	Yes	Yes (in license copy)
BY-NC-SA 4.0	No	Yes (share-alike)	Yes
ODbL v1.0	Yes	Yes (share-alike for DB)	Yes
No license specified	Assume No	Assume No	N/A

Forking a Dataset

Fork = copy a Universe dataset into your workspace (no download/re-upload needed).

1. Open dataset on Universe
2. Click Download Dataset button
3. Choose Train a model with this dataset (full fork) or Train from a portion of this dataset (partial clone)
4. Dataset copies into your workspace

After forking you can: rename classes, add/remove images, generate versions, train models.

Requires: Logged-in Roboflow account.

Downloading a Dataset

For local/notebook training instead of Roboflow cloud training.

Method	When to Use
Train a model with this dataset (fork)	Training on Roboflow, want full data in workspace
Train from a portion (clone)	Want a subset or to combine with other data
Download dataset	Local training via code snippet or ZIP file

Supports all standard export formats (COCO, YOLO, VOC, CreateML, TFRecord, etc.).

Path: Project page > Download Dataset button > choose method.

Using a Universe Model

Direct Inference via Workflows

1. Create a Workflow in Roboflow
2. Add a model block
3. Switch to Public Models tab
4. Paste the model ID from the Universe model page (copy icon at top)
5. Click Use model ID

Model ID format: {owner}/{project}/{version} (shown on Universe model page).

Checkpoint Training

Fork the dataset, then train your own model using the forked data. Use a Universe model's architecture as a starting point via Roboflow Train.

Inference Metrics (shown on model page)

Project Type	Metrics Shown
Object Detection	mAP, precision, recall
Classification	Accuracy
Segmentation	mAP, precision, recall

MCP Tool Reference

universe_search — Search Universe for datasets/models.

Param	Type	Default	Notes
query	str (required)	—	Search query text
result_type	"dataset" | "model" | null	null	Filter by result type
limit	int	12	Max results per page
page	int	1	Page number (1-indexed)

Returns: name, url, type, classes, classCount, images, description, tags, license, stars, views, downloads, modelCount, latestVersion.

Related Skills

• roboflow://skills/roboflow-data-management/SKILL — managing datasets after import
• roboflow://skills/roboflow-training-and-evaluation/SKILL — training on forked data