مرجع REST API

Authorization: Bearer ul_your_api_key_here

curl -H "Authorization: Bearer ul_abc123..." \
  https://platform.ultralytics.com/api/datasets

https://platform.ultralytics.com/api

X-RateLimit-Limit: 60
X-RateLimit-Remaining: 55
X-RateLimit-Reset: 1640000000

{
  "success": true,
  "data": { ... },
  "meta": {
    "page": 1,
    "limit": 20,
    "total": 100
  }
}

{
  "success": false,
  "error": {
    "code": "VALIDATION_ERROR",
    "message": "Invalid dataset ID",
    "details": { ... }
  }
}

GET /api/datasets

{
    "success": true,
    "data": [
        {
            "id": "dataset_abc123",
            "name": "my-dataset",
            "slug": "my-dataset",
            "task": "detect",
            "imageCount": 1000,
            "classCount": 10,
            "visibility": "private",
            "createdAt": "2024-01-15T10:00:00Z"
        }
    ]
}

GET /api/datasets/{datasetId}

POST /api/datasets

{
    "name": "my-dataset",
    "task": "detect",
    "description": "A custom detection dataset"
}

DELETE /api/datasets/{datasetId}

POST /api/datasets/{datasetId}/export

GET /api/datasets/{datasetId}/models

{
    "success": true,
    "data": [
        {
            "id": "model_abc123",
            "name": "experiment-1",
            "projectId": "project_xyz",
            "trainedAt": "2024-01-15T10:00:00Z",
            "metrics": {
                "mAP50": 0.85,
                "mAP50-95": 0.72
            }
        }
    ]
}

GET /api/projects

GET /api/projects/{projectId}

POST /api/projects

{
    "name": "my-project",
    "description": "Detection experiments"
}

DELETE /api/projects/{projectId}

GET /api/models

GET /api/models/{modelId}

POST /api/models

DELETE /api/models/{modelId}

GET /api/models/{modelId}/files

POST /api/models/{modelId}/predict

{
    "success": true,
    "predictions": [
        {
            "class": "person",
            "confidence": 0.92,
            "box": { "x1": 100, "y1": 50, "x2": 300, "y2": 400 }
        }
    ]
}

POST /api/training/start

{
    "modelId": "model_abc123",
    "datasetId": "dataset_xyz789",
    "epochs": 100,
    "imageSize": 640,
    "gpuType": "rtx-4090"
}

GET /api/models/{modelId}/training

DELETE /api/models/{modelId}/training

GET /api/deployments

POST /api/deployments

{
    "modelId": "model_abc123",
    "region": "us-central1",
    "minInstances": 0,
    "maxInstances": 10
}

GET /api/deployments/{deploymentId}

POST /api/deployments/{deploymentId}/start

POST /api/deployments/{deploymentId}/stop

DELETE /api/deployments/{deploymentId}

GET /api/deployments/{deploymentId}/metrics

GET /api/deployments/{deploymentId}/logs

GET /api/exports

POST /api/exports

{
    "modelId": "model_abc123",
    "format": "onnx"
}

GET /api/exports/{exportId}

GET /api/activity

POST /api/activity/mark-seen

POST /api/activity/archive

GET /api/trash

POST /api/trash

{
    "itemId": "item_abc123",
    "type": "dataset"
}

POST /api/trash/empty

GET /api/billing/balance

{
    "success": true,
    "data": {
        "cashBalance": 5000000,
        "creditBalance": 20000000,
        "reservedAmount": 0,
        "totalBalance": 25000000
    }
}

GET /api/billing/usage-summary

POST /api/billing/checkout-session

{
    "amount": 25
}

POST /api/billing/subscription-checkout

POST /api/billing/portal-session

GET /api/billing/payments

GET /api/storage

{
    "success": true,
    "data": {
        "used": 1073741824,
        "limit": 107374182400,
        "percentage": 1.0
    }
}

POST /api/gdpr

{
    "action": "export"
}

GET /api/api-keys

POST /api/api-keys

{
    "name": "training-server",
    "scopes": ["training", "models"]
}

DELETE /api/api-keys/{keyId}

pip install ultralytics

yolo check

yolo settings api_key=YOUR_API_KEY

export ULTRALYTICS_API_KEY=YOUR_API_KEY

from ultralytics import settings

settings.api_key = "YOUR_API_KEY"

from ultralytics import YOLO

model = YOLO("yolo11n.pt")

# Train on your Platform dataset
model.train(
    data="ul://your-username/your-dataset",
    epochs=100,
    imgsz=640,
)

ul://{username}/{resource-type}/{name}

Examples:
ul://john/datasets/coco-custom     # Dataset
ul://john/my-project               # Project
ul://john/my-project/exp-1         # Specific model
ul://ultralytics/yolo26/yolo26n    # Official model

from ultralytics import YOLO

model = YOLO("yolo11n.pt")

# Results automatically sync to Platform
model.train(
    data="coco8.yaml",
    epochs=100,
    project="ul://your-username/my-project",
    name="experiment-1",
)

# Your own model
model = YOLO("ul://username/project/model-name")

# Official model
model = YOLO("ul://ultralytics/yolo26/yolo26n")

results = model("image.jpg")

# Access results
for r in results:
    boxes = r.boxes  # Detection boxes
    masks = r.masks  # Segmentation masks
    keypoints = r.keypoints  # Pose keypoints
    probs = r.probs  # Classification probabilities

# Export to ONNX
model.export(format="onnx", imgsz=640, half=True)

# Export to TensorRT
model.export(format="engine", imgsz=640, half=True)

# Export to CoreML
model.export(format="coreml", imgsz=640)

metrics = model.val(data="ul://username/my-dataset")

print(f"mAP50: {metrics.box.map50}")
print(f"mAP50-95: {metrics.box.map}")

GET /api/datasets?page=2 &
limit=50

import time


def api_request_with_retry(url, max_retries=3):
    for attempt in range(max_retries):
        response = requests.get(url)
        if response.status_code != 429:
            return response
        wait = 2**attempt
        time.sleep(wait)
    raise Exception("Rate limit exceeded")