Inference#

Ultralytics Platform provides browser-based inference for testing trained models and dedicated endpoints for programmatic access.

Predict Tab#

Every model with weights includes a Predict tab for browser-based inference:

1. Navigate to your model
2. Click the Predict tab
3. Upload an image, use an example, or open your webcam
4. Review the task-specific overlay, prediction summary, timing, and raw response

Models without weights show an empty state instead — train the model or upload weights first.

Input Methods#

The predict panel supports multiple input methods:

graph LR
    A[Upload Image]:::start --> D[Auto-Inference]:::proc
    B[Example Image]:::start --> D
    C[Webcam Capture]:::start --> D
    D --> E[Results + Overlays]:::out

    classDef start fill:#4CAF50,color:#fff
    classDef proc fill:#2196F3,color:#fff
    classDef out fill:#9C27B0,color:#fff

Upload Image#

Drag and drop or click to upload:

• Supported formats: JPEG, PNG, WebP, AVIF, HEIC, JP2, TIFF, BMP
• Max size: 10 MB
• Auto-inference: Results appear automatically after upload

Example Images#

The predict panel shows up to two example images from your model's linked dataset, preferring the val split, then test, then train. If no dataset is linked, default examples are used:

For OBB models, aerial images of boats and an airport are shown instead.

Webcam#

Click the webcam card to start a live camera feed:

1. Grant camera permission when prompted
2. Click the video preview to capture a frame
3. Inference runs automatically on the captured frame
4. Click again to restart the webcam

View Results#

Inference results display the output appropriate to the model task: boxes, masks, keypoints, oriented boxes, classification scores, semantic coverage, or a depth map. Object results use the dataset class colors when available. The panel also shows preprocess, inference, postprocess, and network timing.

The results panel shows:

Two controls sit over the preview once results are in: click the image to enlarge it with overlays intact, and use the download button to save an annotated JPEG of the current result.

Inference Parameters#

Adjust inference behavior with the three sliders below the image:

Confidence Threshold#

Filter predictions by confidence:

• Higher (0.5+): Fewer, more certain predictions
• Lower (0.1-0.25): More predictions, some noise
• Default (0.25): Balanced for most use cases

IoU Threshold#

Control Non-Maximum Suppression:

• Higher (0.7+): Allow more overlapping boxes
• Lower (0.3-0.5): Suppress overlapping detections more aggressively
• Default (0.7): Balanced NMS behavior for most use cases

Deployment Predict#

Each running dedicated endpoint includes a Predict tab directly on its deployment card. This uses the deployment's own inference service rather than the shared predict service, letting you test your deployed endpoint from the browser.

Dedicated Endpoint API#

The API Docs card in the model Predict tab contains example Python, JavaScript, and cURL requests, pre-filled with the confidence, IoU, and image size currently set on the sliders. The URL and key are placeholders until you deploy the model — a Deploy button next to the code tabs jumps to the model's Deploy tab. After deployment, the deployment card's Code tab fills in that endpoint's URL and, for workspace owners, its bound API key, ready to copy and run.

Authentication#

Include your API key in requests:

Authorization: Bearer YOUR_API_KEY

Endpoint#

Dedicated endpoints take requests on their own URL:

POST https://YOUR_DEPLOYMENT_URL.run.app/predict

Shared inference uses the Platform API with the model's full path:

POST https://platform.ultralytics.com/api/models/{owner}/{project}/{model}/predict

Both accept the same multipart/form-data body and return the same response shape. With the Python SDK, use client.models.predict(owner, project, model, body=...) for shared inference or client.deployments.predict(owner, deployment, body=...) for a dedicated deployment:

from ultralytics_platform import Platform

client = Platform()  # reads ULTRALYTICS_API_KEY
with open("image.jpg", "rb") as f:
    results = client.models.predict("acme-vision", "inspection", "v3", body={"file": f, "conf": 0.25})

Request#

import requests

url = "https://YOUR_DEPLOYMENT_URL.run.app/predict"
headers = {"Authorization": "Bearer YOUR_API_KEY"}
data = {"conf": 0.25, "iou": 0.7, "imgsz": 640}

with open("image.jpg", "rb") as image_file:
    response = requests.post(url, headers=headers, files={"file": image_file}, data=data)
print(response.json())

Request Parameters#

Response#

{
    "images": [
        {
            "shape": [1080, 1920],
            "results": [
                {
                    "class": 0,
                    "name": "person",
                    "confidence": 0.92,
                    "box": { "x1": 100, "y1": 50, "x2": 300, "y2": 400 }
                },
                {
                    "class": 2,
                    "name": "car",
                    "confidence": 0.87,
                    "box": { "x1": 400, "y1": 200, "x2": 600, "y2": 350 }
                }
            ],
            "speed": {
                "preprocess": 1.2,
                "inference": 12.5,
                "postprocess": 2.3
            }
        }
    ],
    "metadata": {
        "imageCount": 1,
        "functionTimeAlive": 1284.51,
        "functionTimeCall": 0.018,
        "task": "detect",
        "version": {
            "ultralytics": "8.x.x",
            "torch": "2.6.0",
            "torchvision": "0.21.0",
            "python": "3.13.0"
        }
    }
}

Response Fields#

Task-Specific Responses#

Response format varies by task:

{
  "class": 0,
  "name": "person",
  "confidence": 0.92,
  "box": {"x1": 100, "y1": 50, "x2": 300, "y2": 400}
}

Rate Limits#

The shared model API is limited to 20 requests/minute for each API key, signed-in caller, or anonymous IP. When throttled, the API returns 429 with a Retry-After header. See the full rate-limit reference for all endpoint categories.

Error Handling#

Common error responses:

FAQ#