Inference#

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

Predict Tab#

Every model 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

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, DNG, MPO
• Max size: 10MB
• Auto-inference: Results appear automatically after upload

Example Images#

The predict panel shows example images from your model's linked dataset. If no dataset is linked, default examples are used:

For OBB models, aerial images of boats and airports 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:

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. The examples use placeholders until you deploy the model. After deployment, the deployment card's Code tab fills in its endpoint URL and the API key available to your workspace.

Authentication#

Include your API key in requests:

Authorization: Bearer YOUR_API_KEY

Endpoint#

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

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,
        "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#