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---
comments: true
description: Learn how to test YOLO models with the Ultralytics Platform inference API including browser testing and programmatic access.
keywords: Ultralytics Platform, inference, API, YOLO, object detection, prediction, testing
---
# Inference
[Ultralytics Platform](https://platform.ultralytics.com) provides an inference API for testing trained models. Use the browser-based `Predict` tab for quick validation or the [REST API](../api/index.md#models-api) for programmatic access.
![Ultralytics Platform Model Predict Tab With Detections Overlay](https://cdn.jsdelivr.net/gh/ultralytics/assets@main/docs/platform/model-predict-tab-with-detections-overlay.avif)
## 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. View predictions instantly with bounding box overlays
![Ultralytics Platform Predict Tab Image Upload Dropzone](https://cdn.jsdelivr.net/gh/ultralytics/assets@main/docs/platform/predict-tab-image-upload-dropzone.avif)
### Input Methods
The predict panel supports multiple input methods:
| Method | Description |
| ------------------ | ---------------------------------------------------- |
| **Image upload** | Drag and drop or click to upload an image |
| **Example images** | Click built-in examples (dataset images or defaults) |
| **Webcam capture** | Live camera feed with single-frame capture |
```mermaid
graph LR
A[Upload Image] --> D[Auto-Inference]
B[Example Image] --> D
C[Webcam Capture] --> D
D --> E[Results + Overlays]
style D fill:#2196F3,color:#fff
style E fill:#4CAF50,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
!!! info "Auto-Inference"
The predict panel runs inference automatically when you upload an image, select an example, or capture a webcam frame. No button click is needed.
### Example Images
The predict panel shows example images from your model's linked dataset. If no dataset is linked, default examples are used:
| Image | Content |
| ------------ | -------------------------- |
| `bus.jpg` | Street scene with vehicles |
| `zidane.jpg` | Sports scene with people |
For OBB models, aerial images of boats and airports are shown instead.
!!! tip "Preloaded Images"
Example images are preloaded when the page loads, so clicking an example triggers near-instant inference with no download wait.
### 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:
- **Bounding boxes** with class labels as SVG overlays
- **Confidence scores** for each detection
- **Class colors** from your dataset's color palette (or the Ultralytics default palette)
- **Speed breakdown**: Preprocess, inference, postprocess, and network time
![Ultralytics Platform Predict Tab Results With Detections And Speed Stats](https://cdn.jsdelivr.net/gh/ultralytics/assets@main/docs/platform/predict-tab-results-with-detections-and-speed-stats.avif)
The results panel shows:
| Field | Description |
| ------------------- | ------------------------------------------------ |
| **Detections list** | Each detection with class name and confidence |
| **Speed stats** | Preprocess, inference, postprocess, network (ms) |
| **JSON response** | Raw API response in a code block |
## Inference Parameters
Adjust detection behavior with parameters in the collapsible **Parameters** section:
![Ultralytics Platform Predict Tab Parameters Sliders](https://cdn.jsdelivr.net/gh/ultralytics/assets@main/docs/platform/predict-tab-parameters-sliders.avif)
| Parameter | Range | Default | Description |
| -------------- | -------------- | ------- | -------------------------------------- |
| **Confidence** | 0.01-1.0 | 0.25 | Minimum confidence threshold |
| **IoU** | 0.0-0.95 | 0.70 | NMS IoU threshold |
| **Image Size** | 320, 640, 1280 | 640 | Input resize dimension (button toggle) |
!!! note "Auto-Rerun"
Changing any parameter automatically re-runs inference on the current image with a 500ms debounce. No need to re-upload.
### 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)**: Merge nearby detections more aggressively
- **Default (0.70)**: Balanced NMS behavior for most use cases
## Deployment Predict
Each running [dedicated endpoint](endpoints.md) 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.
## REST API
Access inference programmatically:
### Authentication
Include your API key in requests:
```bash
Authorization: Bearer YOUR_API_KEY
```
!!! warning "API Key Required"
To run inference from your own scripts, notebooks, or apps, include an API key. Generate one in [`Settings`](../account/api-keys.md) (API Keys section on the Profile tab).
### Endpoint
```
POST https://platform.ultralytics.com/api/models/{modelId}/predict
```
### Request
=== "Python"
```python
import requests
url = "https://platform.ultralytics.com/api/models/MODEL_ID/predict"
headers = {"Authorization": "Bearer YOUR_API_KEY"}
files = {"file": open("image.jpg", "rb")}
data = {"conf": 0.25, "iou": 0.7, "imgsz": 640}
response = requests.post(url, headers=headers, files=files, data=data)
print(response.json())
```
=== "cURL"
```bash
curl -X POST \
"https://platform.ultralytics.com/api/models/MODEL_ID/predict" \
-H "Authorization: Bearer YOUR_API_KEY" \
-F "file=@image.jpg" \
-F "conf=0.25" \
-F "iou=0.7" \
-F "imgsz=640"
```
=== "JavaScript"
```javascript
const formData = new FormData();
formData.append("file", fileInput.files[0]);
formData.append("conf", "0.25");
formData.append("iou", "0.7");
formData.append("imgsz", "640");
const response = await fetch(
"https://platform.ultralytics.com/api/models/MODEL_ID/predict",
{
method: "POST",
headers: { Authorization: "Bearer YOUR_API_KEY" },
body: formData,
}
);
const result = await response.json();
console.log(result);
```
![Ultralytics Platform Predict Tab Code Examples Python Tab](https://cdn.jsdelivr.net/gh/ultralytics/assets@main/docs/platform/predict-tab-code-examples-python-tab.avif)
### Response
```json
{
"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,
"model": "model.pt",
"version": {
"ultralytics": "8.4.14",
"torch": "2.6.0",
"torchvision": "0.21.0",
"python": "3.13.0"
}
}
}
```
![Ultralytics Platform Predict Tab Json Response View](https://cdn.jsdelivr.net/gh/ultralytics/assets@main/docs/platform/predict-tab-json-response-view.avif)
### Response Fields
| Field | Type | Description |
| ------------------------------- | ------ | --------------------------------- |
| `images` | array | List of processed images |
| `images[].shape` | array | Image dimensions [height, width] |
| `images[].results` | array | List of detections |
| `images[].results[].name` | string | Class name |
| `images[].results[].confidence` | float | Detection confidence (0-1) |
| `images[].results[].box` | object | Bounding box coordinates |
| `images[].speed` | object | Processing times in milliseconds |
| `metadata` | object | Request metadata and version info |
### Task-Specific Responses
Response format varies by task:
=== "Detection"
```json
{
"class": 0,
"name": "person",
"confidence": 0.92,
"box": {"x1": 100, "y1": 50, "x2": 300, "y2": 400}
}
```
=== "Segmentation"
```json
{
"class": 0,
"name": "person",
"confidence": 0.92,
"box": {"x1": 100, "y1": 50, "x2": 300, "y2": 400},
"segments": [[100, 50], [150, 60], ...]
}
```
=== "Pose"
```json
{
"class": 0,
"name": "person",
"confidence": 0.92,
"box": {"x1": 100, "y1": 50, "x2": 300, "y2": 400},
"keypoints": [
{"x": 200, "y": 75, "conf": 0.95},
...
]
}
```
=== "Classification"
```json
{
"results": [
{"class": 0, "name": "cat", "confidence": 0.95},
{"class": 1, "name": "dog", "confidence": 0.03}
]
}
```
=== "OBB"
```json
{
"class": 0,
"name": "ship",
"confidence": 0.89,
"box": {"x1": 100, "y1": 50, "x2": 300, "y2": 400},
"obb": {"x1": 105, "y1": 48, "x2": 295, "y2": 55, "x3": 290, "y3": 395, "x4": 110, "y4": 402}
}
```
## Rate Limits
Shared inference is rate-limited to **20 requests/min per API key**. When throttled, the API returns `429` with a `Retry-After` header. See the full [rate limit reference](../api/index.md#rate-limits) for all endpoint categories.
!!! tip "Need More Throughput?"
Deploy a [dedicated endpoint](endpoints.md) for **unlimited** inference with no rate limits, predictable throughput, and consistent low-latency responses. For local inference, see the [Predict mode guide](../../modes/predict.md).
## Error Handling
Common error responses:
| Code | Message | Solution |
| ---- | --------------- | ------------------------------------------------------------------------------------ |
| 400 | Invalid image | Check file format |
| 401 | Unauthorized | Verify API key |
| 404 | Model not found | Check model ID |
| 429 | Rate limited | Wait and retry, or use a [dedicated endpoint](endpoints.md) for unlimited throughput |
| 500 | Server error | Retry request |
## FAQ
### Can I run inference on video?
The API accepts individual frames. For video:
1. Extract frames locally
2. Send each frame to the API
3. Aggregate results
For real-time video, consider deploying a [dedicated endpoint](endpoints.md).
### How do I get the annotated image?
The API returns JSON predictions. To visualize:
1. Use predictions to draw boxes locally
2. Use Ultralytics `plot()` method:
```python
from ultralytics import YOLO
model = YOLO("yolo26n.pt")
results = model("image.jpg")
results[0].save("annotated.jpg")
```
See the [Predict mode documentation](../../modes/predict.md) for the full results API and visualization options.
### What's the maximum image size?
- **Upload limit**: 10MB
- **Recommended**: <5MB for fast inference
- **Auto-resize**: Images are resized to the selected `Image Size` parameter
Large images are automatically resized while preserving aspect ratio.
### Can I run batch inference?
The current API processes one image per request. For batch:
1. Send concurrent requests
2. Use a dedicated endpoint for higher throughput
3. Consider local inference for large batches
!!! example "Batch Inference with Python"
```python
import concurrent.futures
import requests
url = "https://predict-abc123.run.app/predict"
headers = {"Authorization": "Bearer YOUR_API_KEY"}
images = ["img1.jpg", "img2.jpg", "img3.jpg"]
def predict(image_path):
with open(image_path, "rb") as f:
return requests.post(url, headers=headers, files={"file": f}).json()
with concurrent.futures.ThreadPoolExecutor(max_workers=4) as executor:
results = list(executor.map(predict, images))
```