Roboflow Instant - Object Detection¶
Roboflow Instant is a proprietary object detection model trained on the Roboflow platform. It's a specialized model designed to work seamlessly with your Roboflow account using inference and inference-models.
Overview¶
Roboflow Instant for object detection features:
- Roboflow-exclusive - Proprietary model trained on the Roboflow platform
- Almost no labeling required - Quickly prototype the model using Roboflow platform
- Seamless integration - Works directly with your Roboflow account
Pre-trained Model IDs¶
Roboflow Instant models are accessed through your Roboflow workspace. Model IDs follow a special format specific to Roboflow Instant (not the standard project-url/version format).
Supported Backends¶
| Backend | Extras Required |
|---|---|
hugging-face |
Included in default installation |
Roboflow Platform Compatibility¶
| Feature | Supported |
|---|---|
| Training | ✅ Train on Roboflow (proprietary model) |
| Upload Weights | ❌ Not applicable (Roboflow-exclusive) |
| Serverless API (v2) | ✅ Deploy via hosted API |
| Workflows | ✅ Use in Workflows via Object Detection block |
| Edge Deployment (Jetson) | ✅ Deploy on NVIDIA Jetson devices |
| Self-Hosting | ✅ Deploy with inference-models |
Note: Model ID structure differs from standard Roboflow models.
Installation¶
Roboflow Instant support is included with the default inference-models installation. No additional packages required.
Usage Example¶
import cv2
import supervision as sv
from inference_models import AutoModel
# Load model and image
# Replace with your actual Roboflow Instant model ID
model = AutoModel.from_pretrained("your-roboflow-instant-model-id", api_key="your_roboflow_api_key")
image = cv2.imread("path/to/image.jpg")
# Run inference
predictions = model(image)
detections = predictions[0].to_supervision()
# Annotate image
bounding_box_annotator = sv.BoxAnnotator()
annotated_image = bounding_box_annotator.annotate(image, detections)
# Save or display
cv2.imwrite("annotated.jpg", annotated_image)