YOLACT - Instance Segmentation¶
YOLACT (You Only Look At CoefficienTs) is a real-time instance segmentation model that introduced a novel approach to generating masks in parallel with object detection.
Overview¶
YOLACT is designed for real-time instance segmentation with a unique architecture. Key features include:
- Parallel mask generation - Generates prototype masks and coefficients simultaneously
- Real-time performance - Fast inference suitable for video applications
- Single-stage architecture - Simplified pipeline compared to two-stage methods
- ResNet backbone - Available in ResNet-50 and ResNet-101 variants
- Proven approach - Well-established model with strong community support
License¶
MIT
Open Source License
YOLACT is licensed under MIT, making it free for both commercial and non-commercial use without restrictions.
Learn more: MIT License
Pre-trained Model IDs¶
YOLACT instance segmentation models must be trained on Roboflow or uploaded as custom weights. There are no pre-trained public model IDs available.
Custom model ID format: project-url/version (e.g., my-project-abc123/2)
Supported Backends¶
| Backend | Extras Required |
|---|---|
onnx |
onnx-cpu, onnx-cu12, onnx-cu118, onnx-jp6-cu126 |
Roboflow Platform Compatibility¶
| Feature | Supported |
|---|---|
| Training | ✅ Train custom models on Roboflow |
| Upload Weights | ✅ Upload pre-trained weights (guide) |
| Serverless API (v2) | ✅ Deploy via hosted API |
| Workflows | ✅ Use in Workflows via Instance Segmentation block |
| Edge Deployment (Jetson) | ✅ Deploy on NVIDIA Jetson devices |
| Self-Hosting | ✅ Deploy with inference-models |
Usage Example¶
import cv2
import supervision as sv
from inference_models import AutoModel
# Load your custom model (requires Roboflow API key)
model = AutoModel.from_pretrained(
"my-project-abc123/2",
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 with masks
mask_annotator = sv.MaskAnnotator()
annotated_image = mask_annotator.annotate(image.copy(), detections)
# Save or display
cv2.imwrite("annotated.jpg", annotated_image)