Yolo Bdd Inference

What is Yolo Bdd Inference ?

Yolo Bdd Inference is a tool that allows users to analyze images and videos for object detection. Built on the YOLO (You Only Look Once) object detection framework and leveraging the BDD100K dataset, it specializes in tracking objects within video streams. This tool is particularly useful for applications that require real-time object detection and tracking, such as autonomous vehicles or surveillance systems.

Features

• Real-Time Object Detection: Yolo Bdd Inference processes video and image data in real-time, enabling quick and accurate object detection.
• High Accuracy: Trained on the comprehensive BDD100K dataset, it provides robust detection capabilities for diverse environments.
• Customizable: Users can fine-tune the model to suit specific use cases and improve accuracy.
• Efficient Processing: Optimized for performance, it handles high-resolution videos without compromising speed.

How to use Yolo Bdd Inference ?

1. Install the Yolo Bdd Inference package: Use pip to install the necessary package.

   pip install yolo-bdd-inference
   

2. Prepare Input: Load your image or video file.

3. Run Inference: Apply the detect() function to analyze the input.

   from yolo_bdd_inference import Detector
   detector = Detector()
   results = detector.detect(input_path)
   

4. Interpret Results: Review the returned detections, which include bounding boxes and class labels.

5. Optional - Extend Functionality: Customize the model or integrate with other tools for advanced use cases.

Frequently Asked Questions

What video formats does Yolo Bdd Inference support?
Yolo Bdd Inference supports various formats, including MP4, AVI, and MOV, ensuring versatility in different applications.

Can I train Yolo Bdd Inference on my dataset?
Yes, the tool allows customization. You can fine-tune the model using your dataset for specific object detection needs.

How do I improve detection accuracy?
Improving accuracy may involve data augmentation, increasing model training data, or adjusting detection thresholds to suit your environment.