YOLOv10l
| Property | Value |
|---|---|
| Author | jameslahm |
| Paper | arXiv:2405.14458v1 |
| Repository | GitHub |
What is yolov10l?
YOLOv10l is the latest iteration in the YOLO (You Only Look Once) family of object detection models. This lightweight version represents a significant advancement in real-time object detection, combining efficiency with state-of-the-art performance. Developed by researchers from various institutions, it builds upon the success of previous YOLO versions while introducing new optimizations for real-time applications.
Implementation Details
The model is implemented using the Ultralytics framework and can be easily installed via pip. It supports both training and inference workflows, with seamless integration with the Hugging Face ecosystem for model sharing and deployment.
- Simple installation through pip
- Built-in training and validation capabilities
- Hugging Face Hub integration for model sharing
- Support for various input sources including URLs
Core Capabilities
- Real-time object detection
- End-to-end training pipeline
- Pre-trained model availability
- Flexible deployment options
- Support for custom dataset training
Frequently Asked Questions
Q: What makes this model unique?
YOLOv10l represents the latest advancement in the YOLO architecture, focusing on real-time performance while maintaining high detection accuracy. Its lightweight nature makes it particularly suitable for applications with limited computational resources.
Q: What are the recommended use cases?
The model is ideal for real-time object detection tasks across various domains, including surveillance, autonomous systems, and computer vision applications where speed and accuracy are crucial. It's particularly well-suited for scenarios requiring deployment on edge devices or systems with computational constraints.
