YOLOv10m Object Detection Model
| Property | Value |
|---|---|
| Parameter Count | 16.6M |
| License | AGPL-3.0 |
| Paper | arXiv:2405.14458v1 |
| Tensor Type | F32 |
What is yolov10m?
YOLOv10m is an advanced real-time object detection model that represents the latest evolution in the YOLO (You Only Look Once) family. Developed by researchers from various institutions, it offers state-of-the-art performance while maintaining efficient resource usage with only 16.6M parameters.
Implementation Details
The model is implemented using PyTorch and can be easily installed and integrated into existing workflows. It supports both training and inference modes, with complete COCO dataset compatibility. The model architecture focuses on end-to-end object detection with real-time processing capabilities.
- Simple installation via pip from GitHub repository
- Supports both training and validation workflows
- Includes Hugging Face Hub integration for model sharing
- Uses safetensors format for model weights
Core Capabilities
- Real-time object detection performance
- End-to-end processing pipeline
- COCO dataset compatibility
- Easy model fine-tuning capabilities
- Efficient resource utilization
Frequently Asked Questions
Q: What makes this model unique?
YOLOv10m combines real-time performance with high accuracy while maintaining a relatively small parameter count of 16.6M, making it efficient for deployment in resource-constrained environments.
Q: What are the recommended use cases?
The model is ideal for real-time object detection applications, including surveillance systems, autonomous vehicles, robotics, and any scenario requiring fast and accurate object detection capabilities.
