| Property | Value |
|---|---|
| Parameter Count | 16.6M |
| License | AGPL-3.0 |
| Tensor Type | F32 |
| Paper | arXiv:2405.14458v1 |
| Downloads | 5,711 |
What is yolov10m?
YOLOv10m is a state-of-the-art object detection model that represents the latest evolution in the YOLO (You Only Look Once) family. Developed by researchers from prestigious institutions, this model offers real-time end-to-end object detection capabilities while maintaining high accuracy and efficiency.
Implementation Details
The model is implemented using PyTorch and can be easily integrated using the ultralytics framework. It features a modern architecture optimized for both speed and accuracy, trained on the COCO dataset.
- Efficient architecture with only 16.6M parameters
- Support for both training and inference workflows
- Compatible with the Hugging Face model hub
- Built-in support for model fine-tuning and validation
Core Capabilities
- Real-time object detection
- End-to-end training pipeline
- Easy model deployment and inference
- Integration with popular deep learning frameworks
Frequently Asked Questions
Q: What makes this model unique?
YOLOv10m combines state-of-the-art performance with a relatively small parameter count (16.6M), making it ideal for real-world applications where both speed and accuracy are crucial.
Q: What are the recommended use cases?
This model is particularly well-suited for real-time object detection tasks in various domains, including surveillance, autonomous systems, and computer vision applications requiring quick and accurate object detection.