Object Detection Model Using YOLOv10 for detecting objects in images.
- Model Variants:
- YOLOv10-N: Nano version for extremely resource-constrained environments.
- YOLOv10-S: Small version balancing speed and accuracy.
- YOLOv10-M: Medium version for general-purpose use.
- YOLOv10-B: Balanced version with increased width for higher accuracy.
- YOLOv10-L: Large version for higher accuracy at the cost of increased computational resources.
- YOLOv10-X: Extra-large version for maximum accuracy and performance.
Object.Detection.mp4
YOLOv10 outperforms previous YOLO versions and other state-of-the-art models in terms of accuracy and efficiency. For example, YOLOv10-S is 1.8x faster than RT-DETR-R18 with similar AP on the COCO dataset, and YOLOv10-B has 46% less latency and 25% fewer parameters than YOLOv9-C with the same performance.
| Model | Input Size | APval | FLOPs (G) | Latency (ms) |
|---|---|---|---|---|
| YOLOv10-N | 640 | 38.5 | 6.7 | 1.84 |
| YOLOv10-S | 640 | 46.3 | 21.6 | 2.49 |
| YOLOv10-M | 640 | 51.1 | 59.1 | 4.74 |
| YOLOv10-B | 640 | 52.5 | 92.0 | 5.74 |
| YOLOv10-L | 640 | 53.2 | 120.3 | 7.28 |
| YOLOv10-X | 640 | 54.4 | 160.4 | 10.70 |
- Latency measured with TensorRT FP16 on T4 GPU.
Use the following command to run the model:
- Run using Google Colab button.
- Run using Streamlit button.
- Clone the repository and run the following command:
pip install -r requirements.txt- After installing the required libraries, run the following command:
streamlit run app.py