Object Detection in Images using YOLO

Introduction

Object Detection Model Using YOLO for detecting objects in images.

• Model Variants:
  • YOLOv-N: Nano version for extremely resource-constrained environments.
  • YOLOv-S: Small version balancing speed and accuracy.
  • YOLOv-M: Medium version for general-purpose use.
  • YOLOv-B: Balanced version with increased width for higher accuracy.
  • YOLOv-L: Large version for higher accuracy at the cost of increased computational resources.
  • YOLOv-X: Extra-large version for maximum accuracy and performance.

Demonstration Video

Object.Detection.mp4

Pretrained Models

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.

Usage

Use the following command to run the model:

1. Run using Google Colab button.
2. Run using Streamlit button.
3. Clone the repository and run the following command:
4. pip install -r requirements.txt
5. After installing the required libraries, run the following command:

streamlit run app.py

References

YOLOv10 YOLO11