---
license: mit
datasets:
- duality-robotics/YOLOv8-Multiclass-Object-Detection-Dataset
- beethogedeon/Trucks-Detection-Yolov8
language:
- en
metrics:
- accuracy
base_model:
- varunm2004/yolov8x.pt
pipeline_tag: object-detection
tags:
- object-detection
- yolov8
- machine-learning
- deep-learning
- huggingface
library_name: ultralytics
inference: true
---



# YOLOv8x Model for Object Detection

This is a YOLOv8x model trained on the [duality-robotics/YOLOv8-Multiclass-Object-Detection-Dataset](https://huggingface.co/datasets/duality-robotics/YOLOv8-Multiclass-Object-Detection-Dataset). The model is designed to detect multiple classes in images and videos with high accuracy.

## How to Use

You can use this model directly with the Hugging Face Inference API or deploy it to your own infrastructure.

### Example with the Hugging Face Inference API

```python
import requests

API_URL = "https://api-inference.huggingface.co/models/varunm2004/yolov8x"
headers = {"Authorization": f"Bearer YOUR_HUGGINGFACE_TOKEN"}

def query(filename):
    with open(filename, "rb") as f:
        data = f.read()
    response = requests.post(API_URL, headers=headers, files={"file": data})
    return response.json()

output = query("example.jpg")
print(output)