metadata
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. 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
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)