Sentiment Analysis with Transformers and Object Detection with YOLOv8

Introduction

In this blog, we will explore two powerful AI applications:

1. Object Detection using YOLOv8 – A state-of-the-art model for detecting objects in images.

2. Sentiment Analysis using Transformers – A natural language processing (NLP) technique to analyze the sentiment of text.

Let's dive into the implementation and the AI models used for these tasks.

1. Object Detection with YOLOv8

What is YOLOv8?

YOLO (You Only Look Once) is a real-time object detection model developed by Ultralytics. YOLOv8 is the latest iteration, offering improved accuracy and efficiency.

Code Implementation

from ultralytics import YOLO

yolo= YOLO('yolov8n.pt') 
results = yolo("Screenshot 2025-04-04 141405.png") 

for result in results:
    result.show()  # Display the results

Explanation

• Loading the Model: YOLO("yolov8n.pt") loads a lightweight, pre-trained YOLOv8 model.

• Running Inference: The model processes the given image and detects objects.

• Displaying Results: Detected objects are shown with bounding boxes.

AI Model Used

• Model: YOLOv8 Nano (yolov8n.pt)

• Framework: Ultralytics YOLO library

• Task: Object detection

• AI Used: Deep Learning (CNN-based architecture)

Example Output

Other YOLOv8 Variants

Model	Description
yolov8n.pt	Nano - Fastest, least accurate
yolov8s.pt	Small - Moderate speed and accuracy
yolov8m.pt	Medium - Balanced performance
yolov8l.pt	Large - High accuracy, slower
yolov8x.pt	Extra Large - Most accurate, slowest

2. Sentiment Analysis with Transformers

What is Sentiment Analysis?

Sentiment analysis is a Natural Language Processing (NLP) technique that determines the emotional tone of a given text.

Code Implementation

from transformers import pipeline

sentiment_pipeline = pipeline("sentiment-analysis")

text = "I love using transformers for NLP tasks!"

result = sentiment_pipeline(text)
print(result) 

Explanation

• Loading the Pipeline: pipeline("sentiment-analysis") loads a pre-trained sentiment analysis model.

• Input Text: The given text, "I love using transformers for NLP tasks!", is analyzed.

• Output: The sentiment (Positive/Negative) along with confidence score is printed.

AI Model Used

• Model: distilbert-base-uncased-finetuned-sst-2-english

• Framework: transformers (by Hugging Face)

• Task: Sentiment analysis

• AI Used: Transformer-based NLP Model (BERT variant)

Example Output

This indicates that the text expresses positive sentiment with a high confidence score.

Alternative Models for Sentiment Analysis

Model	Description
bert-base-uncased	General-purpose language model
roberta-large-mnli	Multi-task NLP model
distilbert-base-uncased-finetuned-sst-2-english	Optimized for sentiment analysis

Conclusion

Both YOLOv8 and Transformer-based Sentiment Analysis showcase the power of AI in different domains:

• YOLOv8 is ideal for real-time object detection in images.

• Sentiment analysis with transformers enables understanding emotions in text.

These models can be easily integrated into various applications, from security systems to customer feedback analysis.

To understand the code watch the below video