Yolov5 object detection and classification in a single script

[humairaneha]

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Question

How can I merge yolov5 object detection and classification in a single script? My task is to first detect the roi of the object using object detection and the use the roi as input to classify the object.How can i do this?

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[github-actions]

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Introducing YOLOv8 🚀

We're excited to announce the launch of our latest state-of-the-art (SOTA) object detection model for 2023 - YOLOv8 🚀!

Designed to be fast, accurate, and easy to use, YOLOv8 is an ideal choice for a wide range of object detection, image segmentation and image classification tasks. With YOLOv8, you'll be able to quickly and accurately detect objects in real-time, streamline your workflows, and achieve new levels of accuracy in your projects.

Check out our YOLOv8 Docs for details and get started with:

pip install ultralytics

[glenn-jocher]

@humairaneha hello! Thanks for reaching out with your question. To merge YOLOv5 object detection and classification into a single script, you can follow these general steps:

1. Use YOLOv5 to detect objects in your image, which will give you the bounding boxes (ROIs) of detected objects.
2. Crop these ROIs from the original image.
3. Input each cropped ROI into your classification model to classify the object within that ROI.

Here's a simplified pseudo-code outline:

import torch
from PIL import Image

# Load YOLOv5 model
model_detection = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True)

# Load your classification model (replace with your model)
model_classification = ... # Your classification model loading logic

# Load image
img = Image.open('path/to/your/image.jpg')

# Inference (object detection)
results = model_detection(img)

# Process detections and classify each ROI
for *xyxy, conf, cls in results.xyxy[0]:
    # Crop ROI from original image
    roi = img.crop((xyxy[0], xyxy[1], xyxy[2], xyxy[3]))
    
    # Classify the ROI (replace with your classification logic)
    classification_result = model_classification(roi) # Your classification logic here

    # Handle your classification result
    # ...

# Optionally display or save image with annotations
results.show()
# results.save('path/to/save/image.jpg')

Make sure to replace the classification model loading and inference logic with your own. Also, ensure that the input size and preprocessing steps for the classification model match the requirements of your model.

For more detailed guidance on using YOLOv5, please refer to the Ultralytics Docs.

Happy coding! 😊🚀

[github-actions]

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