Yolo V8 Plant Seedling Classification

🚀 uisikdag/weed_yolov8_balanced

This is an image classification model based on YOLOv8, which can accurately classify various types of weeds.

🚀 Quick Start

Installation

To use this model, you need to install the ultralyticsplus library. You can install it using the following command:

pip install ultralyticsplus==0.0.28 ultralytics==8.0.43

Usage

Here is a simple example of how to load the model and perform predictions:

from ultralyticsplus import YOLO, postprocess_classify_output

# load model
model = YOLO('uisikdag/weed_yolov8_balanced')

# set model parameters
model.overrides['conf'] = 0.25  # model confidence threshold

# set image
image = 'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg'

# perform inference
results = model.predict(image)

# observe results
print(results[0].probs) # [0.1, 0.2, 0.3, 0.4]
processed_result = postprocess_classify_output(model, result=results[0])
print(processed_result) # {"cat": 0.4, "dog": 0.6}

✨ Features

• High Accuracy: The model achieves a top1 accuracy of 0.9 and a top5 accuracy of 1.0.
• Easy to Use: With the ultralyticsplus library, you can quickly load the model and perform predictions.

📚 Documentation

Supported Labels

The model supports the following labels:

['Black-grass', 'Charlock', 'Cleavers', 'Common Chickweed', 'Common wheat', 'Fat Hen', 'Loose Silky-bent', 'Maize', 'Scentless Mayweed', 'Shepherds Purse', 'Small-flowered Cranesbill', 'Sugar beet']

Model Metrics