Yolo V8 Indoor Scene Classification

🚀 uisikdag/indoorsceneclas_v8

This project is an image - classification model based on Ultralytics and YOLOv8, which can classify various indoor scenes.

🚀 Quick Start

Install Dependencies

First, you need to install the required libraries. You can use the following command to install ultralyticsplus and ultralytics:

pip install ultralyticsplus==0.0.23 ultralytics==8.0.21

Load the Model and Make Predictions

After the installation is complete, you can load the model and perform predictions using the following Python code:

from ultralyticsplus import YOLO, postprocess_classify_output

# load model
model = YOLO('uisikdag/indoorsceneclas_v8')

# 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

• Rich Label Support: This model supports a wide range of indoor scene labels, including but not limited to airport_inside, artstudio, auditorium, etc.
• Easy to Use: With the help of ultralyticsplus, you can quickly load the model and perform predictions.

📦 Installation

To use this model, you need to install ultralyticsplus and ultralytics. You can use the following command:

pip install ultralyticsplus==0.0.23 ultralytics==8.0.21

💻 Usage Examples

Basic Usage

from ultralyticsplus import YOLO, postprocess_classify_output

# load model
model = YOLO('uisikdag/indoorsceneclas_v8')

# 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}

📚 Documentation

Supported Labels

The model supports the following labels:

['airport_inside', 'artstudio', 'auditorium', 'bakery', 'bookstore', 'bowling', 'buffet', 'casino', 'children_room', 'church_inside', 'classroom', 'cloister', 'closet', 'clothingstore', 'computerroom', 'concert_hall', 'corridor', 'deli', 'dentaloffice', 'dining_room', 'elevator', 'fastfood_restaurant', 'florist', 'gameroom', 'garage', 'greenhouse', 'grocerystore', 'gym', 'hairsalon', 'hospitalroom', 'inside_bus', 'inside_subway', 'jewelleryshop', 'kindergarden', 'kitchen', 'laboratorywet', 'laundromat', 'library', 'livingroom', 'lobby', 'locker_room', 'mall', 'meeting_room', 'movietheater', 'museum', 'nursery', 'office', 'operating_room', 'pantry', 'poolinside', 'prisoncell', 'restaurant', 'restaurant_kitchen', 'shoeshop', 'stairscase', 'studiomusic', 'subway', 'toystore', 'trainstation', 'tv_studio', 'videostore', 'waitingroom', 'warehouse', 'winecellar']

Dataset

The dataset used in this project can be found here.

Model Metrics

Dataset:
Link