Made up of images of water bottles categorized by their internal liquid levels. The collection provides visual data for training machine learning models to distinguish between different states of container fullness for inventory or industrial monitoring.
Use Cases
- Train a classification model to identify the water level within a bottle using the image and class label
- Develop an automated quality control system for bottling lines using the water level labels to detect underfilled units
- Test the robustness of edge detection algorithms in identifying the meniscus or water line within the bottle images
Strengths
- Image-based dataset specifically labeled for water level classification
- Features multiple classes representing different stages of container fullness
- Provides visual data for training models to recognize liquid levels in transparent bottles