Liquor-data Computer Vision Project

The objective of this project is to curate a comprehensive image dataset of liquor bottles to facilitate the training of a computer vision model. The trained model can then be utilized for various applications, such as bottle recognition, inventory management, and quality control within the liquor industry.

Enhanced Dataset Details:

Bottle Size Variation:

• The dataset will encompass a variety of liquor bottle sizes to enhance model adaptability.
• Include images of bottles in the following size categories:
  • 50ml
  • 100ml
  • 200ml
  • 375ml
  • 750ml
  • 1L
  • 1.75L

Annotation for Bottle Size:

• Annotate each image with additional information specifying the size category of the liquor bottle.
• Use categorical labels corresponding to the different bottle sizes during annotation.

Class Balance:

• Strive for a balanced representation of each bottle size across the dataset to prevent biases in model training.
• Adjust the distribution within the training, validation, and test sets accordingly.

Training Adjustments for Size Recognition:

• Modify the model architecture to include size recognition as an additional classification task.
• Integrate the size information into the training pipeline to improve the model's ability to distinguish between different bottle sizes.

Size-specific Augmentation:

• Apply data augmentation techniques specifically tailored to each bottle size category.
• Consider size-dependent variations such as label placement, cap design, or label size in the augmentation process.

Documentation Update:

• Include detailed documentation on the distribution of bottle sizes within the dataset.
• Specify the methodology used for size annotation and any considerations taken into account during the process.

Potential Use Cases:

• The trained model, with the ability to recognize both the type and size of liquor bottles, can be applied to tasks such as inventory management, logistics optimization, and market analysis.

Collaboration Opportunities:

• Explore collaborations with industry stakeholders to ensure the dataset reflects real-world scenarios and challenges.
• Seek feedback from professionals in the liquor industry to enhance the dataset's relevance and applicability.

This expansion of the dataset to include information about bottle sizes adds another layer of complexity and utility to the computer vision model, making it more versatile and suitable for a wider range of applications within the liquor industry.

Additional Details for Dataset Creation:

Image Capture Specifications:

• The dataset will consist of 400+ liquor bottles, each captured from a 360-degree perspective.
• Each bottle will be photographed at intervals of 15 degrees, resulting in a series of images covering the entire circumference of the bottle.

Dataset Structure:

• Each liquor bottle will have a dedicated folder containing its set of images captured at different angles.
• The folder structure will be organized to include subfolders for training, validation, and test sets.

Annotation Process:

• Annotate each image with bounding boxes or segmentation masks, indicating the boundaries of the liquor bottle.
• Include metadata specifying the angle of capture for each image.

Diversity Considerations:

• Ensure diversity in bottle types, brands, and labels within the dataset to make the model robust across various products.
• Include bottles of different shapes, sizes, and colors to capture real-world variability.

Data Augmentation:

• Apply data augmentation techniques to the images to further enhance model generalization.
• Augmentation methods may include rotation, flipping, changes in lighting, and simulated variations in bottle appearance.

Documentation Enhancements:

• Provide detailed documentation on the specific angles at which each bottle was captured.
• Include information on any challenges faced during image capture and annotation.

Model Training Adjustments:

• Consider the 360-degree nature of the images during model architecture selection.
• Modify the model training pipeline to handle the continuous rotation of bottle images.

Potential Use Cases:

• The resulting model can be applied to recognize liquor bottles from any orientation, contributing to applications like robotic inventory management or automated quality control.

Project Timeline:

• Define a timeline for dataset creation, annotation, model training, and evaluation phases.
• Allocate time for reviewing and addressing any challenges that may arise during the process.

This additional information ensures that the dataset is specifically tailored to the unique requirements of capturing liquor bottles in a 360-degree perspective, providing a more comprehensive and detailed resource for training the computer vision model.