Soda Can Dataset
Welcome to the Soda Can Dataset, a meticulously curated collection of 200 images featuring a wide variety of soda cans. This dataset is designed for the training of AI (LoRA) models focused on object recognition, branding analysis, and material reflection properties.
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Dataset Overview
- Content: This dataset comprises 200 high-quality images showcasing soda cans in various settings, angles, and lighting conditions. It includes cans that are full, empty, new, and used, providing a comprehensive range for AI training.
- Source: All images are carefully selected from this Unsplash collection: Soda Can Collection on Unsplash.
- Usage: Primarily aimed at training AI models for tasks such as object recognition, brand identification, and analyzing material properties under different environmental conditions.
Licensing
- The images within this dataset are licensed under the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International (CC BY-NC-ND 4.0) license. This license permits free non-commercial use, while also prohibiting the distribution of derivative works.
- For more detailed information about this license, please visit the CC BY-NC-ND 4.0 License details.
Transparency, Inspiration, and Use Statement
We host this dataset to promote transparency and serve as a source of inspiration, as well as to support research and private use within the realms of object recognition and materials science. We encourage users to explore the original collection on Unsplash for source attribution. Although the images originate from Unsplash, please be aware that this dataset is governed by a distinct licensing agreement that differs from the Unsplash License. Our goal in providing this dataset under a specific license is to respect the diverse applications in research and academic fields, ensuring usage is aligned with legal and ethical guidelines.
For a clear understanding of the original images' licensing terms, we recommend consulting the Unsplash License. This dataset's license is intended to augment, rather than replace, the original terms, offering avenues for non-commercial exploitation of these images for scholarly and research purposes.
We strive to honor the creativity and efforts of the photographers who share their work on Unsplash. By accessing this dataset, users agree to respect the spirit of creativity and the ethos of the Unsplash community, adhering to our license's specific terms. This approach ensures photographers' work is valued and used in ways that encourage ethical practice, innovation, and inspiration.
Dataset Composition
Each image in the dataset is accompanied by a caption generated using GPT-Vision. These captions are tailored for token shuffling during AI model training, enhancing the models' ability to recognize and classify soda cans accurately. The curated selection of images, paired with detailed captions, renders this dataset an invaluable asset for AI models focused on object recognition and analysis.
How to Use the Dataset
- Download the Dataset: Access and download the dataset through the provided link for your specific AI model training needs.
- Review Images and Captions: Engage with the dataset to grasp the extensive variety of soda cans and their presentation.
- Implement in AI Model Training: Apply the dataset in training your LoRA AI models, using the detailed captions to fine-tune object recognition and analysis capabilities.
Contributions and Feedback
We welcome your feedback and contributions. If you have suggestions or wish to contribute additional images to the dataset, please reach out to us. Your involvement aids in the continual enhancement of the dataset for the broader AI community.
Related
For insights into ethical approaches to AI model training and the use of art datasets, visit Crafting the Future: Blibla's Ethical Approach to AI Model Training.
This Soda Can Dataset stands as a pivotal tool for advancing AI capabilities in object recognition, brand analysis, and understanding material properties. We are excited to see the innovative applications and developments it will enable in your AI projects.
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