Expanding the dataset to include more complex garment types and stitching relationships is crucial for enhancing the versatility of the generated garments. Here are some strategies to achieve this:
Diverse Garment Types: Include a wider range of garment types such as evening gowns, tailored suits, sportswear, and traditional attire from various cultures. This diversity will enable the model to learn and generate a broader spectrum of designs.
Multi-Layered Garments: Incorporate garments with multiple layers like jackets with inner linings, hoodies with pockets, or dresses with intricate overlays. This will challenge the model to understand and generate complex stitching relationships.
Unconventional Designs: Introduce unconventional designs like asymmetrical dresses, garments with unique cutouts, or avant-garde silhouettes. This will push the model to think creatively and expand its design capabilities.
Detailed Stitching Information: Provide detailed stitching information for each garment type, including seam types, stitching styles, and embellishments. This will help the model create more realistic and intricate sewing patterns.
User-Generated Content: Allow users to contribute their garment designs to the dataset. This crowdsourcing approach can bring in a wide variety of styles and inspirations, enriching the dataset with unique and innovative designs.
By incorporating these elements into the dataset, the model will be exposed to a more diverse and comprehensive set of examples, enabling it to generate a wider range of complex and realistic garments with intricate stitching relationships.