Fashion design, in most people’s minds, is a subjective area that has little room for a machine to learn. Many fashion executives regard AI as too mechanical because technically, machines are good at processing numerical or low dimensional tasks. However, with the exponential growth of deep learning neural network in image processing for the past years, AI has gradually penetrated the fashion design area to help designers identify market trends and reduce time to market.
When it comes to a fashion product like a jacket, we can decompose the jacket into multiple attributes, such as texture, shape, style, color, etc. All these attributes combined determines the success of the product, assuming the price and promotions are appropriate. Traditionally, a designer’s job is to conceive the optimal combinations of the attributes to build and candidate products list and then perform a market test to eventually determine the products to be bulk released to the market. This process is very expensive on both time and cost, and it could even end up with a negative value to the companies because designers couldn’t have a holistic view of the market trend, also the trend could have completely changed when the product is released. This explains why 70 percent of the fashion products are “loser” products in reality.
The good news is that the current deep learning technology can identify multiple attributes of fashion products by training through the largest dataset of product images from the current market. Based on the decomposed attributes, integrating with sales volume, rating and review sentiments, we can predict the key drives of success for different products.
Quantified Sales Plan
Once the key attributes are predicted, the designers can directly use the AI suggested product and immediately release to the market. In addition, AI will also assist in the sales plan to predict the volumes of products to be manufactured. For example, the trending color in a product recently is blue, the company should increase the manufacturing volume in blue comparing other colors.
To build a functional deep learning model that can really help the business, there are always three things need to be covered - data, model, and infrastructure. Data might be the largest challenge for most of the companies. To train the deep learning model with meaningful results, it might need millions of images, as well as other features of data for the training dataset from external data sources. Even a company can receive the complete set of data, it still needs massive work in data cleaning, preprocessing and feature engineering. Building a good deep learning model is like arts. Choosing the most appropriate model and setting the adequate parameters will not only largely improve the performance of the model but also reduce the time of training. Lastly, image processing is an expensive model that relies on machines with strong computational powers.
How we can help at Neural Trend
Neural Trend has fully equipped with data, model, and infrastructure to provide best-in-class solutions to support your fashion design process. While choosing Neural Trend, we can reduce 90 percent of the cost if you build on your own. Our deep learning model will provide a recommended design pattern based on real-time product universe as well as quantified sales plan to improve your sell-through and mitigate risks.